face_generation_gan/dlnd_face_generation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Face Generation\n",
    "In this project, you'll use generative adversarial networks to generate new images of faces.\n",
    "### Get the Data\n",
    "You'll be using two datasets in this project:\n",
    "- MNIST\n",
    "- CelebA\n",
    "\n",
    "Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA.  Running the GANs on MNIST will allow you to see how well your model trains sooner.\n",
    "\n",
    "If you're using [FloydHub](https://www.floydhub.com/), set `data_dir` to \"/input\" and use the [FloydHub data ID](http://docs.floydhub.com/home/using_datasets/) \"R5KrjnANiKVhLWAkpXhNBe\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found mnist Data\n",
      "Found celeba Data\n"
     ]
    }
   ],
   "source": [
    "#data_dir = './data'\n",
    "data_dir = '/input'\n",
    "\n",
    "# FloydHub - Use with data ID \"R5KrjnANiKVhLWAkpXhNBe\"\n",
    "#data_dir = '/input'\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import helper\n",
    "\n",
    "helper.download_extract('mnist', data_dir)\n",
    "helper.download_extract('celeba', data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Data\n",
    "### MNIST\n",
    "As you're aware, the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset contains images of handwritten digits. You can view the first number of examples by changing `show_n_images`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e730a1ef0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noF4BCBgOQkQAQ6EQE+F
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e79a93278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "%matplotlib inline\n",
    "import os\n",
    "from glob import glob\n",
    "from matplotlib import pyplot\n",
    "\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "The [CelebFaces Attributes Dataset (CelebA)](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html) dataset contains over 200,000 celebrity images with annotations.  Since you're going to be generating faces, you won't need the annotations.  You can view the first number of examples by changing `show_n_images`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1e730176d8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQUAAAD8CAYAAAB+fLH0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGD/n/PRnWYZY0xyLxoV
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1e73044e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_n_images = 25\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')\n",
    "pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the Data\n",
    "Since the project's main focus is on building the GANs, we'll preprocess the data for you.  The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images.  The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.\n",
    "\n",
    "The MNIST images are black and white images with a single [color channel](https://en.wikipedia.org/wiki/Channel_(digital_image%29) while the CelebA images have [3 color channels (RGB color channel)](https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images).\n",
    "## Build the Neural Network\n",
    "You'll build the components necessary to build a GANs by implementing the following functions below:\n",
    "- `model_inputs`\n",
    "- `discriminator`\n",
    "- `generator`\n",
    "- `model_loss`\n",
    "- `model_opt`\n",
    "- `train`\n",
    "\n",
    "### Check the Version of TensorFlow and Access to GPU\n",
    "This will check to make sure you have the correct version of TensorFlow and access to a GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow Version: 1.2.1\n",
      "Default GPU Device: /gpu:0\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "from distutils.version import LooseVersion\n",
    "import warnings\n",
    "import tensorflow as tf\n",
    "\n",
    "# Check TensorFlow Version\n",
    "assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\n",
    "print('TensorFlow Version: {}'.format(tf.__version__))\n",
    "\n",
    "# Check for a GPU\n",
    "if not tf.test.gpu_device_name():\n",
    "    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\n",
    "else:\n",
    "    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Input\n",
    "Implement the `model_inputs` function to create TF Placeholders for the Neural Network. It should create the following placeholders:\n",
    "- Real input images placeholder with rank 4 using `image_width`, `image_height`, and `image_channels`.\n",
    "- Z input placeholder with rank 2 using `z_dim`.\n",
    "- Learning rate placeholder with rank 0.\n",
    "\n",
    "Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ERROR:tensorflow:==================================\n",
      "Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):\n",
      "<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>\n",
      "If you want to mark it as used call its \"mark_used()\" method.\n",
      "It was originally created here:\n",
      "['File \"/usr/local/lib/python3.5/runpy.py\", line 193, in _run_module_as_main\\n    \"__main__\", mod_spec)', 'File \"/usr/local/lib/python3.5/runpy.py\", line 85, in _run_code\\n    exec(code, run_globals)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel_launcher.py\", line 16, in <module>\\n    app.launch_new_instance()', 'File \"/usr/local/lib/python3.5/site-packages/traitlets/config/application.py\", line 658, in launch_instance\\n    app.start()', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelapp.py\", line 477, in start\\n    ioloop.IOLoop.instance().start()', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/ioloop.py\", line 177, in start\\n    super(ZMQIOLoop, self).start()', 'File \"/usr/local/lib/python3.5/site-packages/tornado/ioloop.py\", line 888, in start\\n    handler_func(fd_obj, events)', 'File \"/usr/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n    return fn(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 440, in _handle_events\\n    self._handle_recv()', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 472, in _handle_recv\\n    self._run_callback(callback, msg)', 'File \"/usr/local/lib/python3.5/site-packages/zmq/eventloop/zmqstream.py\", line 414, in _run_callback\\n    callback(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/tornado/stack_context.py\", line 277, in null_wrapper\\n    return fn(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 283, in dispatcher\\n    return self.dispatch_shell(stream, msg)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 235, in dispatch_shell\\n    handler(stream, idents, msg)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/kernelbase.py\", line 399, in execute_request\\n    user_expressions, allow_stdin)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/ipkernel.py\", line 196, in do_execute\\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File \"/usr/local/lib/python3.5/site-packages/ipykernel/zmqshell.py\", line 533, in run_cell\\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2698, in run_cell\\n    interactivity=interactivity, compiler=compiler, result=result)', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2808, in run_ast_nodes\\n    if self.run_code(code, result):', 'File \"/usr/local/lib/python3.5/site-packages/IPython/core/interactiveshell.py\", line 2862, in run_code\\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File \"<ipython-input-7-60e8f0e9d266>\", line 22, in <module>\\n    tests.test_model_inputs(model_inputs)', 'File \"/output/problem_unittests.py\", line 12, in func_wrapper\\n    result = func(*args)', 'File \"/output/problem_unittests.py\", line 68, in test_model_inputs\\n    _check_input(learn_rate, [], \\'Learning Rate\\')', 'File \"/output/problem_unittests.py\", line 34, in _check_input\\n    _assert_tensor_shape(tensor, shape, \\'Real Input\\')', 'File \"/output/problem_unittests.py\", line 20, in _assert_tensor_shape\\n    assert tf.assert_rank(tensor, len(shape), message=\\'{} has wrong rank\\'.format(display_name))', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 617, in assert_rank\\n    dynamic_condition, data, summarize)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/ops/check_ops.py\", line 571, in _assert_rank_condition\\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 170, in wrapped\\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File \"/usr/local/lib/python3.5/site-packages/tensorflow/python/util/tf_should_use.py\", line 139, in _add_should_use_warn
      "==================================\n",
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "import problem_unittests as tests\n",
    "\n",
    "def model_inputs(image_width, image_height, image_channels, z_dim):\n",
    "    \"\"\"\n",
    "    Create the model inputs\n",
    "    :param image_width: The input image width\n",
    "    :param image_height: The input image height\n",
    "    :param image_channels: The number of image channels\n",
    "    :param z_dim: The dimension of Z\n",
    "    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)\n",
    "    \"\"\"\n",
    "    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='inputs_real')\n",
    "    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='inputs_z')\n",
    "    learn_r = tf.placeholder(tf.float32, name='lr')\n",
    "\n",
    "    return inputs_real, inputs_z, learn_r\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_inputs(model_inputs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# leaky relu slope\n",
    "alpha = 0.2\n",
    "\n",
    "# print loss every\n",
    "print_every = 10\n",
    "\n",
    "# show image sample from G every\n",
    "show_every = 100\n",
    "\n",
    "# Smoothing \n",
    "smooth = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discriminator\n",
    "Implement `discriminator` to create a discriminator neural network that discriminates on `images`.  This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"discriminator\" to allow the variables to be reused.  The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def discriminator(images, reuse=False):\n",
    "    \"\"\"\n",
    "    Create the discriminator network\n",
    "    :param images: Tensor of input image(s)\n",
    "    :param reuse: Boolean if the weights should be reused\n",
    "    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)\n",
    "    \"\"\"\n",
    "    with tf.variable_scope('discriminator', reuse=reuse):\n",
    "        \n",
    "        # Weights Initializer\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "        \n",
    "        \n",
    "        ############# CONV BLOCK 1 ################\n",
    "        # conv2d -> relu\n",
    "        \n",
    "        # Input layer is 28x28x3 (no batch norm here)\n",
    "        conv1 = tf.layers.conv2d(images, 64, 5,\n",
    "                              strides=1,\n",
    "                              padding='valid',\n",
    "                              kernel_initializer=xavier_init)\n",
    "        relu1 = tf.maximum(alpha * conv1, conv1)\n",
    "        #print(relu1)\n",
    "        # 24x24x64\n",
    "        \n",
    "        ############# END CONV BLOCK 1 ############\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ############# CONV BLOCK 2 ################\n",
    "        # conv2d -> batch_norm -> drop -> relu\n",
    "        \n",
    "        conv2 = tf.layers.conv2d(relu1, 128, 5,\n",
    "                              strides=1,\n",
    "                              padding='valid',\n",
    "                              use_bias=False, \n",
    "                              kernel_initializer=xavier_init)\n",
    "        \n",
    "        bn2 = tf.layers.batch_normalization(conv2, training=True)\n",
    "        drop2 = tf.layers.dropout(bn2, training=True)\n",
    "        relu2 = tf.maximum(alpha * drop2, drop2)\n",
    "        #print(relu2)\n",
    "        # 20x20x128\n",
    "        \n",
    "        ############# END CONV BLOCK 2 ############\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        #############  CONV BLOCK 3 ##############\n",
    "        # conv2d -> batch_norm -> drop -> relu\n",
    "        \n",
    "        conv3 = tf.layers.conv2d(relu2, 256, 5,\n",
    "                                 strides=1,\n",
    "                                 padding='valid',\n",
    "                                 use_bias=False,\n",
    "                                 kernel_initializer=xavier_init)\n",
    "        \n",
    "        bn3 = tf.layers.batch_normalization(conv3, training=True)\n",
    "        drop3 = tf.layers.dropout(bn3, training=True)\n",
    "        relu3 = tf.maximum(alpha * drop3, drop3)\n",
    "        #print(relu3)\n",
    "        # 16x16x256\n",
    "        \n",
    "        #############  END CONV BLOCK 3 ##############\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "\n",
    "        \n",
    "        #############  CONV BLOCK 4 ##################\n",
    "        # conv2d -> batch_norm -> drop -> relu\n",
    "\n",
    "        \n",
    "        conv4 = tf.layers.conv2d(relu3, 512, 5,\n",
    "                              strides=2,\n",
    "                              padding='valid',\n",
    "                              use_bias=False,\n",
    "                              kernel_initializer=xavier_init)\n",
    "        \n",
    "        bn4 = tf.layers.batch_normalization(conv4, training=True)\n",
    "        drop4 = tf.layers.dropout(bn4, training=True)        \n",
    "        relu4 = tf.maximum(alpha * drop4, drop4)\n",
    "        #print(relu4)\n",
    "        # 6x6x512\n",
    "        \n",
    "        #############  END CONV BLOCK 4 ##############\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ############# Out Layer ##############\n",
    "\n",
    "        # Flatten it\n",
    "        flat = tf.reshape(relu4, (-1, 6*6*512))\n",
    "        logits = tf.layers.dense(flat, 1, kernel_initializer=xavier_init)\n",
    "        #print(flat)\n",
    "        out = tf.sigmoid(logits)\n",
    "        \n",
    "        \n",
    "        ############# End Out Layer ##############\n",
    "       \n",
    "        \n",
    "\n",
    "    return out, logits\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_discriminator(discriminator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generator\n",
    "Implement `generator` to generate an image using `z`. This function should be able to reuse the variables in the neural network.  Use [`tf.variable_scope`](https://www.tensorflow.org/api_docs/python/tf/variable_scope) with a scope name of \"generator\" to allow the variables to be reused. The function should return the generated 28 x 28 x `out_channel_dim` images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def generator(z, out_channel_dim, is_train=True):\n",
    "    \"\"\"\n",
    "    Create the generator network\n",
    "    :param z: Input z\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param is_train: Boolean if generator is being used for training\n",
    "    :return: The tensor output of the generator\n",
    "    \"\"\"\n",
    " \n",
    "    with tf.variable_scope('generator', reuse=not is_train):\n",
    "      \n",
    "    \n",
    "        # Weights Initializer\n",
    "        xavier_init = tf.contrib.layers.xavier_initializer()\n",
    "    \n",
    "        ############# Fully Connected  ################\n",
    "\n",
    "        fc = tf.layers.dense(z, 7*7*1024, kernel_initializer=xavier_init)\n",
    "        fc = tf.reshape(fc, (-1, 7, 7, 1024))\n",
    "        drop = tf.layers.dropout(fc, training=is_train)\n",
    "        relu = tf.maximum(alpha * drop, drop)\n",
    "        #print(fc)\n",
    "        # 7x7x1024 now\n",
    "        \n",
    "        ############# END Fully connected #############\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ############# DCONV BLOCK 1 ###################\n",
    "        # conv2d_transpose -> batch_norm -> drop -> relu\n",
    "        \n",
    "        dconv1 = tf.layers.conv2d_transpose(relu, 512, 3,\n",
    "                                            strides=2,\n",
    "                                            padding='same',\n",
    "                                            use_bias=False,\n",
    "                                            kernel_initializer=xavier_init)\n",
    "        \n",
    "        bn1 = tf.layers.batch_normalization(dconv1, training=is_train)\n",
    "        drop1 = tf.layers.dropout(bn1, training=is_train)\n",
    "        relu1 = tf.maximum(alpha * drop1, drop1)\n",
    "        #print(dconv1)\n",
    "        # 14x14x512 now\n",
    "        \n",
    "        ############# END DCONV BLOCK 1 ################\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "         \n",
    "            \n",
    "        ############# DCONV BLOCK 2 ###################\n",
    "        # conv2d_transpose -> batch_norm -> drop -> relu    \n",
    "            \n",
    "        \n",
    "        dconv2 = tf.layers.conv2d_transpose(relu1, 256, 3,\n",
    "                                            strides=2,\n",
    "                                            padding='same',\n",
    "                                            use_bias=False,\n",
    "                                            kernel_initializer=xavier_init)\n",
    "        \n",
    "        bn2 = tf.layers.batch_normalization(dconv2, training=is_train)\n",
    "        drop2 = tf.layers.dropout(bn2, training=is_train)\n",
    "        relu2 = tf.maximum(alpha * drop2, drop2)\n",
    "        #print(dconv2)\n",
    "        # 28x28x256 now\n",
    "        \n",
    "        ############# END DCONV BLOCK 2 ###################\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ############# DCONV BLOCK 3 ###################\n",
    "        # conv2d_transpose -> batch_norm -> drop -> relu\n",
    "        \n",
    "        dconv3 = tf.layers.conv2d_transpose(relu2, 128, 5,\n",
    "                                            strides=1,\n",
    "                                            padding='same',\n",
    "                                            use_bias=False,\n",
    "                                            kernel_initializer=xavier_init)\n",
    "        \n",
    "        bn3 = tf.layers.batch_normalization(dconv3, training=is_train)\n",
    "        drop3 = tf.layers.dropout(bn3,training=is_train)\n",
    "        relu3 = tf.maximum(alpha * drop3, drop3)\n",
    "        #print(dconv3)\n",
    "        # 28x28x128 now\n",
    "        \n",
    "        ############# END DCONV BLOCK 3 ###################\n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        \n",
    "        ############# Out Layer ##############\n",
    "        \n",
    "        logits = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5,\n",
    "                                            strides=1,\n",
    "                                            padding='same',\n",
    "                                            kernel_initializer=xavier_init)\n",
    "        \n",
    "        #print(logits)\n",
    "        # 28x28x(channels) now\n",
    "        \n",
    "        out = tf.tanh(logits)\n",
    "        \n",
    "        ############# Out Layer ##############\n",
    "    \n",
    "    return out\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_generator(generator, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loss\n",
    "Implement `model_loss` to build the GANs for training and calculate the loss.  The function should return a tuple of (discriminator loss, generator loss).  Use the following functions you implemented:\n",
    "- `discriminator(images, reuse=False)`\n",
    "- `generator(z, out_channel_dim, is_train=True)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_loss(input_real, input_z, out_channel_dim):\n",
    "    \"\"\"\n",
    "    Get the loss for the discriminator and generator\n",
    "    :param input_real: Images from the real dataset\n",
    "    :param input_z: Z input\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :return: A tuple of (discriminator loss, generator loss)\n",
    "    \"\"\"\n",
    "    g_model = generator(input_z, out_channel_dim, True)\n",
    "    d_model_real, d_logits_real = discriminator(input_real)\n",
    "    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)\n",
    "\n",
    "    d_loss_real = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real,\n",
    "                                                labels=tf.ones_like(d_model_real) * (1 - smooth)))\n",
    "    \n",
    "    d_loss_fake = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))\n",
    "    \n",
    "    \n",
    "    g_loss = tf.reduce_mean(\n",
    "        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))\n",
    "\n",
    "    d_loss = d_loss_real + d_loss_fake\n",
    "    \n",
    "    return d_loss, g_loss\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_loss(model_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimization\n",
    "Implement `model_opt` to create the optimization operations for the GANs. Use [`tf.trainable_variables`](https://www.tensorflow.org/api_docs/python/tf/trainable_variables) to get all the trainable variables.  Filter the variables with names that are in the discriminator and generator scope names.  The function should return a tuple of (discriminator training operation, generator training operation)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tests Passed\n"
     ]
    }
   ],
   "source": [
    "def model_opt(d_loss, g_loss, learning_rate, beta1):\n",
    "    \"\"\"\n",
    "    Get optimization operations\n",
    "    :param d_loss: Discriminator loss Tensor\n",
    "    :param g_loss: Generator loss Tensor\n",
    "    :param learning_rate: Learning Rate Placeholder\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :return: A tuple of (discriminator training operation, generator training operation)\n",
    "    \"\"\"\n",
    "    \n",
    "    # Get weights and bias to update\n",
    "    t_vars = tf.trainable_variables()\n",
    "    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]\n",
    "    g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "\n",
    "    # Optimize\n",
    "    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
    "        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)\n",
    "        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)\n",
    "\n",
    "    \n",
    "    return d_train_opt, g_train_opt\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "tests.test_model_opt(model_opt, tf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Neural Network Training\n",
    "### Show Output\n",
    "Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL\n",
    "\"\"\"\n",
    "import numpy as np\n",
    "\n",
    "def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):\n",
    "    \"\"\"\n",
    "    Show example output for the generator\n",
    "    :param sess: TensorFlow session\n",
    "    :param n_images: Number of Images to display\n",
    "    :param input_z: Input Z Tensor\n",
    "    :param out_channel_dim: The number of channels in the output image\n",
    "    :param image_mode: The mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    "    cmap = None if image_mode == 'RGB' else 'gray'\n",
    "    z_dim = input_z.get_shape().as_list()[-1]\n",
    "    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])\n",
    "\n",
    "    samples = sess.run(\n",
    "        generator(input_z, out_channel_dim, False),\n",
    "        feed_dict={input_z: example_z})\n",
    "\n",
    "    images_grid = helper.images_square_grid(samples, image_mode)\n",
    "    pyplot.imshow(images_grid, cmap=cmap)\n",
    "    pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train\n",
    "Implement `train` to build and train the GANs.  Use the following functions you implemented:\n",
    "- `model_inputs(image_width, image_height, image_channels, z_dim)`\n",
    "- `model_loss(input_real, input_z, out_channel_dim)`\n",
    "- `model_opt(d_loss, g_loss, learning_rate, beta1)`\n",
    "\n",
    "Use the `show_generator_output` to show `generator` output while you train. Running `show_generator_output` for every batch will drastically increase training time and increase the size of the notebook.  It's recommended to print the `generator` output every 100 batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.debugger import Tracer\n",
    "\n",
    "def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):\n",
    "    \"\"\"\n",
    "    Train the GAN\n",
    "    :param epoch_count: Number of epochs\n",
    "    :param batch_size: Batch Size\n",
    "    :param z_dim: Z dimension\n",
    "    :param learning_rate: Learning Rate\n",
    "    :param beta1: The exponential decay rate for the 1st moment in the optimizer\n",
    "    :param get_batches: Function to get batches\n",
    "    :param data_shape: Shape of the data\n",
    "    :param data_image_mode: The image mode to use for images (\"RGB\" or \"L\")\n",
    "    \"\"\"\n",
    "    \n",
    "    # create input placeholders\n",
    "    #print(data_shape)\n",
    "    image_shape = data_shape[1:]\n",
    "    #print(image_shape)\n",
    "    out_channel_dim = image_shape[-1]\n",
    "    #print(out_channel_dim)\n",
    "    \n",
    "    inputs_real, inputs_z, lr_rate = model_inputs(*image_shape, z_dim)\n",
    "    #print(inputs_real)\n",
    "    d_loss, g_loss = model_loss(inputs_real, inputs_z, out_channel_dim)\n",
    "    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, lr_rate, beta1)\n",
    "    #print(inputs_real)\n",
    "    \n",
    "    #t_vars = tf.trainable_variables()\n",
    "    #g_vars = [var for var in t_vars if var.name.startswith('generator')]\n",
    "    #saver = tf.train.Saver(var_list=g_vars)\n",
    "    \n",
    "    def scale(x, target_range=(-1,1)):\n",
    "        min, max = target_range\n",
    "        \n",
    "        # One liner, but too expensive \n",
    "        #x = (x - x.min()) * (max - min) / (x.max() - x.min()) + min\n",
    "        \n",
    "        # First scale to [0,1]\n",
    "        x = ((x - x.min())/(x.max() - x.min()))\n",
    "    \n",
    "        # Then scale to target_range\n",
    "        min, max = target_range\n",
    "        x = x * (max - min) + min\n",
    "        return x\n",
    "    \n",
    "    \n",
    "    steps = 0\n",
    "    with tf.Session() as sess:\n",
    "        sess.run(tf.global_variables_initializer())\n",
    "        for epoch_i in range(epoch_count):\n",
    "            for batch_images in get_batches(batch_size):\n",
    "                steps += 1\n",
    "                #print(\"Step {} ...\".format(steps))\n",
    "                \n",
    "                #1 Scale real images to [-1,1] range since that is also the generator output scale\n",
    "                batch_images = scale(batch_images, target_range=(-1,1))\n",
    "                \n",
    "                \n",
    "                #3 Sample random noise for generator\n",
    "                batch_z = np.random.uniform(-1,1, size=(batch_size, z_dim))\n",
    "                \n",
    "                \n",
    "                #3 Run optimizers\n",
    "                #print(batch_images.shape)\n",
    "                            \n",
    "                \n",
    "                # Optimize discriminator\n",
    "                _ = sess.run(d_train_opt, feed_dict={inputs_real: batch_images,\n",
    "                                                     inputs_z: batch_z,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                \n",
    "                \n",
    "                \n",
    "                \n",
    "                # Optimize generator (Run twice to avoid faster convergence of D)\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n",
    "                                                     inputs_real: batch_images,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                _ = sess.run(g_train_opt, feed_dict={inputs_z: batch_z,\n",
    "                                                     inputs_real: batch_images,\n",
    "                                                     lr_rate: learning_rate})\n",
    "                \n",
    "                \n",
    "                #Tracer()() #this one triggers the debugger\n",
    "                if steps % print_every == 0:\n",
    "                    \n",
    "                    d_train_loss = d_loss.eval({inputs_real: batch_images,\n",
    "                                                inputs_z: batch_z,\n",
    "                                                lr_rate: learning_rate})\n",
    "                \n",
    "                    # Optimize generator\n",
    "                    g_train_loss = g_loss.eval({inputs_z: batch_z,\n",
    "                                                inputs_real: batch_images,\n",
    "                                                lr_rate: learning_rate})\n",
    "                    \n",
    "                    #Tracer()() #this one triggers the debugger\n",
    "                    print(\"Epoch {}/{}-Step {}...\".format(epoch_i+1, epoch_count,steps),\n",
    "                          \"Discriminator Loss: {:.4f}...\".format(d_train_loss),\n",
    "                          \"Generator Loss: {:.4f}\".format(g_train_loss))\n",
    "                    \n",
    "                if steps % show_every == 0:\n",
    "                        show_generator_output(sess, 9, inputs_z, out_channel_dim, data_image_mode)\n",
    "                \n",
    "                \n",
    "                \n",
    "                "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST\n",
    "Test your GANs architecture on MNIST.  After 2 epochs, the GANs should be able to generate images that look like handwritten digits.  Make sure the loss of the generator is lower than the loss of the discriminator or close to 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 10... Discriminator Loss: 2.3118... Generator Loss: 1.8300\n",
      "Epoch 1/2-Step 20... Discriminator Loss: 3.3062... Generator Loss: 0.4146\n",
      "Epoch 1/2-Step 30... Discriminator Loss: 5.4623... Generator Loss: 0.0596\n",
      "Epoch 1/2-Step 40... Discriminator Loss: 3.8122... Generator Loss: 2.1554\n",
      "Epoch 1/2-Step 50... Discriminator Loss: 2.1463... Generator Loss: 1.6847\n",
      "Epoch 1/2-Step 60... Discriminator Loss: 1.8216... Generator Loss: 1.3277\n",
      "Epoch 1/2-Step 70... Discriminator Loss: 3.0009... Generator Loss: 0.2086\n",
      "Epoch 1/2-Step 80... Discriminator Loss: 2.1514... Generator Loss: 0.5999\n",
      "Epoch 1/2-Step 90... Discriminator Loss: 2.6132... Generator Loss: 0.6457\n",
      "Epoch 1/2-Step 100... Discriminator Loss: 1.3977... Generator Loss: 0.9171\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dd49f58d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 110... Discriminator Loss: 1.7667... Generator Loss: 1.2358\n",
      "Epoch 1/2-Step 120... Discriminator Loss: 2.2311... Generator Loss: 1.1288\n",
      "Epoch 1/2-Step 130... Discriminator Loss: 1.6081... Generator Loss: 1.1581\n",
      "Epoch 1/2-Step 140... Discriminator Loss: 2.1011... Generator Loss: 0.8867\n",
      "Epoch 1/2-Step 150... Discriminator Loss: 2.1195... Generator Loss: 0.4963\n",
      "Epoch 1/2-Step 160... Discriminator Loss: 1.9894... Generator Loss: 0.6073\n",
      "Epoch 1/2-Step 170... Discriminator Loss: 2.5766... Generator Loss: 0.3160\n",
      "Epoch 1/2-Step 180... Discriminator Loss: 2.6919... Generator Loss: 0.3946\n",
      "Epoch 1/2-Step 190... Discriminator Loss: 2.1254... Generator Loss: 0.9327\n",
      "Epoch 1/2-Step 200... Discriminator Loss: 3.2503... Generator Loss: 0.2115\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrSDTvg4if/jhh4NMpgL
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dc4537898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 210... Discriminator Loss: 2.3345... Generator Loss: 1.4846\n",
      "Epoch 1/2-Step 220... Discriminator Loss: 2.3870... Generator Loss: 0.3692\n",
      "Epoch 1/2-Step 230... Discriminator Loss: 2.6547... Generator Loss: 0.7615\n",
      "Epoch 1/2-Step 240... Discriminator Loss: 1.9027... Generator Loss: 1.3755\n",
      "Epoch 1/2-Step 250... Discriminator Loss: 2.2588... Generator Loss: 1.6757\n",
      "Epoch 1/2-Step 260... Discriminator Loss: 1.8497... Generator Loss: 0.4162\n",
      "Epoch 1/2-Step 270... Discriminator Loss: 2.2802... Generator Loss: 0.8063\n",
      "Epoch 1/2-Step 280... Discriminator Loss: 2.4021... Generator Loss: 1.9779\n",
      "Epoch 1/2-Step 290... Discriminator Loss: 2.1448... Generator Loss: 0.5301\n",
      "Epoch 1/2-Step 300... Discriminator Loss: 2.8183... Generator Loss: 2.2886\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ntARS0k2oKvI0z1NOOSX
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dd48a7780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 310... Discriminator Loss: 2.2783... Generator Loss: 0.7090\n",
      "Epoch 1/2-Step 320... Discriminator Loss: 2.3461... Generator Loss: 1.9455\n",
      "Epoch 1/2-Step 330... Discriminator Loss: 1.7101... Generator Loss: 0.7023\n",
      "Epoch 1/2-Step 340... Discriminator Loss: 1.8282... Generator Loss: 0.4235\n",
      "Epoch 1/2-Step 350... Discriminator Loss: 2.4861... Generator Loss: 0.4528\n",
      "Epoch 1/2-Step 360... Discriminator Loss: 2.2659... Generator Loss: 0.7544\n",
      "Epoch 1/2-Step 370... Discriminator Loss: 2.0567... Generator Loss: 0.5790\n",
      "Epoch 1/2-Step 380... Discriminator Loss: 1.7823... Generator Loss: 0.9565\n",
      "Epoch 1/2-Step 390... Discriminator Loss: 1.9036... Generator Loss: 0.7933\n",
      "Epoch 1/2-Step 400... Discriminator Loss: 2.1564... Generator Loss: 0.4115\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrOK2ies6XcylAKvnl6C
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dd4d7d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 410... Discriminator Loss: 1.9079... Generator Loss: 0.7702\n",
      "Epoch 1/2-Step 420... Discriminator Loss: 1.7968... Generator Loss: 0.7860\n",
      "Epoch 1/2-Step 430... Discriminator Loss: 2.0565... Generator Loss: 0.5020\n",
      "Epoch 1/2-Step 440... Discriminator Loss: 1.5293... Generator Loss: 1.2694\n",
      "Epoch 1/2-Step 450... Discriminator Loss: 2.2105... Generator Loss: 0.1410\n",
      "Epoch 1/2-Step 460... Discriminator Loss: 1.3486... Generator Loss: 1.1598\n",
      "Epoch 1/2-Step 470... Discriminator Loss: 2.2251... Generator Loss: 1.4939\n",
      "Epoch 1/2-Step 480... Discriminator Loss: 1.7503... Generator Loss: 0.6644\n",
      "Epoch 1/2-Step 490... Discriminator Loss: 3.0195... Generator Loss: 0.3693\n",
      "Epoch 1/2-Step 500... Discriminator Loss: 2.2151... Generator Loss: 0.9348\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/Nc80+rZS7eZbZNNNkm
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dc43f9b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 510... Discriminator Loss: 3.9171... Generator Loss: 0.0883\n",
      "Epoch 1/2-Step 520... Discriminator Loss: 1.4402... Generator Loss: 1.4085\n",
      "Epoch 1/2-Step 530... Discriminator Loss: 2.2724... Generator Loss: 0.7950\n",
      "Epoch 1/2-Step 540... Discriminator Loss: 1.9018... Generator Loss: 0.8728\n",
      "Epoch 1/2-Step 550... Discriminator Loss: 1.5139... Generator Loss: 0.9002\n",
      "Epoch 1/2-Step 560... Discriminator Loss: 1.3713... Generator Loss: 0.9616\n",
      "Epoch 1/2-Step 570... Discriminator Loss: 1.7664... Generator Loss: 1.1300\n",
      "Epoch 1/2-Step 580... Discriminator Loss: 1.6943... Generator Loss: 1.7043\n",
      "Epoch 1/2-Step 590... Discriminator Loss: 2.2566... Generator Loss: 0.3415\n",
      "Epoch 1/2-Step 600... Discriminator Loss: 1.6204... Generator Loss: 0.8635\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd0d2b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 610... Discriminator Loss: 2.0628... Generator Loss: 0.6711\n",
      "Epoch 1/2-Step 620... Discriminator Loss: 1.3906... Generator Loss: 1.0603\n",
      "Epoch 1/2-Step 630... Discriminator Loss: 1.4823... Generator Loss: 0.6392\n",
      "Epoch 1/2-Step 640... Discriminator Loss: 1.7678... Generator Loss: 0.7574\n",
      "Epoch 1/2-Step 650... Discriminator Loss: 1.7096... Generator Loss: 1.1681\n",
      "Epoch 1/2-Step 660... Discriminator Loss: 1.7436... Generator Loss: 0.9023\n",
      "Epoch 1/2-Step 670... Discriminator Loss: 2.0634... Generator Loss: 0.6600\n",
      "Epoch 1/2-Step 680... Discriminator Loss: 1.8377... Generator Loss: 2.0370\n",
      "Epoch 1/2-Step 690... Discriminator Loss: 1.6467... Generator Loss: 0.7104\n",
      "Epoch 1/2-Step 700... Discriminator Loss: 1.5969... Generator Loss: 0.5801\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nG2TrrrsukD/hKQ5xl26
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dc462d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 710... Discriminator Loss: 2.0489... Generator Loss: 0.4872\n",
      "Epoch 1/2-Step 720... Discriminator Loss: 2.2425... Generator Loss: 0.4303\n",
      "Epoch 1/2-Step 730... Discriminator Loss: 2.0173... Generator Loss: 0.8741\n",
      "Epoch 1/2-Step 740... Discriminator Loss: 2.1449... Generator Loss: 0.5130\n",
      "Epoch 1/2-Step 750... Discriminator Loss: 1.5261... Generator Loss: 0.8271\n",
      "Epoch 1/2-Step 760... Discriminator Loss: 1.8701... Generator Loss: 0.3742\n",
      "Epoch 1/2-Step 770... Discriminator Loss: 1.7754... Generator Loss: 0.6628\n",
      "Epoch 1/2-Step 780... Discriminator Loss: 1.7760... Generator Loss: 0.5923\n",
      "Epoch 1/2-Step 790... Discriminator Loss: 1.8626... Generator Loss: 0.6293\n",
      "Epoch 1/2-Step 800... Discriminator Loss: 1.4426... Generator Loss: 1.4333\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbce674a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 810... Discriminator Loss: 2.2480... Generator Loss: 0.2999\n",
      "Epoch 1/2-Step 820... Discriminator Loss: 2.1555... Generator Loss: 1.3444\n",
      "Epoch 1/2-Step 830... Discriminator Loss: 1.9879... Generator Loss: 0.9417\n",
      "Epoch 1/2-Step 840... Discriminator Loss: 2.4632... Generator Loss: 0.3972\n",
      "Epoch 1/2-Step 850... Discriminator Loss: 1.4709... Generator Loss: 0.5511\n",
      "Epoch 1/2-Step 860... Discriminator Loss: 2.2099... Generator Loss: 2.0288\n",
      "Epoch 1/2-Step 870... Discriminator Loss: 1.8572... Generator Loss: 1.4878\n",
      "Epoch 1/2-Step 880... Discriminator Loss: 1.9986... Generator Loss: 1.5338\n",
      "Epoch 1/2-Step 890... Discriminator Loss: 1.7793... Generator Loss: 0.7089\n",
      "Epoch 1/2-Step 900... Discriminator Loss: 1.0658... Generator Loss: 1.5902\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxkUXXQSkh1RKv3VGGj1
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbce675c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 910... Discriminator Loss: 2.0082... Generator Loss: 0.6012\n",
      "Epoch 1/2-Step 920... Discriminator Loss: 1.6025... Generator Loss: 0.5098\n",
      "Epoch 1/2-Step 930... Discriminator Loss: 2.4625... Generator Loss: 0.2596\n",
      "Epoch 1/2-Step 940... Discriminator Loss: 1.4311... Generator Loss: 1.0545\n",
      "Epoch 1/2-Step 950... Discriminator Loss: 1.7271... Generator Loss: 0.7301\n",
      "Epoch 1/2-Step 960... Discriminator Loss: 2.2494... Generator Loss: 1.4716\n",
      "Epoch 1/2-Step 970... Discriminator Loss: 1.6490... Generator Loss: 0.9634\n",
      "Epoch 1/2-Step 980... Discriminator Loss: 1.8123... Generator Loss: 2.0872\n",
      "Epoch 1/2-Step 990... Discriminator Loss: 1.8791... Generator Loss: 1.1141\n",
      "Epoch 1/2-Step 1000... Discriminator Loss: 2.2623... Generator Loss: 0.6271\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nj3POrfTet1Gvf+K9X+d
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd336390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1010... Discriminator Loss: 1.5344... Generator Loss: 0.4333\n",
      "Epoch 1/2-Step 1020... Discriminator Loss: 1.3361... Generator Loss: 1.1219\n",
      "Epoch 1/2-Step 1030... Discriminator Loss: 2.0035... Generator Loss: 1.5471\n",
      "Epoch 1/2-Step 1040... Discriminator Loss: 1.5246... Generator Loss: 0.7886\n",
      "Epoch 1/2-Step 1050... Discriminator Loss: 2.2192... Generator Loss: 0.6574\n",
      "Epoch 1/2-Step 1060... Discriminator Loss: 1.5182... Generator Loss: 1.3824\n",
      "Epoch 1/2-Step 1070... Discriminator Loss: 2.7937... Generator Loss: 0.3068\n",
      "Epoch 1/2-Step 1080... Discriminator Loss: 1.4752... Generator Loss: 0.8406\n",
      "Epoch 1/2-Step 1090... Discriminator Loss: 1.8927... Generator Loss: 0.6407\n",
      "Epoch 1/2-Step 1100... Discriminator Loss: 1.7118... Generator Loss: 0.6947\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbWy4TlOSvNHsM/Hzn/8
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcd10eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1110... Discriminator Loss: 1.8363... Generator Loss: 0.8679\n",
      "Epoch 1/2-Step 1120... Discriminator Loss: 2.0464... Generator Loss: 0.4784\n",
      "Epoch 1/2-Step 1130... Discriminator Loss: 1.6536... Generator Loss: 1.8249\n",
      "Epoch 1/2-Step 1140... Discriminator Loss: 1.7578... Generator Loss: 0.5718\n",
      "Epoch 1/2-Step 1150... Discriminator Loss: 2.8404... Generator Loss: 0.2328\n",
      "Epoch 1/2-Step 1160... Discriminator Loss: 1.2185... Generator Loss: 1.2534\n",
      "Epoch 1/2-Step 1170... Discriminator Loss: 1.5187... Generator Loss: 0.8082\n",
      "Epoch 1/2-Step 1180... Discriminator Loss: 1.8224... Generator Loss: 0.7550\n",
      "Epoch 1/2-Step 1190... Discriminator Loss: 1.5622... Generator Loss: 1.1450\n",
      "Epoch 1/2-Step 1200... Discriminator Loss: 1.2265... Generator Loss: 1.1609\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbk+dL/ea2ohvGBmkKUd
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcbc7780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1210... Discriminator Loss: 1.4962... Generator Loss: 1.2880\n",
      "Epoch 1/2-Step 1220... Discriminator Loss: 1.7215... Generator Loss: 0.9768\n",
      "Epoch 1/2-Step 1230... Discriminator Loss: 1.8570... Generator Loss: 0.5593\n",
      "Epoch 1/2-Step 1240... Discriminator Loss: 1.4478... Generator Loss: 1.3494\n",
      "Epoch 1/2-Step 1250... Discriminator Loss: 2.1037... Generator Loss: 0.5527\n",
      "Epoch 1/2-Step 1260... Discriminator Loss: 3.1092... Generator Loss: 0.1941\n",
      "Epoch 1/2-Step 1270... Discriminator Loss: 1.6438... Generator Loss: 0.5245\n",
      "Epoch 1/2-Step 1280... Discriminator Loss: 1.4956... Generator Loss: 0.9718\n",
      "Epoch 1/2-Step 1290... Discriminator Loss: 2.0359... Generator Loss: 0.4613\n",
      "Epoch 1/2-Step 1300... Discriminator Loss: 1.2851... Generator Loss: 2.5540\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1kfZjz/+OMpsopeGR9s
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dc43f98d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1310... Discriminator Loss: 1.9794... Generator Loss: 1.2672\n",
      "Epoch 1/2-Step 1320... Discriminator Loss: 1.5431... Generator Loss: 0.8462\n",
      "Epoch 1/2-Step 1330... Discriminator Loss: 2.6111... Generator Loss: 2.0525\n",
      "Epoch 1/2-Step 1340... Discriminator Loss: 1.8478... Generator Loss: 0.6593\n",
      "Epoch 1/2-Step 1350... Discriminator Loss: 1.5591... Generator Loss: 2.2388\n",
      "Epoch 1/2-Step 1360... Discriminator Loss: 2.0985... Generator Loss: 0.4223\n",
      "Epoch 1/2-Step 1370... Discriminator Loss: 1.3647... Generator Loss: 0.6367\n",
      "Epoch 1/2-Step 1380... Discriminator Loss: 1.2238... Generator Loss: 1.0516\n",
      "Epoch 1/2-Step 1390... Discriminator Loss: 1.5150... Generator Loss: 1.1102\n",
      "Epoch 1/2-Step 1400... Discriminator Loss: 1.0320... Generator Loss: 1.1787\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKzbUKDZWWmtiaQLRo0e
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc864208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1410... Discriminator Loss: 2.0789... Generator Loss: 0.8383\n",
      "Epoch 1/2-Step 1420... Discriminator Loss: 1.5342... Generator Loss: 0.8889\n",
      "Epoch 1/2-Step 1430... Discriminator Loss: 2.1573... Generator Loss: 1.1052\n",
      "Epoch 1/2-Step 1440... Discriminator Loss: 1.5579... Generator Loss: 0.6092\n",
      "Epoch 1/2-Step 1450... Discriminator Loss: 2.5980... Generator Loss: 0.3473\n",
      "Epoch 1/2-Step 1460... Discriminator Loss: 0.9412... Generator Loss: 1.3549\n",
      "Epoch 1/2-Step 1470... Discriminator Loss: 1.5168... Generator Loss: 0.7796\n",
      "Epoch 1/2-Step 1480... Discriminator Loss: 1.3101... Generator Loss: 1.2143\n",
      "Epoch 1/2-Step 1490... Discriminator Loss: 1.7092... Generator Loss: 0.5636\n",
      "Epoch 1/2-Step 1500... Discriminator Loss: 1.7458... Generator Loss: 0.9821\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7NhWnwC7T9+/f38gWXe
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc8041d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1510... Discriminator Loss: 2.2684... Generator Loss: 0.5608\n",
      "Epoch 1/2-Step 1520... Discriminator Loss: 1.9566... Generator Loss: 1.3383\n",
      "Epoch 1/2-Step 1530... Discriminator Loss: 1.3982... Generator Loss: 1.3955\n",
      "Epoch 1/2-Step 1540... Discriminator Loss: 1.6526... Generator Loss: 2.5369\n",
      "Epoch 1/2-Step 1550... Discriminator Loss: 0.7793... Generator Loss: 1.4792\n",
      "Epoch 1/2-Step 1560... Discriminator Loss: 2.7674... Generator Loss: 0.6169\n",
      "Epoch 1/2-Step 1570... Discriminator Loss: 2.0942... Generator Loss: 0.9290\n",
      "Epoch 1/2-Step 1580... Discriminator Loss: 2.5194... Generator Loss: 0.2785\n",
      "Epoch 1/2-Step 1590... Discriminator Loss: 0.9844... Generator Loss: 0.9368\n",
      "Epoch 1/2-Step 1600... Discriminator Loss: 1.6432... Generator Loss: 1.0269\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc809f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1610... Discriminator Loss: 1.9181... Generator Loss: 0.5161\n",
      "Epoch 1/2-Step 1620... Discriminator Loss: 1.5031... Generator Loss: 2.0783\n",
      "Epoch 1/2-Step 1630... Discriminator Loss: 0.6584... Generator Loss: 1.7617\n",
      "Epoch 1/2-Step 1640... Discriminator Loss: 1.7910... Generator Loss: 1.2240\n",
      "Epoch 1/2-Step 1650... Discriminator Loss: 1.6499... Generator Loss: 0.6987\n",
      "Epoch 1/2-Step 1660... Discriminator Loss: 1.2733... Generator Loss: 0.7293\n",
      "Epoch 1/2-Step 1670... Discriminator Loss: 1.4346... Generator Loss: 1.5883\n",
      "Epoch 1/2-Step 1680... Discriminator Loss: 1.0651... Generator Loss: 0.8878\n",
      "Epoch 1/2-Step 1690... Discriminator Loss: 2.7191... Generator Loss: 0.4455\n",
      "Epoch 1/2-Step 1700... Discriminator Loss: 1.4566... Generator Loss: 0.8971\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXe0VNXVwH8bsKKIaEQEEREsqBGsoCbLhj1ibLEuNXaj\ngiaxsaIpGmOJGo0mYtfYiBpjSCyAEsUoAcXYELEgghRNMJpo8kU93x8z+8y+vPvezLyZuW/mzf6t\nxeK8PW/uPbecd/bZZxcJIeA4TnPRpaM74DhO9vjAd5wmxAe+4zQhPvAdpwnxge84TYgPfMdpQnzg\nO04TUtHAF5E9RWS2iLwpIudWq1OO49QWaa8Dj4h0Bd4ARgLzgenAYSGE16rXPcdxakG3Cr67LfBm\nCOFtABG5FxgFtDrwRSSICADuMZiO358CXboUFNKuXbsC8Pnnn0eZ36N0QghS7HcqGfh9gffMz/OB\n7dr6goiw/PLLA/Df//63glN3XlZYYQUA/vOf/3RwTzoOHeQrr7xylPXo0QOAJUuWRNn//ve/bDtW\nx+gfyS+//LKk369k4JeEiJwInFjr8ziOUzqVDPwFwLrm5355WYIQwjhgHORUfauqOS1xTQi++OIL\nAP71r39Fmc5o/v6kU+6ypxKr/nRgsIisLyLLA4cCD1dwPMdxMqLdM34I4XMROQ14DOgK3BJCeLVq\nPWtS3GBVwN6Ljz/+uIXMKVDufWn3dl57EJGghhtV5xynFHy3o3RKseq7557jNCE1t+p3BL169Yrt\n5ZZbDoDFixfX7HzduhVuo91OKXVrxSmOz/TVxWd8x2lCMp/xazULrrXWWrF93333xfagQYMAGDNm\nTJQ9+OCDQPtmkU033TS2J0yYAMAHH3wQZbvsskts2+2oesPXzAVWXHHF2D7wwAMBOOigg6JM36E1\n11wzytQRDQqORPPmzYuySy65BICHHy5sdNWTXctnfMdpQnzgO04Tkvl2Xg2OCRSMeFBQzQDGjh0L\nwL///e8o+9GPfgQkDX56HIANNtgAgBtvvDHKtt56ayCp4ilPP/10bO+6666xXQ+q3e677x7b1113\nXWyvvfbaQPr1/N///V9sL1hQcMZ86KGHABg/fnyUvfZaLibLehw2wvJhjTXWiO3p06fHdv/+/YHk\n+5B2PfZz216WhQsXxvZGG20U27VcBvp2nuM4qfjAd5wmpOFV/WLstddeANx1111RpqGvFiuzceDL\nYncl3n//fQD23XffKHv11VdTf1fJ6n737NkTgHfffTfKVl111aqfZ+nSpUByifPiiy9W/TzVxj7j\noUOHxvaWW24JFK4LYPbs2UBy98Yukc466ywAzjjjjChLU/8vuuii2L7gggva3fdiuKrvOE4qndJz\nLw2b1MEaApU0Y45NhnHPPfcAcM0110SZGgc//fTTKNNYBHseO8tnFXar4as33XRTlI0cOTK2V199\ndQC6d+8eZXoPit0rix7Haj2NMONbbeyFF15IbZfKmWeeCRQ0QIBLL720xe8dfvjhsX3hhRcCHWcI\n9RnfcZoQH/iO04R0SuOeVdvVyGVdbT/88EMgmb/NqvXatvcm7T6pgciqxnZ/WPn73/8e28X2b+vN\nldYawebPnw8UfACg0E91dYXCfn8tKLa/3pHYZZPmD7D9nTVrVmzb97HauHHPcZxUOo1xz85MNkz2\no48+AmDq1KlVP6du6dx8881Rtscee8T2O++8A8BRRx0VZfpXvx68+krBGsE233xzIGnEUt5+++1M\n+mONjdbDsB5I0/Ys+i7WA0VnfBG5RUSWiMgrRtZLRCaKyJz8/6vXtpuO41STUlT924A9l5GdC0wO\nIQwGJud/dhynQSiq6ocQnhKRAcuIRwE75du3A1OAc6rYr7Kx++ebbLJJbL/ySk5RqVS1ViPNdtsV\naob8+c9/Blrf6954441b9OeNN95o0Z+2gjzqiX/+858APPXUU1GmfddlTa2x3ofWIPvZZ58BHZP1\nSO+BDYJKM9LecMMN2XasDdpr3OsdQtCwo0VA7yr1x3GcDKjYuBdCCG1t03klHcepP9o78BeLSJ8Q\nwkIR6QMsae0Xl62k087zFcVWWLHBEpoe63e/+13Zx7TLBw3yOeSQQ9r8zieffBLbTzzxBFCIWV+2\nn4pVBxtB7bfBJuqCXM1raGt//itf+UpsDxs2LLaHDBkCwGWXXRZl9lm01bf2+APYVG+qwtsdHcUu\n6SZPnlz2eWpFe1X9h4Gj8+2jgd9XpzuO42RB0RlfRO4hZ8hbU0TmAxcCPwPGi8hxwLtA29Ng8nhA\n9b2u7PFsoMUtt9wCwHrrrRdlV199dZvHWmmllQB49NFHo2yHHXYAksajP/zhDwAcfPDBUWY/V48+\nO8sXMz6pP0JH7vPbWXGVVVaJ7eOPPx6AU045JcrUl8H2116jek5aQ9zcuXMBuP/++6PszjvvjO1/\n/OMfrfbNZlJSgx7AqFGjABg4cGCUXX/99QAsWrQoyvr27dtCZkOX04Ko9H4cd9xxUXbaaafFtoZ0\npz1bK7MenB1NKVb9w1r5aNdW5I7j1Dnusus4TUinrJ2nqjoUkh3aQJr33nsPgCuvvDL1O6q6r7/+\n+lGmSSf33LPgy1SsOk+aGlzsutXduBbloK1bc48ePQDYbbfdouzEE3ObL9tss02U2X3ztjITlYPe\nA5vH4PTTT4/tO+64o9Xv2kxJ1vg6evRooJAUFeDll18Gkmq9GgTVJwGSue9nzJjR4py6TDzyyCOj\nzC4n9d24++67o8wuOZRzzim4ulx++eUtPq8WHqTjOE4qmc74Xbp0CerlVssAC2ucUu86Nc4t+3ka\nek9sCO3Pf/5zIJlZpS1DEBS0CGvYKmbcq7ZGZGdpOwtpRiGbb87OoNVC76VWm4HCPbL36plnnolt\nzd+Xdq9a0zq0yo0aE6EQdm0r5Rx9dG4z6ogjjogyzakH8Mtf/hJI5tzr3Tvnn2a1DRuopMFL+l1I\nhugqdqylGRmrhc/4juOk4gPfcZqQTFX9bt26Bd0XTlOnqxVgYePx1eCi+7z289ZU/rR7ktbfcePG\nAcmElvYa1IBk92+zVvVtjPgPfvCD2D7mmGOA9JTb9vqtiq59t8s09Y6z++s2aEmzHdllxIYbbggk\n1WH7ffXCs1V80o5t71Ga/0Pac9RjP/bYY1FmDb+q9ttnpsuIPn36RJm9r7oEsNdYbDmpx9dlBFTv\n/XdV33GcVHzgO04TkmnqrZ49e7LPPvsA8L3vfQ+Axx9/PH7+/e9/H2ifO6+19uo5ADbbbDMgaaVV\nS+qDDz4YZbZw4k477QTAySefHGW6RLGqsbptjhgxIspsxR5dZnREjLiq0arSA+y4446xrTsSaaqz\nqueQVIk1xZhNGqk7FtZyfuqpp8a23iP7uarB9r7YyjRa/UjdrS1WhbbfL/Uea4owa/23qr7Nt9DW\nuS3qc2Et9OonkWbdB1httdWA5E6BdUGuNT7jO04TkumMv84668QS1euss06Lz88++2ygvBlf/wrb\nQBlb7UZnalv37NprrwWSs7Pdk3/kkUeApDFs++23BwoeYlDYF7fGLmtcyvIvOCRn7/333x9IJvq0\noaRp6OxjjVhWe9JsRvPmzYsy1bQOPfTQKNNnbI9pvfReeukloGA0A+jVq1dsaznpNG3EGhvbgz4r\n+7ztrKzvXlpqdeuPMWnSpNjWoCXrDai+CDYc3Br/9L7Ze6AepVngM77jNCE+8B2nCclU1e/SpUtU\nvdWd8thjj42ft8cIpgEUv/jFL6LM7rGqSmeNVPfeey+QVN3SsCr8lClTAHj66aejTANC1A0Ukgae\n9uzFl3oPrBqsavLee+8dZeeem0t8bPMQWP+GUgNu+vXrF9u6RLJLKe1va4YvrShjg4FU1bcqtr2H\nupfelj9Fe9H+2sSY3/rWt2L7vvvuA2DixIlRpgZB+2yLPae0JK9pLtFf/epXY1srFWXhW+MzvuM0\nIZnO+J9++mkMe9QwzLf
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcab14e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1710... Discriminator Loss: 0.9720... Generator Loss: 1.2247\n",
      "Epoch 1/2-Step 1720... Discriminator Loss: 1.5625... Generator Loss: 1.0967\n",
      "Epoch 1/2-Step 1730... Discriminator Loss: 1.3570... Generator Loss: 1.2406\n",
      "Epoch 1/2-Step 1740... Discriminator Loss: 0.9930... Generator Loss: 1.0665\n",
      "Epoch 1/2-Step 1750... Discriminator Loss: 1.2531... Generator Loss: 1.1553\n",
      "Epoch 1/2-Step 1760... Discriminator Loss: 1.6308... Generator Loss: 2.6386\n",
      "Epoch 1/2-Step 1770... Discriminator Loss: 1.5074... Generator Loss: 2.9194\n",
      "Epoch 1/2-Step 1780... Discriminator Loss: 1.1102... Generator Loss: 0.9617\n",
      "Epoch 1/2-Step 1790... Discriminator Loss: 1.5774... Generator Loss: 0.8478\n",
      "Epoch 1/2-Step 1800... Discriminator Loss: 1.6303... Generator Loss: 0.7241\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc6c1588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1810... Discriminator Loss: 1.4055... Generator Loss: 0.9881\n",
      "Epoch 1/2-Step 1820... Discriminator Loss: 0.9379... Generator Loss: 1.4142\n",
      "Epoch 1/2-Step 1830... Discriminator Loss: 1.2579... Generator Loss: 1.0808\n",
      "Epoch 1/2-Step 1840... Discriminator Loss: 0.9951... Generator Loss: 1.7914\n",
      "Epoch 1/2-Step 1850... Discriminator Loss: 1.2072... Generator Loss: 1.8785\n",
      "Epoch 1/2-Step 1860... Discriminator Loss: 1.7810... Generator Loss: 0.9379\n",
      "Epoch 1/2-Step 1870... Discriminator Loss: 1.6841... Generator Loss: 0.7043\n",
      "Epoch 1/2-Step 1880... Discriminator Loss: 1.6064... Generator Loss: 0.4850\n",
      "Epoch 1/2-Step 1890... Discriminator Loss: 1.9991... Generator Loss: 0.9446\n",
      "Epoch 1/2-Step 1900... Discriminator Loss: 0.7067... Generator Loss: 2.2667\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmOGcez0r+w012E0nDlG
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc32acc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 1910... Discriminator Loss: 1.0243... Generator Loss: 1.6444\n",
      "Epoch 1/2-Step 1920... Discriminator Loss: 1.2215... Generator Loss: 1.3887\n",
      "Epoch 1/2-Step 1930... Discriminator Loss: 1.6844... Generator Loss: 0.7205\n",
      "Epoch 1/2-Step 1940... Discriminator Loss: 1.4666... Generator Loss: 0.9609\n",
      "Epoch 1/2-Step 1950... Discriminator Loss: 2.2047... Generator Loss: 0.2255\n",
      "Epoch 1/2-Step 1960... Discriminator Loss: 0.9293... Generator Loss: 1.8723\n",
      "Epoch 1/2-Step 1970... Discriminator Loss: 1.9385... Generator Loss: 1.2363\n",
      "Epoch 1/2-Step 1980... Discriminator Loss: 1.3688... Generator Loss: 1.4991\n",
      "Epoch 1/2-Step 1990... Discriminator Loss: 1.4144... Generator Loss: 2.1793\n",
      "Epoch 1/2-Step 2000... Discriminator Loss: 2.1385... Generator Loss: 1.0334\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc46d898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2010... Discriminator Loss: 1.8282... Generator Loss: 2.2538\n",
      "Epoch 1/2-Step 2020... Discriminator Loss: 1.3680... Generator Loss: 1.4629\n",
      "Epoch 1/2-Step 2030... Discriminator Loss: 1.2789... Generator Loss: 1.4095\n",
      "Epoch 1/2-Step 2040... Discriminator Loss: 0.7795... Generator Loss: 1.7727\n",
      "Epoch 1/2-Step 2050... Discriminator Loss: 1.8060... Generator Loss: 1.1282\n",
      "Epoch 1/2-Step 2060... Discriminator Loss: 2.7817... Generator Loss: 0.3962\n",
      "Epoch 1/2-Step 2070... Discriminator Loss: 1.3030... Generator Loss: 1.8516\n",
      "Epoch 1/2-Step 2080... Discriminator Loss: 1.2551... Generator Loss: 2.0143\n",
      "Epoch 1/2-Step 2090... Discriminator Loss: 1.1564... Generator Loss: 1.1777\n",
      "Epoch 1/2-Step 2100... Discriminator Loss: 1.5885... Generator Loss: 0.5241\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOaOSzigkryaShQbyvc1
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc33b240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2110... Discriminator Loss: 1.3204... Generator Loss: 1.6334\n",
      "Epoch 1/2-Step 2120... Discriminator Loss: 1.0556... Generator Loss: 1.4026\n",
      "Epoch 1/2-Step 2130... Discriminator Loss: 1.1855... Generator Loss: 1.3955\n",
      "Epoch 1/2-Step 2140... Discriminator Loss: 1.4678... Generator Loss: 2.5821\n",
      "Epoch 1/2-Step 2150... Discriminator Loss: 1.8101... Generator Loss: 0.6262\n",
      "Epoch 1/2-Step 2160... Discriminator Loss: 1.7871... Generator Loss: 1.3442\n",
      "Epoch 1/2-Step 2170... Discriminator Loss: 1.3084... Generator Loss: 0.9783\n",
      "Epoch 1/2-Step 2180... Discriminator Loss: 1.1878... Generator Loss: 1.0560\n",
      "Epoch 1/2-Step 2190... Discriminator Loss: 1.4956... Generator Loss: 1.1276\n",
      "Epoch 1/2-Step 2200... Discriminator Loss: 2.5187... Generator Loss: 0.3612\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO+8cZV/96lfj2EY6doT
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7ff4978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2210... Discriminator Loss: 1.3161... Generator Loss: 0.9862\n",
      "Epoch 1/2-Step 2220... Discriminator Loss: 1.1794... Generator Loss: 1.5978\n",
      "Epoch 1/2-Step 2230... Discriminator Loss: 1.7036... Generator Loss: 1.4239\n",
      "Epoch 1/2-Step 2240... Discriminator Loss: 1.2739... Generator Loss: 1.7801\n",
      "Epoch 1/2-Step 2250... Discriminator Loss: 1.1780... Generator Loss: 0.7900\n",
      "Epoch 1/2-Step 2260... Discriminator Loss: 1.6259... Generator Loss: 0.6541\n",
      "Epoch 1/2-Step 2270... Discriminator Loss: 1.0366... Generator Loss: 1.3761\n",
      "Epoch 1/2-Step 2280... Discriminator Loss: 0.9188... Generator Loss: 1.4899\n",
      "Epoch 1/2-Step 2290... Discriminator Loss: 1.3118... Generator Loss: 1.9320\n",
      "Epoch 1/2-Step 2300... Discriminator Loss: 1.5558... Generator Loss: 1.8601\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niDOz4sx7/V7t/Ntkk02
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc1f4198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2310... Discriminator Loss: 1.3513... Generator Loss: 1.1792\n",
      "Epoch 1/2-Step 2320... Discriminator Loss: 1.1276... Generator Loss: 1.6514\n",
      "Epoch 1/2-Step 2330... Discriminator Loss: 1.0582... Generator Loss: 1.3400\n",
      "Epoch 1/2-Step 2340... Discriminator Loss: 1.0007... Generator Loss: 1.2159\n",
      "Epoch 1/2-Step 2350... Discriminator Loss: 1.3421... Generator Loss: 0.4202\n",
      "Epoch 1/2-Step 2360... Discriminator Loss: 0.9796... Generator Loss: 3.7176\n",
      "Epoch 1/2-Step 2370... Discriminator Loss: 1.4807... Generator Loss: 0.4350\n",
      "Epoch 1/2-Step 2380... Discriminator Loss: 0.9803... Generator Loss: 2.3177\n",
      "Epoch 1/2-Step 2390... Discriminator Loss: 1.9081... Generator Loss: 0.8476\n",
      "Epoch 1/2-Step 2400... Discriminator Loss: 1.4687... Generator Loss: 0.9120\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXHNNaIvar735xNilDX/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7f6ce48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2410... Discriminator Loss: 1.1684... Generator Loss: 0.7956\n",
      "Epoch 1/2-Step 2420... Discriminator Loss: 0.6919... Generator Loss: 1.3747\n",
      "Epoch 1/2-Step 2430... Discriminator Loss: 0.8773... Generator Loss: 1.5710\n",
      "Epoch 1/2-Step 2440... Discriminator Loss: 1.2435... Generator Loss: 2.3948\n",
      "Epoch 1/2-Step 2450... Discriminator Loss: 0.8684... Generator Loss: 1.5118\n",
      "Epoch 1/2-Step 2460... Discriminator Loss: 1.1518... Generator Loss: 0.8184\n",
      "Epoch 1/2-Step 2470... Discriminator Loss: 0.7865... Generator Loss: 1.7250\n",
      "Epoch 1/2-Step 2480... Discriminator Loss: 2.0862... Generator Loss: 0.6269\n",
      "Epoch 1/2-Step 2490... Discriminator Loss: 0.7544... Generator Loss: 1.5364\n",
      "Epoch 1/2-Step 2500... Discriminator Loss: 0.7736... Generator Loss: 2.7293\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n//33jzKdDW34qDWAqpb
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc0bb6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2510... Discriminator Loss: 1.6349... Generator Loss: 1.0930\n",
      "Epoch 1/2-Step 2520... Discriminator Loss: 1.8854... Generator Loss: 0.6645\n",
      "Epoch 1/2-Step 2530... Discriminator Loss: 1.5640... Generator Loss: 1.0977\n",
      "Epoch 1/2-Step 2540... Discriminator Loss: 1.0245... Generator Loss: 1.7135\n",
      "Epoch 1/2-Step 2550... Discriminator Loss: 0.8430... Generator Loss: 1.1761\n",
      "Epoch 1/2-Step 2560... Discriminator Loss: 1.6688... Generator Loss: 0.8801\n",
      "Epoch 1/2-Step 2570... Discriminator Loss: 0.8400... Generator Loss: 3.5527\n",
      "Epoch 1/2-Step 2580... Discriminator Loss: 1.1897... Generator Loss: 2.6267\n",
      "Epoch 1/2-Step 2590... Discriminator Loss: 1.3093... Generator Loss: 3.1767\n",
      "Epoch 1/2-Step 2600... Discriminator Loss: 1.3642... Generator Loss: 1.4354\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0NflnnvuCbJU5unfv3/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7b96d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2610... Discriminator Loss: 0.9792... Generator Loss: 2.6876\n",
      "Epoch 1/2-Step 2620... Discriminator Loss: 1.1148... Generator Loss: 1.1051\n",
      "Epoch 1/2-Step 2630... Discriminator Loss: 0.9979... Generator Loss: 0.9700\n",
      "Epoch 1/2-Step 2640... Discriminator Loss: 0.6082... Generator Loss: 1.9845\n",
      "Epoch 1/2-Step 2650... Discriminator Loss: 1.0193... Generator Loss: 1.4141\n",
      "Epoch 1/2-Step 2660... Discriminator Loss: 0.8010... Generator Loss: 1.6993\n",
      "Epoch 1/2-Step 2670... Discriminator Loss: 0.9638... Generator Loss: 1.9727\n",
      "Epoch 1/2-Step 2680... Discriminator Loss: 4.1851... Generator Loss: 5.9589\n",
      "Epoch 1/2-Step 2690... Discriminator Loss: 1.0559... Generator Loss: 2.3036\n",
      "Epoch 1/2-Step 2700... Discriminator Loss: 0.7456... Generator Loss: 3.0289\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNDrxQZuYTRxTGIu5p9f
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db76f6198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2710... Discriminator Loss: 1.6097... Generator Loss: 0.8380\n",
      "Epoch 1/2-Step 2720... Discriminator Loss: 0.7764... Generator Loss: 1.9949\n",
      "Epoch 1/2-Step 2730... Discriminator Loss: 1.9236... Generator Loss: 0.5473\n",
      "Epoch 1/2-Step 2740... Discriminator Loss: 1.0212... Generator Loss: 2.1570\n",
      "Epoch 1/2-Step 2750... Discriminator Loss: 1.3450... Generator Loss: 3.2112\n",
      "Epoch 1/2-Step 2760... Discriminator Loss: 1.0912... Generator Loss: 0.5948\n",
      "Epoch 1/2-Step 2770... Discriminator Loss: 1.8577... Generator Loss: 1.2803\n",
      "Epoch 1/2-Step 2780... Discriminator Loss: 1.6736... Generator Loss: 0.4892\n",
      "Epoch 1/2-Step 2790... Discriminator Loss: 1.8356... Generator Loss: 0.4581\n",
      "Epoch 1/2-Step 2800... Discriminator Loss: 1.1618... Generator Loss: 2.8949\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nunXrBkRj7fVyXtwy28i
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7ce2e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2810... Discriminator Loss: 0.8225... Generator Loss: 1.0229\n",
      "Epoch 1/2-Step 2820... Discriminator Loss: 0.9250... Generator Loss: 2.2914\n",
      "Epoch 1/2-Step 2830... Discriminator Loss: 0.7572... Generator Loss: 1.8686\n",
      "Epoch 1/2-Step 2840... Discriminator Loss: 0.6191... Generator Loss: 1.5279\n",
      "Epoch 1/2-Step 2850... Discriminator Loss: 1.2797... Generator Loss: 0.5229\n",
      "Epoch 1/2-Step 2860... Discriminator Loss: 2.3627... Generator Loss: 0.4211\n",
      "Epoch 1/2-Step 2870... Discriminator Loss: 2.2202... Generator Loss: 0.6415\n",
      "Epoch 1/2-Step 2880... Discriminator Loss: 0.9318... Generator Loss: 2.2874\n",
      "Epoch 1/2-Step 2890... Discriminator Loss: 1.1470... Generator Loss: 1.2436\n",
      "Epoch 1/2-Step 2900... Discriminator Loss: 0.9684... Generator Loss: 1.6122\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUKO47rYc3V8xUmmZ9kZ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7916400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 2910... Discriminator Loss: 0.9839... Generator Loss: 1.8861\n",
      "Epoch 1/2-Step 2920... Discriminator Loss: 0.6033... Generator Loss: 2.6670\n",
      "Epoch 1/2-Step 2930... Discriminator Loss: 1.0567... Generator Loss: 1.3305\n",
      "Epoch 1/2-Step 2940... Discriminator Loss: 1.3203... Generator Loss: 0.8143\n",
      "Epoch 1/2-Step 2950... Discriminator Loss: 0.9820... Generator Loss: 1.9188\n",
      "Epoch 1/2-Step 2960... Discriminator Loss: 1.8473... Generator Loss: 0.5898\n",
      "Epoch 1/2-Step 2970... Discriminator Loss: 0.8452... Generator Loss: 1.5901\n",
      "Epoch 1/2-Step 2980... Discriminator Loss: 1.1733... Generator Loss: 1.6805\n",
      "Epoch 1/2-Step 2990... Discriminator Loss: 1.4451... Generator Loss: 4.1489\n",
      "Epoch 1/2-Step 3000... Discriminator Loss: 1.5283... Generator Loss: 0.9003\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db77ff358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3010... Discriminator Loss: 1.0045... Generator Loss: 1.3305\n",
      "Epoch 1/2-Step 3020... Discriminator Loss: 1.2027... Generator Loss: 0.6926\n",
      "Epoch 1/2-Step 3030... Discriminator Loss: 1.4056... Generator Loss: 1.7993\n",
      "Epoch 1/2-Step 3040... Discriminator Loss: 0.7977... Generator Loss: 2.2008\n",
      "Epoch 1/2-Step 3050... Discriminator Loss: 0.9407... Generator Loss: 3.3201\n",
      "Epoch 1/2-Step 3060... Discriminator Loss: 1.6828... Generator Loss: 1.4347\n",
      "Epoch 1/2-Step 3070... Discriminator Loss: 0.9688... Generator Loss: 1.4361\n",
      "Epoch 1/2-Step 3080... Discriminator Loss: 1.1253... Generator Loss: 1.6667\n",
      "Epoch 1/2-Step 3090... Discriminator Loss: 1.3357... Generator Loss: 1.5047\n",
      "Epoch 1/2-Step 3100... Discriminator Loss: 1.9671... Generator Loss: 1.1792\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOF9zJUZpxLn3wgsvBF1
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db71ea9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3110... Discriminator Loss: 0.7444... Generator Loss: 1.8457\n",
      "Epoch 1/2-Step 3120... Discriminator Loss: 1.2955... Generator Loss: 1.3343\n",
      "Epoch 1/2-Step 3130... Discriminator Loss: 1.9783... Generator Loss: 0.7650\n",
      "Epoch 1/2-Step 3140... Discriminator Loss: 1.2794... Generator Loss: 1.7128\n",
      "Epoch 1/2-Step 3150... Discriminator Loss: 0.9588... Generator Loss: 1.4020\n",
      "Epoch 1/2-Step 3160... Discriminator Loss: 1.6277... Generator Loss: 0.9372\n",
      "Epoch 1/2-Step 3170... Discriminator Loss: 0.7439... Generator Loss: 2.2052\n",
      "Epoch 1/2-Step 3180... Discriminator Loss: 0.8528... Generator Loss: 1.7296\n",
      "Epoch 1/2-Step 3190... Discriminator Loss: 0.9380... Generator Loss: 1.8576\n",
      "Epoch 1/2-Step 3200... Discriminator Loss: 1.0516... Generator Loss: 1.7026\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhhr6vaX3GhQi6XR/Onm
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db71b8ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3210... Discriminator Loss: 1.1894... Generator Loss: 1.5121\n",
      "Epoch 1/2-Step 3220... Discriminator Loss: 1.6376... Generator Loss: 0.7133\n",
      "Epoch 1/2-Step 3230... Discriminator Loss: 0.9435... Generator Loss: 1.6021\n",
      "Epoch 1/2-Step 3240... Discriminator Loss: 0.6321... Generator Loss: 1.9048\n",
      "Epoch 1/2-Step 3250... Discriminator Loss: 1.5177... Generator Loss: 0.6432\n",
      "Epoch 1/2-Step 3260... Discriminator Loss: 1.1988... Generator Loss: 1.5884\n",
      "Epoch 1/2-Step 3270... Discriminator Loss: 0.8108... Generator Loss: 1.3113\n",
      "Epoch 1/2-Step 3280... Discriminator Loss: 0.6272... Generator Loss: 2.3086\n",
      "Epoch 1/2-Step 3290... Discriminator Loss: 0.8616... Generator Loss: 1.8550\n",
      "Epoch 1/2-Step 3300... Discriminator Loss: 1.0116... Generator Loss: 2.2713\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nO9KUAqIVZmqFXqnFhSv
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db77e05c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3310... Discriminator Loss: 0.9675... Generator Loss: 1.0892\n",
      "Epoch 1/2-Step 3320... Discriminator Loss: 1.7879... Generator Loss: 3.6830\n",
      "Epoch 1/2-Step 3330... Discriminator Loss: 0.9832... Generator Loss: 1.2708\n",
      "Epoch 1/2-Step 3340... Discriminator Loss: 0.9825... Generator Loss: 1.6189\n",
      "Epoch 1/2-Step 3350... Discriminator Loss: 1.2052... Generator Loss: 2.5176\n",
      "Epoch 1/2-Step 3360... Discriminator Loss: 0.8736... Generator Loss: 1.6014\n",
      "Epoch 1/2-Step 3370... Discriminator Loss: 1.3317... Generator Loss: 0.8679\n",
      "Epoch 1/2-Step 3380... Discriminator Loss: 1.8753... Generator Loss: 0.8683\n",
      "Epoch 1/2-Step 3390... Discriminator Loss: 1.3497... Generator Loss: 1.1751\n",
      "Epoch 1/2-Step 3400... Discriminator Loss: 1.9078... Generator Loss: 1.0901\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndNMRtUmXPt53332BqA9
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7570c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3410... Discriminator Loss: 0.8109... Generator Loss: 2.9047\n",
      "Epoch 1/2-Step 3420... Discriminator Loss: 0.6065... Generator Loss: 2.0010\n",
      "Epoch 1/2-Step 3430... Discriminator Loss: 0.5993... Generator Loss: 2.5057\n",
      "Epoch 1/2-Step 3440... Discriminator Loss: 0.9897... Generator Loss: 1.7310\n",
      "Epoch 1/2-Step 3450... Discriminator Loss: 0.9016... Generator Loss: 2.6209\n",
      "Epoch 1/2-Step 3460... Discriminator Loss: 1.1423... Generator Loss: 1.5937\n",
      "Epoch 1/2-Step 3470... Discriminator Loss: 0.5596... Generator Loss: 1.8234\n",
      "Epoch 1/2-Step 3480... Discriminator Loss: 1.4996... Generator Loss: 0.9788\n",
      "Epoch 1/2-Step 3490... Discriminator Loss: 0.9864... Generator Loss: 1.2390\n",
      "Epoch 1/2-Step 3500... Discriminator Loss: 1.1743... Generator Loss: 1.9742\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngCdbX39jYDJFdtPxhhq
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7ac57f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3510... Discriminator Loss: 1.5141... Generator Loss: 1.1573\n",
      "Epoch 1/2-Step 3520... Discriminator Loss: 1.3913... Generator Loss: 0.3091\n",
      "Epoch 1/2-Step 3530... Discriminator Loss: 1.5702... Generator Loss: 0.9008\n",
      "Epoch 1/2-Step 3540... Discriminator Loss: 0.5940... Generator Loss: 2.6815\n",
      "Epoch 1/2-Step 3550... Discriminator Loss: 0.9067... Generator Loss: 1.8273\n",
      "Epoch 1/2-Step 3560... Discriminator Loss: 0.6312... Generator Loss: 2.2397\n",
      "Epoch 1/2-Step 3570... Discriminator Loss: 0.9396... Generator Loss: 2.2278\n",
      "Epoch 1/2-Step 3580... Discriminator Loss: 2.0399... Generator Loss: 0.5491\n",
      "Epoch 1/2-Step 3590... Discriminator Loss: 0.9086... Generator Loss: 0.8842\n",
      "Epoch 1/2-Step 3600... Discriminator Loss: 1.4444... Generator Loss: 1.0543\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db76dfef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3610... Discriminator Loss: 0.5347... Generator Loss: 3.4708\n",
      "Epoch 1/2-Step 3620... Discriminator Loss: 1.5718... Generator Loss: 0.7647\n",
      "Epoch 1/2-Step 3630... Discriminator Loss: 0.8013... Generator Loss: 2.0245\n",
      "Epoch 1/2-Step 3640... Discriminator Loss: 1.6968... Generator Loss: 2.3635\n",
      "Epoch 1/2-Step 3650... Discriminator Loss: 0.6283... Generator Loss: 2.0152\n",
      "Epoch 1/2-Step 3660... Discriminator Loss: 1.0592... Generator Loss: 1.0891\n",
      "Epoch 1/2-Step 3670... Discriminator Loss: 0.5548... Generator Loss: 2.8192\n",
      "Epoch 1/2-Step 3680... Discriminator Loss: 0.9127... Generator Loss: 1.8178\n",
      "Epoch 1/2-Step 3690... Discriminator Loss: 2.0284... Generator Loss: 0.5596\n",
      "Epoch 1/2-Step 3700... Discriminator Loss: 1.2324... Generator Loss: 1.1205\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\no2yNLyKrA08Avw4hTBK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db79075c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2-Step 3710... Discriminator Loss: 1.1957... Generator Loss: 2.9403\n",
      "Epoch 1/2-Step 3720... Discriminator Loss: 1.4918... Generator Loss: 0.9780\n",
      "Epoch 1/2-Step 3730... Discriminator Loss: 0.8119... Generator Loss: 3.1082\n",
      "Epoch 1/2-Step 3740... Discriminator Loss: 1.2655... Generator Loss: 1.1769\n",
      "Epoch 1/2-Step 3750... Discriminator Loss: 1.7132... Generator Loss: 1.4216\n",
      "Epoch 2/2-Step 3760... Discriminator Loss: 0.9273... Generator Loss: 1.4539\n",
      "Epoch 2/2-Step 3770... Discriminator Loss: 0.6323... Generator Loss: 3.1133\n",
      "Epoch 2/2-Step 3780... Discriminator Loss: 0.8319... Generator Loss: 2.0285\n",
      "Epoch 2/2-Step 3790... Discriminator Loss: 1.2167... Generator Loss: 2.7195\n",
      "Epoch 2/2-Step 3800... Discriminator Loss: 0.6840... Generator Loss: 2.4182\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n7+td55fCV19+mXfeeec
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7a5fb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3810... Discriminator Loss: 0.8050... Generator Loss: 1.9574\n",
      "Epoch 2/2-Step 3820... Discriminator Loss: 0.8091... Generator Loss: 1.1413\n",
      "Epoch 2/2-Step 3830... Discriminator Loss: 1.0711... Generator Loss: 1.4569\n",
      "Epoch 2/2-Step 3840... Discriminator Loss: 0.8343... Generator Loss: 2.4736\n",
      "Epoch 2/2-Step 3850... Discriminator Loss: 0.9920... Generator Loss: 1.6217\n",
      "Epoch 2/2-Step 3860... Discriminator Loss: 0.8220... Generator Loss: 2.2878\n",
      "Epoch 2/2-Step 3870... Discriminator Loss: 1.2535... Generator Loss: 2.0134\n",
      "Epoch 2/2-Step 3880... Discriminator Loss: 0.8582... Generator Loss: 2.1332\n",
      "Epoch 2/2-Step 3890... Discriminator Loss: 2.4133... Generator Loss: 3.6196\n",
      "Epoch 2/2-Step 3900... Discriminator Loss: 1.5483... Generator Loss: 0.9423\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXe4VNW1wH8L7EREUBEFBcSGIooNDRoFFbt+Rk0wGmOI\nmOizRY1BY4t5GmNFxG58RlDU2E2iIrEl9oYFRIoiIGBBwBJjjPv9MbP2rLn33Dtz7516z/p9Hx/7\nrilnnzZrnbVXkRACjuOkiw7VnoDjOJXHb3zHSSF+4ztOCvEb33FSiN/4jpNC/MZ3nBTiN77jpJA2\n3fgisqeITBeRmSLy61JNynGc8iKtDeARkY7AO8DuwDzgRWBECGFq6abnOE45WK4Nn90OmBlCmA0g\nIhOBA4Amb/yOHTuG5ZbLbPLrr79uw6YrT4cOGeOoW7duUbb66qsDYH88P/nkkzhetmwZAP/973+j\nLOmHVkTiePnllwfgP//5T6Ntf/vtt81+T3vGHiMlbcegWEIIjQ9WA9py468LzDV/zwO2b3Zjyy1H\njx49Mm+eNw+on4t55ZVXBuDggw+OskMPPRSAr776KsomTJgQx48++igAS5YsiTK7v0rHjh3jWI/P\nokWLomyllVYC4Msvv4wy+8NZy8etLdibXY+B5V//+lclp9OuaMuNXxQiMgoYBfkXuOM41aMtz/g7\nAOeGEIZn/x4NEEK4sKnPdOjQIagpW2+mvqKPKgD9+/cHoHPnzlH2zjvvxPHixYsB+Oabb5r9TqvZ\nunbtCsC///3vRq9/9tlnrZ12u8Eeq/Zq6bSVYkz9tnj1XwQ2FJE+IrIC8EPggTZ8n+M4FaLVpn4I\n4RsR+R/gEaAj8McQwltFfK61m6wJrPZ+/fXXS/Kd9pisssoqQP7zq/UhVBr1bUBu363j0alP2vSM\nH0L4K/DXEs3FcZwK4ZF7jpNCWu3ca9XGRIJ69u3atpNDTetqLlXtsccecXzmmWfG8aRJkwC4+OKL\no8w6ISuBO/cKU27nnuM4dUrFNb7+Ytfar7XVJBopVw2rpNIWkY2tuO+++wAYPnx4lOmxsHNaunRp\nlD355JMAnHfeeVH21ls5H2+pz7Nr/ORjYK8b1/iO4yTiN77jpJBUm/pdunSJ47/97W9xvP766wOw\n2267RdnUqZVJOkxKyCkHGvv+1FNPRdmgQYPy5tCQ5hKM7Gs2KvP2228H4Be/+EWUtcYhmKYkHbuv\nK664Yhzvtddeef8D3HbbbQDMnDkTyOR4fP31127qO47TGL/xHSeFlD07ryHVMs+syXTEEUcAcPnl\nl0dZp06dGn3myiuvjGNNx7VmrJqspfTAl/P4WBPyt7/9LQBbbbVVlCWZ+HbfNGTXPobo2B5fTcQC\nOPzwwwHYaaedouy4444DcisCUNj8r+ZKS1uwxzRp5cieb319hRVWiDJ7fs444wwA1llnnSjT9PYP\nPvig0faanVdx03ccpz1RcY1faTTpxUagnXzyyUBycQfI/QrrujbAuuuuC8DZZ58dZarZfvWrX0XZ\nrFmzGn1PrWBTivfdd1+gsJZfsGBBHL/yyisA3HHHHVE2ZcoUIP9YqpMQYOedd270/TvssAMA77//\nfpSpcwoKpzHXGrqG/vOf/zzKNK7Bpmxb1ML54osvokzHNjFKr1+7nblzc/Vvrr32WiBX7KXYBCrX\n+I6TQvzGd5wU0i5NfetoOvroowE44YQToqwpE1/R2nbW5NT16M0337zR+9dbb7043m677eK4NaZ+\nOeMc7HFZe+2187ZnseanNevHjRsH5BxKkGyW6yMBwE033dTodd1mS2IVas2pp7EeAE888QSQ73TT\nRyh7LG1dBTXhbfHWNdZYA8g/Lp9++mkcP/bYYwCMGjUqyuz3twTX+I6TQtqNxreayzqUfvOb3wDJ\ny3VNocspV1xxRSNZEqV0RiVp4FJhtVTS8VArw5YIv/vuu+N4/vz5QMv2N8lyaYs1U80kne9///tx\nPH78+DhWp5utjPzTn/4UgH/84x9Rtuqqq8bxUUcdBeQnRKml9NBDD0WZjRhV7V+KCkgFNb6I/FFE\nPhSRN42sq4hMEpEZ2f9Xb/NMHMepGMWY+v8H7NlA9mtgcghhQ2By9m/HceqEgqZ+COEpEendQHwA\nsEt2fAvwBHB6CefVYlZbbbU4Hjt2bBxrueqWYCPPmkOdNRqJBm1Pril1co7Nt7fxBnZNX1HT+dVX\nX42yadOmxXEtFNmsRmyEViTShBjIj3/QeIQf/ehHUaaFWO3xHzZsWBxrFyaNKYFcHYNKxDG01rnX\nPYSgkR0Lge4lmo/jOBWgzc69EEIQkSZ/hm0nHcdxaoPW3viLRKRHCGGBiPQAPmzqjSGE64HrIZOP\n38rtFeT888+P4w022CCOk7zk2sxyzpw5UbbJJpvEcXOmvi2CqQkor732WitmnEypTdnNNtssjq0H\nOQnt1HPJJZdEmR6rtKAmvIY0A0ycOBHIPzf2etNkLvu4OXr0aAD22WefKLPH8tRTTwXgjTfeiLJy\n12CwtNbUfwA4Mjs+Eri/NNNxHKcSFNT4InI7GUfeGiIyDzgH+D1wp4iMBOYAh5Zzks2hEWgjR46M\nsqTmnPbX9LnnngPynVVW4yeh6bjHHHNMlOl6a61FlQF85zvfAfIj72yUmGLnrskfM2bMiLJaSzQq\nB9YqPPLIjD6zVo86cY899tgou+uuuxp9T+/eveNYrxcbZWcjGiup3ZMoxqs/oomXhjUhdxynxvGQ\nXcdJIXUfsnvKKacAhRNv7LqrFtG0Jl6hUFnNn164cGGU1ZoZbPdRE0c23njjoj+jNQeGDBkSZdo9\nB3Lma9JxS6q/D7k16Vo7VhbrALVVl5Rf/vKXQH5B1g033DCONRGpX79+UXbrrbcCuXoFUH3z3uIa\n33FSSN1rfP1FTapd1hTF1iVL+ozW3oNcAoXWO6s2Bx10UBzbWm3NYY+VRpPZCDW7VPnMM88AuWov\nkFvC6tOnT5R9/PHHcaya74UXXoiyWquw06tXrzjW1GWrnbWyji7BAfTt2zeOk+oBHnDAAUB+Mo/V\n/tW2gFzjO04K8RvfcVJI3XfS0cKE1pS0Dq2kNf1ic97tHN9++20AHn300ShTZ09LykSXAzU1778/\nF0e19957A6XN79fjYc1g/f6m8uQ1GtAWKb3hhhuA/Io01cTWJnj66acB6N+/f5TpNWT3e/bs2XGs\nVZ5shaMbb7wRyE8Ss+3FL730UqA87dC9aabjOIn4je84KaTuTX3F1h/fdttt41jX7K35ryambYqp\nob8WW8hQvfnWy62vW5O1Gh5rNUV1vRlyed623JM95uqBtnn5+tjUmlWPQtiikQMGDABqZzXEosfS\nJtzotaGlxwCWLl3a6LP2cUc7B9nSZfaRQvd9zJgxUaaPkbY3Q2uuJzf1HcdJpN1o/AbbiWPVaDay\nT4tOPvDAA1FmEywU68g75JBDgHwroFYisVRDW6tFy3zb5COrpT78MJNJbdffDzvsMCC/KKe1pJIs\nAY3ms6/ZY63nwq5xa2Tg888/X2DPaouWOEr1vSNG5FJdbr755jhWy0Kdn5BLNLvnnnuirDX3iWt8\nx3ES8RvfcVJI3YfsJmHNI825t+v5u+66KwA9evRI/LyuxV944YVRpiZ+rZj3Fp2TdZZprYB//vOf\nUTZw4MA43nHHHQEYPHhwlKnZqevskN84VCsW2VgFdT6pw67hZ7QGgH0UsA7HekArMv3xj3+MMtu7\nQR8ZdW0ecuvzQ4cObSSDXIFO+/pHH31Uymk3i2t8x0kh7VLjJ2Hr6B144IGNZNZK0DLHtZpSWQyq\niW1XHLsUqVaPbWmtjtCrrroqymxEpB4Dq721w5B13hVaDixljcJyYTsnabnxjTbaKMps9SZ1Vtr0\nXl0OtEu96jwFeOSRR4DqJSwV00mnl4g8LiJTReQtETkxK/duOo5TpxRj6n8DnBJC6A8MBo4Tkf54\nNx3HqVuKqbm3AFiQHX8
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc4e4e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 3910... Discriminator Loss: 0.7256... Generator Loss: 1.6172\n",
      "Epoch 2/2-Step 3920... Discriminator Loss: 1.0606... Generator Loss: 1.3935\n",
      "Epoch 2/2-Step 3930... Discriminator Loss: 0.6840... Generator Loss: 1.4029\n",
      "Epoch 2/2-Step 3940... Discriminator Loss: 1.5648... Generator Loss: 0.4768\n",
      "Epoch 2/2-Step 3950... Discriminator Loss: 1.8569... Generator Loss: 1.3834\n",
      "Epoch 2/2-Step 3960... Discriminator Loss: 1.1159... Generator Loss: 3.2792\n",
      "Epoch 2/2-Step 3970... Discriminator Loss: 0.6805... Generator Loss: 2.0604\n",
      "Epoch 2/2-Step 3980... Discriminator Loss: 1.4394... Generator Loss: 0.6645\n",
      "Epoch 2/2-Step 3990... Discriminator Loss: 0.6373... Generator Loss: 2.3259\n",
      "Epoch 2/2-Step 4000... Discriminator Loss: 1.8258... Generator Loss: 1.6766\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nScMj99SRog4RWxa7o2i
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7f93d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4010... Discriminator Loss: 2.5978... Generator Loss: 4.1080\n",
      "Epoch 2/2-Step 4020... Discriminator Loss: 0.8558... Generator Loss: 1.7508\n",
      "Epoch 2/2-Step 4030... Discriminator Loss: 1.1174... Generator Loss: 1.3364\n",
      "Epoch 2/2-Step 4040... Discriminator Loss: 0.5710... Generator Loss: 2.5279\n",
      "Epoch 2/2-Step 4050... Discriminator Loss: 0.5763... Generator Loss: 2.4129\n",
      "Epoch 2/2-Step 4060... Discriminator Loss: 0.6498... Generator Loss: 3.9780\n",
      "Epoch 2/2-Step 4070... Discriminator Loss: 0.8375... Generator Loss: 1.0327\n",
      "Epoch 2/2-Step 4080... Discriminator Loss: 0.7236... Generator Loss: 2.0829\n",
      "Epoch 2/2-Step 4090... Discriminator Loss: 1.0059... Generator Loss: 1.5619\n",
      "Epoch 2/2-Step 4100... Discriminator Loss: 2.6371... Generator Loss: 0.4754\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUWf7611yySVAsptOoSW
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc30c2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4110... Discriminator Loss: 0.8128... Generator Loss: 2.5129\n",
      "Epoch 2/2-Step 4120... Discriminator Loss: 0.8923... Generator Loss: 1.5166\n",
      "Epoch 2/2-Step 4130... Discriminator Loss: 0.8151... Generator Loss: 2.2795\n",
      "Epoch 2/2-Step 4140... Discriminator Loss: 1.4754... Generator Loss: 4.0093\n",
      "Epoch 2/2-Step 4150... Discriminator Loss: 0.8900... Generator Loss: 0.7977\n",
      "Epoch 2/2-Step 4160... Discriminator Loss: 0.8838... Generator Loss: 2.2693\n",
      "Epoch 2/2-Step 4170... Discriminator Loss: 1.5833... Generator Loss: 2.0589\n",
      "Epoch 2/2-Step 4180... Discriminator Loss: 1.2953... Generator Loss: 2.0012\n",
      "Epoch 2/2-Step 4190... Discriminator Loss: 1.0730... Generator Loss: 1.3989\n",
      "Epoch 2/2-Step 4200... Discriminator Loss: 1.3883... Generator Loss: 2.0992\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcc58f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4210... Discriminator Loss: 1.0157... Generator Loss: 4.1873\n",
      "Epoch 2/2-Step 4220... Discriminator Loss: 0.8365... Generator Loss: 1.9523\n",
      "Epoch 2/2-Step 4230... Discriminator Loss: 0.6131... Generator Loss: 2.5284\n",
      "Epoch 2/2-Step 4240... Discriminator Loss: 1.1748... Generator Loss: 1.3751\n",
      "Epoch 2/2-Step 4250... Discriminator Loss: 0.6887... Generator Loss: 2.5840\n",
      "Epoch 2/2-Step 4260... Discriminator Loss: 0.5632... Generator Loss: 3.1034\n",
      "Epoch 2/2-Step 4270... Discriminator Loss: 0.7161... Generator Loss: 2.7014\n",
      "Epoch 2/2-Step 4280... Discriminator Loss: 0.9079... Generator Loss: 3.0261\n",
      "Epoch 2/2-Step 4290... Discriminator Loss: 0.8594... Generator Loss: 2.3147\n",
      "Epoch 2/2-Step 4300... Discriminator Loss: 0.7226... Generator Loss: 2.9783\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nsUZQL7GGeNo947bm0uz
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc8162b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4310... Discriminator Loss: 0.7103... Generator Loss: 1.5627\n",
      "Epoch 2/2-Step 4320... Discriminator Loss: 0.8324... Generator Loss: 1.3744\n",
      "Epoch 2/2-Step 4330... Discriminator Loss: 1.1705... Generator Loss: 0.9826\n",
      "Epoch 2/2-Step 4340... Discriminator Loss: 1.2036... Generator Loss: 3.3336\n",
      "Epoch 2/2-Step 4350... Discriminator Loss: 0.5394... Generator Loss: 1.9205\n",
      "Epoch 2/2-Step 4360... Discriminator Loss: 1.0280... Generator Loss: 1.7976\n",
      "Epoch 2/2-Step 4370... Discriminator Loss: 1.0513... Generator Loss: 1.0484\n",
      "Epoch 2/2-Step 4380... Discriminator Loss: 0.6092... Generator Loss: 1.4064\n",
      "Epoch 2/2-Step 4390... Discriminator Loss: 0.7810... Generator Loss: 2.4568\n",
      "Epoch 2/2-Step 4400... Discriminator Loss: 0.7146... Generator Loss: 1.3537\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBvILR7SW3U6Z0VpzgB/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc5af6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4410... Discriminator Loss: 1.7961... Generator Loss: 1.2303\n",
      "Epoch 2/2-Step 4420... Discriminator Loss: 1.0087... Generator Loss: 2.6644\n",
      "Epoch 2/2-Step 4430... Discriminator Loss: 1.1209... Generator Loss: 2.0010\n",
      "Epoch 2/2-Step 4440... Discriminator Loss: 1.7087... Generator Loss: 3.2714\n",
      "Epoch 2/2-Step 4450... Discriminator Loss: 0.8297... Generator Loss: 1.1483\n",
      "Epoch 2/2-Step 4460... Discriminator Loss: 0.9406... Generator Loss: 3.2302\n",
      "Epoch 2/2-Step 4470... Discriminator Loss: 1.4158... Generator Loss: 0.9934\n",
      "Epoch 2/2-Step 4480... Discriminator Loss: 0.6997... Generator Loss: 2.7613\n",
      "Epoch 2/2-Step 4490... Discriminator Loss: 1.0477... Generator Loss: 2.3829\n",
      "Epoch 2/2-Step 4500... Discriminator Loss: 0.9892... Generator Loss: 1.7170\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN+RKsMsSHeSW+CB3vHo
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcf4c780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4510... Discriminator Loss: 1.6809... Generator Loss: 4.9071\n",
      "Epoch 2/2-Step 4520... Discriminator Loss: 0.8174... Generator Loss: 1.7120\n",
      "Epoch 2/2-Step 4530... Discriminator Loss: 0.5889... Generator Loss: 3.8659\n",
      "Epoch 2/2-Step 4540... Discriminator Loss: 0.6366... Generator Loss: 1.8449\n",
      "Epoch 2/2-Step 4550... Discriminator Loss: 0.6644... Generator Loss: 3.2493\n",
      "Epoch 2/2-Step 4560... Discriminator Loss: 0.7319... Generator Loss: 3.4059\n",
      "Epoch 2/2-Step 4570... Discriminator Loss: 1.6027... Generator Loss: 0.6539\n",
      "Epoch 2/2-Step 4580... Discriminator Loss: 0.8634... Generator Loss: 1.0770\n",
      "Epoch 2/2-Step 4590... Discriminator Loss: 0.8624... Generator Loss: 1.3737\n",
      "Epoch 2/2-Step 4600... Discriminator Loss: 0.5867... Generator Loss: 1.8046\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd1dd208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4610... Discriminator Loss: 1.1267... Generator Loss: 2.4996\n",
      "Epoch 2/2-Step 4620... Discriminator Loss: 0.7398... Generator Loss: 3.9589\n",
      "Epoch 2/2-Step 4630... Discriminator Loss: 1.1008... Generator Loss: 3.0764\n",
      "Epoch 2/2-Step 4640... Discriminator Loss: 1.9842... Generator Loss: 0.8261\n",
      "Epoch 2/2-Step 4650... Discriminator Loss: 0.6369... Generator Loss: 3.2500\n",
      "Epoch 2/2-Step 4660... Discriminator Loss: 0.8727... Generator Loss: 1.3971\n",
      "Epoch 2/2-Step 4670... Discriminator Loss: 0.5712... Generator Loss: 1.7606\n",
      "Epoch 2/2-Step 4680... Discriminator Loss: 0.9897... Generator Loss: 1.4680\n",
      "Epoch 2/2-Step 4690... Discriminator Loss: 0.7763... Generator Loss: 1.7340\n",
      "Epoch 2/2-Step 4700... Discriminator Loss: 0.7342... Generator Loss: 2.0838\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyC36u0IImomwJK/iU6q
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd1eeb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4710... Discriminator Loss: 1.3577... Generator Loss: 1.7229\n",
      "Epoch 2/2-Step 4720... Discriminator Loss: 0.5806... Generator Loss: 3.3829\n",
      "Epoch 2/2-Step 4730... Discriminator Loss: 0.8749... Generator Loss: 1.0274\n",
      "Epoch 2/2-Step 4740... Discriminator Loss: 1.3508... Generator Loss: 2.4017\n",
      "Epoch 2/2-Step 4750... Discriminator Loss: 0.7190... Generator Loss: 2.2952\n",
      "Epoch 2/2-Step 4760... Discriminator Loss: 0.9876... Generator Loss: 3.0151\n",
      "Epoch 2/2-Step 4770... Discriminator Loss: 2.9583... Generator Loss: 0.4921\n",
      "Epoch 2/2-Step 4780... Discriminator Loss: 0.5907... Generator Loss: 1.9550\n",
      "Epoch 2/2-Step 4790... Discriminator Loss: 0.5692... Generator Loss: 1.6081\n",
      "Epoch 2/2-Step 4800... Discriminator Loss: 1.0259... Generator Loss: 0.6609\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db714b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4810... Discriminator Loss: 2.0318... Generator Loss: 0.4954\n",
      "Epoch 2/2-Step 4820... Discriminator Loss: 1.2075... Generator Loss: 1.6561\n",
      "Epoch 2/2-Step 4830... Discriminator Loss: 0.6910... Generator Loss: 2.5186\n",
      "Epoch 2/2-Step 4840... Discriminator Loss: 0.6184... Generator Loss: 2.7840\n",
      "Epoch 2/2-Step 4850... Discriminator Loss: 0.8373... Generator Loss: 2.0886\n",
      "Epoch 2/2-Step 4860... Discriminator Loss: 0.6483... Generator Loss: 1.9491\n",
      "Epoch 2/2-Step 4870... Discriminator Loss: 0.7256... Generator Loss: 2.9973\n",
      "Epoch 2/2-Step 4880... Discriminator Loss: 0.6956... Generator Loss: 2.6425\n",
      "Epoch 2/2-Step 4890... Discriminator Loss: 1.1275... Generator Loss: 4.1734\n",
      "Epoch 2/2-Step 4900... Discriminator Loss: 1.4497... Generator Loss: 1.6510\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFwI5bTch6/hKQ3JN3nB
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6bb66a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 4910... Discriminator Loss: 1.2242... Generator Loss: 1.9881\n",
      "Epoch 2/2-Step 4920... Discriminator Loss: 1.4209... Generator Loss: 1.2964\n",
      "Epoch 2/2-Step 4930... Discriminator Loss: 1.1311... Generator Loss: 1.5902\n",
      "Epoch 2/2-Step 4940... Discriminator Loss: 0.7357... Generator Loss: 3.3282\n",
      "Epoch 2/2-Step 4950... Discriminator Loss: 0.6916... Generator Loss: 2.8701\n",
      "Epoch 2/2-Step 4960... Discriminator Loss: 0.6660... Generator Loss: 3.8739\n",
      "Epoch 2/2-Step 4970... Discriminator Loss: 0.6230... Generator Loss: 2.3669\n",
      "Epoch 2/2-Step 4980... Discriminator Loss: 1.0523... Generator Loss: 2.5116\n",
      "Epoch 2/2-Step 4990... Discriminator Loss: 0.9139... Generator Loss: 1.0435\n",
      "Epoch 2/2-Step 5000... Discriminator Loss: 0.8021... Generator Loss: 3.0711\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEbAo2/6Hc2460J4qVdO
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6a64e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5010... Discriminator Loss: 0.6576... Generator Loss: 2.1492\n",
      "Epoch 2/2-Step 5020... Discriminator Loss: 0.7555... Generator Loss: 2.1061\n",
      "Epoch 2/2-Step 5030... Discriminator Loss: 1.2018... Generator Loss: 1.1961\n",
      "Epoch 2/2-Step 5040... Discriminator Loss: 0.5098... Generator Loss: 2.7158\n",
      "Epoch 2/2-Step 5050... Discriminator Loss: 0.8177... Generator Loss: 2.4968\n",
      "Epoch 2/2-Step 5060... Discriminator Loss: 0.7574... Generator Loss: 3.0226\n",
      "Epoch 2/2-Step 5070... Discriminator Loss: 0.6693... Generator Loss: 1.8254\n",
      "Epoch 2/2-Step 5080... Discriminator Loss: 0.8775... Generator Loss: 1.7649\n",
      "Epoch 2/2-Step 5090... Discriminator Loss: 1.0300... Generator Loss: 2.4597\n",
      "Epoch 2/2-Step 5100... Discriminator Loss: 0.6733... Generator Loss: 2.5881\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n8cYbzf6OemHmzJlxrI4
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd33a588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5110... Discriminator Loss: 0.7277... Generator Loss: 3.5191\n",
      "Epoch 2/2-Step 5120... Discriminator Loss: 0.7121... Generator Loss: 2.9921\n",
      "Epoch 2/2-Step 5130... Discriminator Loss: 0.6108... Generator Loss: 2.8381\n",
      "Epoch 2/2-Step 5140... Discriminator Loss: 0.8490... Generator Loss: 2.5457\n",
      "Epoch 2/2-Step 5150... Discriminator Loss: 0.5292... Generator Loss: 3.2059\n",
      "Epoch 2/2-Step 5160... Discriminator Loss: 0.5909... Generator Loss: 1.5392\n",
      "Epoch 2/2-Step 5170... Discriminator Loss: 0.8997... Generator Loss: 1.5416\n",
      "Epoch 2/2-Step 5180... Discriminator Loss: 0.5973... Generator Loss: 2.8303\n",
      "Epoch 2/2-Step 5190... Discriminator Loss: 1.2312... Generator Loss: 1.7203\n",
      "Epoch 2/2-Step 5200... Discriminator Loss: 0.9359... Generator Loss: 1.5172\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbVtmO0voPjuUAkIa8zZ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6683d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5210... Discriminator Loss: 0.6013... Generator Loss: 2.7446\n",
      "Epoch 2/2-Step 5220... Discriminator Loss: 1.5900... Generator Loss: 1.7314\n",
      "Epoch 2/2-Step 5230... Discriminator Loss: 1.3293... Generator Loss: 0.9577\n",
      "Epoch 2/2-Step 5240... Discriminator Loss: 0.7651... Generator Loss: 4.5080\n",
      "Epoch 2/2-Step 5250... Discriminator Loss: 0.8763... Generator Loss: 2.2820\n",
      "Epoch 2/2-Step 5260... Discriminator Loss: 0.8644... Generator Loss: 1.7405\n",
      "Epoch 2/2-Step 5270... Discriminator Loss: 1.2652... Generator Loss: 1.4441\n",
      "Epoch 2/2-Step 5280... Discriminator Loss: 0.5929... Generator Loss: 1.8398\n",
      "Epoch 2/2-Step 5290... Discriminator Loss: 0.5260... Generator Loss: 3.4115\n",
      "Epoch 2/2-Step 5300... Discriminator Loss: 0.9375... Generator Loss: 1.7354\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXeUVFXSwH9XEVkVJaiIgKKAKGJCMGFWFFHBsLhgRl0M\niBiOcQ2Ys5hRBBXXgPqJYRExgIqRFTARBBGQsAQDmDeo9/uju27Xc3qYnplOb179zuFQU939+qXb\nt17dCs57j2EYyWK1Uu+AYRjFxwa+YSQQG/iGkUBs4BtGArGBbxgJxAa+YSQQG/iGkUBqNfCdc92d\nc7Occ3Occxfla6cMwygsrqYBPM651YHZQDdgEfAB0Nd7PyN/u2cYRiGoV4vP7gTM8d7PBXDOjQJ6\nAZUOfOechQkaRgFwzgHgvcd776p6f21M/RbAQvX3orTOqAWrrbYaq61mrpfKcM6Ff0bmfDRo0IAG\nDRrkfF5qM+PnhHOuP9C/0N9jGEbu1GbgLwZaqb9bpnURvPfDgGFgpn4u/P7776XehbLGksqiyPn4\n5ZdfqvW52tiUHwDtnHObOefqA32AF2qxPcMwikSNZ3zv/a/OuTOBl4HVgQe999PztmeGYRSMGi/n\n1ejLzNQ3jIJTaK++YRgxpeBe/Xxy4IEHAnDLLbcE3XvvvQfA4MGDg27JkiVBNmfQqqlXL3ML9OjR\nA4BDDz006Nq2bRvkRo0aATBr1qygO+mkkwD4+eefC7qfRn6xGd8wEkisZvwmTZoA0Vloq622AuCw\nww4Luk022STI//73v4u0d/FijTXWAOCaa64JujPOOAOAtdZaK+tnxHr63//+F3QSMKIDR+rXrx/k\n5s2bA/DNN98E3Q8//FCrfTdqj834hpFAbOAbRgKJlan/xBNPVNCNHDkSgIYNGwadxbpXzamnngrA\nmWeeGXQNGjQA4Lfffgu6uXPnBvnYY48FYOrUqUEnkYba1N9mm22C/OqrrwLw/PPPB92JJ55Y6/03\naoeNEMNIIDbwDSOBxMrUF0aNGhXkvfbaC4Ajjjgi6LbYYosgf/TRR8XbsTJHe9svueQSANZcc82g\nkxWQyy+/POjuvffeIK8qEUTHS7Rr1y7I66yzDhA/T75+dGnTpg0QjW/48ccfgzxjRqoExeeffx50\nK1asAODXX38NunKKKbEZ3zASSOxj9ddbbz0gE8EHUUdf3759AXj77bfz/dWxQ84VwL/+9S8AVl99\n9aDr3r07AG+88Ua1t623M27cuCCLRSZRlwCvv/56tbdfLGSm32233YJu7NixQMZ6qQw9liTWQZ/L\nI488MsiFjHS0WH3DMLJiA98wEkjsTX1JMlmwYEHQNWvWLMhff/01AOeee27Q/fTTT0AmnBTg22+/\nDbKsPa9cuTLo6kJlHO3IE1Nf/gfYdtttgZo5oXQY9fvvvx9keQRo37590C1fvrza2y8Wcu988skn\nQbfuuusC0fBvfY7+9Kc/AZkwaMjEkuj33XjjjUEW52ohMFPfMIysxHI5TyMOK0nggeivrDiStDOl\nZ8+eAHTr1i3omjZtWmHbS5cuDfKtt94KwN///veg+/7772u178Xmv//9b5Dvu+8+ABYtWhR0NZnp\nxYoYNGhQ0K299tpBnjRpEgDfffddtbddLGTGBnj88ceBaKKSpIGPGDEi6PTxyHndYIMNgm7ChAkA\nbLrppkF33HHHBfnSSy8FSmdJVjnjO+cedM4td85NU7omzrlXnXOfp/9vXNjdNAwjn+Ri6j8MdP+D\n7iJgvPe+HTA+/bdhGDEhJ+eec641MMZ73zH99yxgb+/9Eudcc+AN7337VWxCtpMX555OwhET86ab\nbgo6bb7uvPPOQNR5J8esTdJ77rknyL179wai1WnEJFu2bFmF92lnVlyQKD5t0mpn5qrQ53/33XcH\n4B//+EfW18877zwAhg8fHnTl4CjVkXlSRQjg7rvvBqLOvQMOOACIRuvpRKZsyCOorlYkFYwAunbt\nCsCUKVOqve9VUUjnXjPvvdS3Wgo0W9WbDcMoL2rt3PPe+1XN5NZJxzDKj5oO/GXOuebK1K90YbYQ\nnXS0eXr66acD0WQIXU4qW7KEoL3yAwYMCPL8+fMB6NChQ9BJ6KmOERgzZgwAW2+9ddDpR4FyRkJK\n9WqHbryYDTHhN9poo6CTRyTJ5QeYNi34gZk4cSJQHua9Rj/GnX/++UGWuANZ9YBMglF1jkG8/nrt\nXst77rknUBhTPxdqauq/AJyQlk8Anl/Few3DKDOqnPGdc08AewPrO+cWAVcANwBPOedOBr4Ejirk\nTv4RnXYr0XeLF2fa9r3wQqaTV7aZXtAOnk6dOgVZ1nX1L7RE81177bVBJ0kb2jl0ww03BLmc0jD/\niKy/r7/++kEnM5qe2fTr2223HQD9+vULutatWwOZCEmIngMdUVlOaOecTjAStFUjlk51Zny5t/be\ne+8KOoA333wz520VgioHvve+byUv7ZfnfTEMo0hYyK5hJJBYJemIqTRs2LCgO/744yvozjnnnCBn\nM/XFdGvRokXQadNLnDk6J1sSNPbZZ5+ge/bZZ4FoKKzOOxfHTSlNfr2mrotgSuHMjTfeOOgkGUU/\nSokpD9lNYjGZP/vss6A75phjgizVacrNuacZOHBgkG+77TYgur8vvvgiEE300olGcv31dZbw3Y8/\n/jjo9Plr2bIlUJi+D5akYxhGVmI148vstXDhwqDbcMMNAejcuXPQ6V9Z+YyuniKOvCFDhgSdnuVk\n6W7y5MkV9kH/aksCyvbbbx90usuMJAOJY7CYiINSV4CRrkOQce5ph5Ocq8rKk8u9ou8Z+bw+br00\n+uCDD1b4TLmhj/fhhx8G4Oijjw46OUa99KnTmaUEudyLkLGU9H33yiuvBFlqROrzli9sxjcMIys2\n8A0jgcQqH19MLp33LE6Y6dOnV3gfwB577AFEo/mkGoyONjvqqEwoQjYTX9Drv3369AGijxa6yk3H\njh0BeO2114KukCavPm4pK64r4+jXs+2PHJtucPnOO+8EWRJx5LggY9br6jOy3h8XtCNPnMW6xLiY\n/1KhCKJ59ptvvnmFbcq51PeLdoBWleRTaGzGN4wEYgPfMBJIrEz9bCGlkiOta8afffbZQb7ggguA\nqEm7ZEkqo1iv08+ZMyenfdAmrax76zxtLUuiR1XmvTbBa/MoII0wIdP9RaM74Ugyka4lcP/991fQ\nZfM6y3FDxvutPdr6M+XszV8VkqgFmdgNXb9B59aLB1+X8DrrrLOAzKMmZOIBoPRxDTbjG0YCidWM\nL7OHbt0sjhXtONGz8syZMwG46KJMdTBZ264qakqv755wQioZUUcFygygq7WIww9W3Wsun4jFoJOK\nZN/1MepEJOnzVpPEE21diYNUR0i+9dZbOW8zDsh9p605KdEOGQsyW0KTtngk7qMcsBnfMBKIDXzD\nSCCxMvWFU045JchSN1+btJtttlmQtXm2KrSDTdZodYhlq1atgGg9dcnNHzp0aNCtKv+/MmrrAJMK\nQNmaOur4htmzZ9fqO6X3wNNPPx108riji5l++OGH1d523NDnTx6revToEXRSqWnq1KlBV4iEnJpi\nM75hJJBYzvi6JbakVD700ENBl2vig57ldVedxx57DIjO3uIcfOCBB4KukK2Oq4O0t84WmadnGZ1g\nlGtlIh0lefPNNwPZI/N0IpJOYEkC4uDUyUlyDsu1IlMunXRaOeded87NcM5Nd84NSuutm45hxJRc\nTP1fgfO89x2AXYABzrkOWDcdw4gt1c7Hd849D9yd/letbjqFaJNdE8Tk1WWVtZk2evRoAP72t78F\nXa5OwlIgSTM6WUhMTb3fjz76aJCfe+45IBqDII8FuurO1VdfHeT99kuVWdRxEtJ9p1evXkH37rvv\n1vBI4olUMZJqQ5C5x3SVp2I1Wc0lH79az/jpVlo7AJPIsZuONdQwjPIj5xnfObcO8CZwrfd+tHNu\npfe+kXp9hfd+lc/5pZj
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6114630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5310... Discriminator Loss: 0.9733... Generator Loss: 1.5390\n",
      "Epoch 2/2-Step 5320... Discriminator Loss: 0.7806... Generator Loss: 2.0623\n",
      "Epoch 2/2-Step 5330... Discriminator Loss: 0.4897... Generator Loss: 2.3890\n",
      "Epoch 2/2-Step 5340... Discriminator Loss: 1.0056... Generator Loss: 2.0729\n",
      "Epoch 2/2-Step 5350... Discriminator Loss: 0.6536... Generator Loss: 1.9443\n",
      "Epoch 2/2-Step 5360... Discriminator Loss: 0.7212... Generator Loss: 2.3086\n",
      "Epoch 2/2-Step 5370... Discriminator Loss: 0.7070... Generator Loss: 3.1036\n",
      "Epoch 2/2-Step 5380... Discriminator Loss: 1.1220... Generator Loss: 1.6468\n",
      "Epoch 2/2-Step 5390... Discriminator Loss: 0.5778... Generator Loss: 3.2888\n",
      "Epoch 2/2-Step 5400... Discriminator Loss: 0.6168... Generator Loss: 2.5259\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db67cfe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5410... Discriminator Loss: 0.5683... Generator Loss: 3.2456\n",
      "Epoch 2/2-Step 5420... Discriminator Loss: 0.7777... Generator Loss: 1.4606\n",
      "Epoch 2/2-Step 5430... Discriminator Loss: 0.6013... Generator Loss: 2.6161\n",
      "Epoch 2/2-Step 5440... Discriminator Loss: 0.8191... Generator Loss: 2.7877\n",
      "Epoch 2/2-Step 5450... Discriminator Loss: 0.6987... Generator Loss: 2.4595\n",
      "Epoch 2/2-Step 5460... Discriminator Loss: 1.5040... Generator Loss: 0.6110\n",
      "Epoch 2/2-Step 5470... Discriminator Loss: 0.8633... Generator Loss: 2.2098\n",
      "Epoch 2/2-Step 5480... Discriminator Loss: 0.9388... Generator Loss: 1.8265\n",
      "Epoch 2/2-Step 5490... Discriminator Loss: 0.7507... Generator Loss: 2.4642\n",
      "Epoch 2/2-Step 5500... Discriminator Loss: 0.5585... Generator Loss: 3.2339\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2DSkn/7yl7+M4z/96U/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6431400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5510... Discriminator Loss: 0.6442... Generator Loss: 3.0003\n",
      "Epoch 2/2-Step 5520... Discriminator Loss: 0.5520... Generator Loss: 2.7205\n",
      "Epoch 2/2-Step 5530... Discriminator Loss: 0.8147... Generator Loss: 1.8698\n",
      "Epoch 2/2-Step 5540... Discriminator Loss: 0.6854... Generator Loss: 1.3011\n",
      "Epoch 2/2-Step 5550... Discriminator Loss: 0.5850... Generator Loss: 3.3359\n",
      "Epoch 2/2-Step 5560... Discriminator Loss: 0.6005... Generator Loss: 2.6258\n",
      "Epoch 2/2-Step 5570... Discriminator Loss: 0.6296... Generator Loss: 2.5284\n",
      "Epoch 2/2-Step 5580... Discriminator Loss: 0.7324... Generator Loss: 4.1761\n",
      "Epoch 2/2-Step 5590... Discriminator Loss: 0.7818... Generator Loss: 2.6275\n",
      "Epoch 2/2-Step 5600... Discriminator Loss: 1.1629... Generator Loss: 0.8184\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n2JRsNZ0QwvLEneT2VRJ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6564eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5610... Discriminator Loss: 0.8138... Generator Loss: 3.4572\n",
      "Epoch 2/2-Step 5620... Discriminator Loss: 1.4815... Generator Loss: 1.7691\n",
      "Epoch 2/2-Step 5630... Discriminator Loss: 0.4693... Generator Loss: 4.3668\n",
      "Epoch 2/2-Step 5640... Discriminator Loss: 0.6629... Generator Loss: 1.5059\n",
      "Epoch 2/2-Step 5650... Discriminator Loss: 0.8070... Generator Loss: 2.4559\n",
      "Epoch 2/2-Step 5660... Discriminator Loss: 0.5123... Generator Loss: 2.6219\n",
      "Epoch 2/2-Step 5670... Discriminator Loss: 1.4344... Generator Loss: 1.9084\n",
      "Epoch 2/2-Step 5680... Discriminator Loss: 0.7615... Generator Loss: 3.0340\n",
      "Epoch 2/2-Step 5690... Discriminator Loss: 1.0736... Generator Loss: 1.6895\n",
      "Epoch 2/2-Step 5700... Discriminator Loss: 0.5061... Generator Loss: 3.1781\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXeYlNX1+D8n9k5REQEFrGhUNGrsBRsqRjSKqD/5GjGW\nGDV2sWCPPULsBmNXQERFVNQgqFGxIFa6HWlqwIIlSu7vj5lz57zuu7uzu7NTds7neXi4e2bmnfe9\nM+/cc0+VEAKO41QXvyr1CTiOU3z8xnecKsRvfMepQvzGd5wqxG98x6lC/MZ3nCrEb3zHqUKadOOL\nSE8RmSYiM0Xk7EKdlOM4zYs0NoBHRJYApgN7ALOA14BDQwiTC3d6juM0B0s24bVbAzNDCB8AiMhQ\nYH+g1htfRIKIAOARg9XLr35VU9H83//+V+dr9HtTG23btgVgueWWi7K5c+cC8NNPPzX0FCuGpZde\nGoBWrVoB8PXXX/P999/XPVk07cbvAHxq/p4F/LauF4gISy6ZecuW/GE0BZ2fn3/+ucRnUliWWGKJ\nOF5++eUBWLx4cZT98MMPcZz2I7DUUksByR8N+/oDDjgAgI033jjKrrrqKgBmz57dpHMvN+yP4Bpr\nrAHkrv+BBx7I6xhNufHzQkSOAY5p7vdxHCd/mnLjfwZ0Mn93zMoShBBuA26DjKrvK33dtLSVXrGr\n8zfffNPg1//3v/8FkpqDXflWWGEFAD766KMoU1W/pWG3yZ9+mlG6n3vuOSD/uW2KVf81YD0R6SIi\nSwN9gVFNOJ7jOEWi0VZ9ABHZBxgELAH8M4RwWT3Pd4ue0yTUBgJJW0Dnzp0B+PHHH6Pss89qKKAt\nlpVWWgmARYsWsXjx4mY17hFCeAJ4oinHcByn+HjknuNUIU1S9Rv8Zq7qO03EGveswbDa40N0XhYv\nXkwIoV5V31d8x6lCmt2P31LQX9TVVlstyjbYYAMA3nnnnSj7z3/+U9wTqzJqW9GrdaVX6ot8/CW+\n4jtOFeI3vuNUIa7qp7DssssCcMQRR0TZOeecA0D79u2jTNV/G2c+alQuhql///41Hq80bGz8dttt\nB8DAgQNrPO/BBx+M4zvuuCOOCx2JWEqV3kYKWiOjYtXthqreTaWh8+IrvuNUIX7jO04VUtV+fKuu\nXXjhhXF8+umnA7DMMsvU+XqdOzuHVrV9+OGHATj88MOjrNgqYEPQ+dhvv/2i7Pbbb49jzflOy423\nc/D444/Hce/evYHyvu527drF8d577w3Ab37zmyjbcMMNAejatWuUaZow5OZt5syZUXbnnXcCMGzY\nsCj7/vvv47g57zv34zuOk0pVrvhqvBszZkyU7bjjjnGsK5pdpRYtWgTAlClTokxTISdMmBBl06ZN\ni2NNEf3uu+8KdeoFZ8UVV4zjW2+9FYA+ffpEWVrhi3nz5kXZyiuvXOM4OleQKxRRLnOw6qqrAvDk\nk09GmS3eoSu51Wp0XF8VIIvOlU0TPumkk+J43LhxQPMYfn3FdxwnFb/xHacKqRpV3xryRo4cCcA+\n++yT+txPPvkEgPPOOy/KRo8eDSQNNKrOVWK4qBouhwwZEmV9+/YFkuq9VUXPPfdcIGnw23777QF4\n9NFHU99HDWKlzI23RtovvvgCyFXsgeTnp9f7wQcf1DiO3fp99dVXcbzKKqsAsPbaa0eZbn3s9sBu\ngY4//ngAhg4dGmU26agpuKrvOE4qLT5yT1evU089Ncp0pdc6bgDDhw+PYzXCNKY2XDljq9ccd9xx\nABxyyCFRpnNlq9joygRw3333AcmVT1dG+xo1nkLOmFYuK749tzTUrXv99ddHma7EtUXm6bytvvrq\nUfb0008DsNFGG0WZVhcG2GmnnYD8q+IWmnpXfBH5p4jMF5F3jayNiDwjIjOy/7du3tN0HKeQ5KPq\n3wn0/IXsbGBsCGE9YGz2b8dxKoR6Vf0QwvMi0vkX4v2BXbLju4DxwFkFPK+CoarWZZfl6oCqMeeE\nE06IMlVjoeU2+1h33XXjWA2X1uip6rqNNNTow9pQlbeUCSr18fXXX8fx5ZdfDsCAAQOizBr3vv32\nWyD5HajP6KaPq+EQcvEcVtW376PG4lLNVWONe+1CCHOy47lAu7qe7DhOedFk414IIdTlpvNOOo5T\nfuTlx8+q+qNDCL/O/j0N2CWEMEdE2gPjQwgb5HGcoji8O3ToEMfvvfcekKs7DvDSSy8ByWQU65et\nRL98bdgmko899lgc77zzzkAyqeiaa64Bkvn29c2F+vFt+LNlk002AZKhq+VAmzZt4vjll1+OY22+\n+cgjj0SZbgmt58KivvpOnXKNpd5++20g+b3T+BDIzYtuLQpJc/rxRwH/lx3/H5AeveE4TllS74ov\nIg+QMeStCswDLgAeAYYDawEfA31CCPVWmWyOFV9907///e+j7B//+Ecca4SWNaJMnToVyK38kKwa\nM3lyptO3/TUuN4NVvuyxxx5x/NBDD8Wx+rNtCq369G18Qxo2HuC2224DktWKPv/88zheZ511gGTE\nY7lhffvab88mHc2ZkzFnnXnmmVH21FNPxbFqVWo4BDjssMOApJFQU34Bxo8fX4hTTyWfFT8fq/6h\ntTy0W4PPyHGcssBDdh2nCqnIJB1VHyGXcGNzqm2SiV5fWoilxfpq1dCnvlaAG2+8EUhWWdGkC2sg\nKxfDoF6jVUl33XXXONZr3HTTTaOsvrBaNWLZ6jTPP/88AEsvvXSU2TDUfv36AeUzL/Wh1XbUOAfJ\nLjWKqv8As2bNAmCttdaKMk3cOeusXHiLbot+eaxC40k6juOkUlErvrrpbOca/WW16Y/WoKKJNvYX\nVqus2OdZY44ae+wxdVW3BsGjjjoKSLqqysUIqNc4f/78KLOupSuvvBKAiy++OMr0Gmv7TmgSyhtv\nvBFlWq/Opu+qiw/gzTffbNwFlAj9zCdNmhRl6nqrrQKPzpedg2uvvRZI1nIs1nfDV3zHcVLxG99x\nqpCyz8e3hjiNDlP1HnLq04wZM6LM5lKr+mUfV/XT+pbt+7RunckyVoMeQK9evQDYZpttokz93ldc\ncUXDLqoIqEHKqqc28kzH1resTUDt9sBer/qmbfUa3ULZObdbsUpD1fYePXpEmfrnDz744Ciz30Gd\nY5v3X19p9lLjK77jVCF+4ztOFVL2Vn2bQ/7uu5kiQDZk9IUXXgDgoIMOirKFCxfWOI61qOZ7zTZX\nXYsiHnDAAVGmFn4to1RO6LlreDIkk0hURbf+97T4hjRLtrVeH3nkkQCMGDEiyprDeq3nUYp4AP2+\n2S3OaaedFsdnn3124nmQ20ZuvfXWUaah4OCddBzHKQFlb9yzPmH9RbX+d02Z/M9/cjlChfo1tSuX\nJlXsv//+NWTliK7oZ5xxRpTZJBLFGjh1Djt37hxldqxo+2/Itcdu7pVYNZhCt93OB31Pm7pt/fMa\nA2I75WjizosvvhhlGsUI8MQTTySO3VQaqhH5iu84VYjf+I5ThZS9ce+vf/1rHGs+9OzZs6NsvfXW\nA2qvjtIYVK3s1q1blGneutaJh5zfW5M0yhFrsLNhyTpfaaqmxixALgkKclssrVIDye4wzYleR7mE\nRNt51UQn25tBOwilJYxBzlC97777RpkmSTXmnrSqvhv3HMdJpeyNex07dqwhe+utt+K4KemN1lVl\nu5zsvvvuANx0001Rpqvcq6++GmWl7A6TL3aFtGWm09DVac8994yytMg/6wIs1oqv12HPp1DaqjVg\nqgFvwYIFNZ5nV29by1Bbp++www5RptGctlS5Jk5BLvHHuvjUVWyNxvl+vxs6F/l00ukkIuNEZLKI\nvCciJ2fl3k3HcSqUfFT9n4HTQggbAdsAJ4jIRng3HcepWPKpuTcHmJMdfyMiU4AOFKmbjlXBVZ2x\nKteaa64JJNVY6+fXsVWFNN/edjmxLbF32WUXIKnSahFGW9SzUqrK5Iuqor179059XD+L1157Lco0\nctJ2IrJVivRz+e6776L
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db61b0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5710... Discriminator Loss: 0.5405... Generator Loss: 3.1697\n",
      "Epoch 2/2-Step 5720... Discriminator Loss: 0.9135... Generator Loss: 3.4455\n",
      "Epoch 2/2-Step 5730... Discriminator Loss: 0.7770... Generator Loss: 2.7214\n",
      "Epoch 2/2-Step 5740... Discriminator Loss: 0.6653... Generator Loss: 1.8489\n",
      "Epoch 2/2-Step 5750... Discriminator Loss: 1.6680... Generator Loss: 1.4212\n",
      "Epoch 2/2-Step 5760... Discriminator Loss: 0.8880... Generator Loss: 1.8065\n",
      "Epoch 2/2-Step 5770... Discriminator Loss: 0.5183... Generator Loss: 2.7174\n",
      "Epoch 2/2-Step 5780... Discriminator Loss: 0.6663... Generator Loss: 3.2934\n",
      "Epoch 2/2-Step 5790... Discriminator Loss: 0.7025... Generator Loss: 2.9029\n",
      "Epoch 2/2-Step 5800... Discriminator Loss: 0.7466... Generator Loss: 2.9246\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n1fQfq7r98pe/jO2f//z
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db62e0278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5810... Discriminator Loss: 0.6765... Generator Loss: 1.8345\n",
      "Epoch 2/2-Step 5820... Discriminator Loss: 0.7103... Generator Loss: 1.7287\n",
      "Epoch 2/2-Step 5830... Discriminator Loss: 0.5158... Generator Loss: 2.4167\n",
      "Epoch 2/2-Step 5840... Discriminator Loss: 0.8594... Generator Loss: 4.9640\n",
      "Epoch 2/2-Step 5850... Discriminator Loss: 0.6634... Generator Loss: 1.9108\n",
      "Epoch 2/2-Step 5860... Discriminator Loss: 0.4578... Generator Loss: 2.7778\n",
      "Epoch 2/2-Step 5870... Discriminator Loss: 0.8115... Generator Loss: 4.1891\n",
      "Epoch 2/2-Step 5880... Discriminator Loss: 0.7035... Generator Loss: 2.7653\n",
      "Epoch 2/2-Step 5890... Discriminator Loss: 0.9100... Generator Loss: 2.3595\n",
      "Epoch 2/2-Step 5900... Discriminator Loss: 1.0375... Generator Loss: 2.1054\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOPjgg6PMViJqrPnw64L
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db62e8358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 5910... Discriminator Loss: 0.7952... Generator Loss: 1.3918\n",
      "Epoch 2/2-Step 5920... Discriminator Loss: 1.4054... Generator Loss: 1.2510\n",
      "Epoch 2/2-Step 5930... Discriminator Loss: 0.7395... Generator Loss: 1.9064\n",
      "Epoch 2/2-Step 5940... Discriminator Loss: 0.4409... Generator Loss: 2.9010\n",
      "Epoch 2/2-Step 5950... Discriminator Loss: 0.9362... Generator Loss: 2.7503\n",
      "Epoch 2/2-Step 5960... Discriminator Loss: 1.0228... Generator Loss: 1.4608\n",
      "Epoch 2/2-Step 5970... Discriminator Loss: 2.3172... Generator Loss: 4.4076\n",
      "Epoch 2/2-Step 5980... Discriminator Loss: 0.7294... Generator Loss: 3.3239\n",
      "Epoch 2/2-Step 5990... Discriminator Loss: 1.6670... Generator Loss: 1.0813\n",
      "Epoch 2/2-Step 6000... Discriminator Loss: 1.1560... Generator Loss: 1.4040\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXe0VNX1+D87NiIWqohCFJAlKlEhauxfRbGgoIm9xxJd\ndmNDjbHEaNRoosYW7C3YC9HYQkT9WbCiiEAEQUFpoqKiJpqc3x8z+8y+vIGZ9960++7+rMXizJ43\nc8+9M2f2vvvsIiEEHMfJFj+o9wQcx6k9vvAdJ4P4wnecDOIL33EyiC98x8kgvvAdJ4P4wnecDNKq\nhS8iO4nIZBGZIiJnVGpSjuNUF2lpAI+ILAX8CxgMzAReBfYLIbxbuek5jlMNlm7FazcBpoQQ3gcQ\nkbuB3YDFLnwRCSICgEcMFsevz5JZaqml4vh///tfk+f9ukEIQUr9TWsW/urADPN4JvDTJb1ARFh2\n2WUB+Pe//92KQ7ctdLEDLL107iP57rvv6jWdhkSv0UorrRRlX331VRz/4Ae5u9b//Oc/UeY/Aoun\nNQu/LETkSODIah/HcZzyac3C/wjoaR73yMsShBBGACMgZ+rbX2Qnh9VMrumLo9foyy+/jLLvv/++\nXtNJPa3x6r8K9BWRXiKyLLAvMKoy03Icp5q0WOOHEL4XkeOAJ4GlgJtDCBPKeF1LD+k4RR16TvNp\n8XZeiw4m4qveaRXqxAP/EVgc5Xj1PXLPcTJI1b36jlNJXMtXBtf4jpNBXOM7jkF9CDZQ6NtvvwXg\nv//9b5RpIBrAcsstl/g7gG+++QZoXGe2a3zHySC+8B0ng/h2nlM2miDTqVOnKOvZMxe8+cUXX0TZ\n/Pnz41jl1kxuZM4//3wAdt999yg7+uijAXj99dejzEZY6u3BGmusEWX77rsvAA888ECUTZo0qQoz\nbopv5zmOUxRf+I6TQVJl6mtqpo3esimtin1ePa4rrLBClK244opAIQUWkqaqJoJYL60mhNh95Eb1\n2LaUZZZZBoDevXtH2XHHHRfH22yzDQDdu3ePMr2+loULF8bxCy+8AMBvfvObKJs4cSLQONfvhz/8\nYRx/+OGHANx7771RduKJJwKlk4Ls9+kvf/kLAP3794+yLbfcMo6rmYzlpr7jOEVpyH18+8t51FFH\nxfHPf/5zAPr06RNlqnHsa+weq2p/+7w6qay1YJ1PqrE+/vjjKHvttdcAeOyxx6LsH//4BwCff/55\nmWdWX+w1WG211QC4/PLLo2yHHXYAkhrQWk+qoUulEa+88spxvPPOOwNJbffLX/4SgFGjGiOZ0zrl\n9Lvxpz/9KcrKdUxai+CVV14Bkk7Cjh07xvHcuXNbNtkK4RrfcTKIL3zHySANaervs88+cXzxxRfH\nsTVBFXW2WaebHauZZmVq4tvCjdYMVlPVmqx9+/YFYLPNNosyrSb06KOPFj12rbG3Luuttx4Ahxxy\nSJSpKQ+F87G3RTp3Ww/R3sa8//77AIwZMybKnnzySSB5W2Sv5YEHHgjASSedFGXnnXcekLxtquc+\n/yabbBLHehtj6/m1xAn52WefAclrYR3Mbuo7jlNzGlLjDx8+PI6XX375ONZf3mnTpkXZHXfcASS1\nrtVSqgU1wgygS5cuTWSDBg1qMm7Xrl2UqWa0jqAhQ4YA8Pjjj0dZPTS+nuOFF14YZcceeyyQPAc7\nN00iUaclFK7hiy++GGWTJ0+O408//bTJ+xTThtby0G0tq/H182mUaD5rSeq5tXZuumVsr0UjpRSX\n1PgicrOIzBWRd4ysk4g8LSLv5f/vuKT3cBynsSjH1L8V2GkR2RnA6BBCX2B0/rHjOCmhpKkfQnhO\nRNZcRLwbsE1+fBswBhhOhbCRYZYJE3K1PLfeeusoW7BgQVnvOXXq1DguFgF4yy23xPGVV14JwAEH\nHNDkNdYZtu666yaeqxeaEHLyySdHmUbhWZNVo+gAfvGLXwAwZ86cKKuUmWuvx8CBA4HkdTvzzDNb\n9f6VRqP1Wot1Fm+11VZA8lrYiMZ601LnXrcQwqz8eDbQrULzcRynBrTauRdCCEuKwfdOOo7TeJSV\npJM39R8NIfTPP54MbBNCmCUi3YExIYS1y3ifsjZEp0+fHsfqHQVYe+3cIT755JNy3qbFdOuWM2Bs\n/rTd01d0P3v77bePslp5bm1yzLx58wBo3759lGmMwc033xxlp5xyShzbBKRKY/P1x40bByTDVTt0\n6AA0jld/9dVXj2MNtd1ll12iTM+hFPb662u6du0aZausskocV7OjVDWTdEYBGhlyCPBIC9/HcZw6\nUNLUF5GR5Bx5XURkJnAucDFwr4gcDnwA7F3JSR1//PFxPH78+DiutqZf9Dj/+te/omzjjTdu8ncz\nZ84E6rM/u9tuu8Wx7kPbiLv9998fgEceKfwmVzMN1kal3XbbbXGsjtrRo0dHWaNoesU6ONUBd/DB\nB0fZW2+9BZS+fmopQkG725iTRuqLWI5Xf7/FPLVdhefiOE6N8JBdx8kgDRmya5M36mFGq0lXan9e\nE1TqgebTQ8FRd88990SZ5rpXw7y38Q9qyp922mlRZsOfdW6///3vKz6PSmHz6C+44AIgGROx6qqr\nAjB79uyir9ew6COOOCLK1KzXkGVonIpD4BrfcTJJQ2r8eiczaNRbjx49mjxnHWg2MajW2Jpw222X\nc7ecffbZUdaaa9i5c+c4tttwmmJqty9/9atfAQWtCElL6aOPPgKSyUL6fCNpQOWvf/0rUKj2BPDS\nSy8B8O6770aZjRjdaKONgKRzT6P4NthggyhrpE6/rvEdJ4P4wnecDNKQpn690cKQmrdv0QKbkCzJ\nXWs0Wg/g5ZdfBlofjadmvUavQdKJqLdA1mQthjVjNXKttRVtaoXGGOy1115RNnLkSAB23HHHKLOR\nd/oa6yTUyEqNpwA4/fTT47ie3x1wje84mcQXvuNkEDf189jyS5dddhmQNGk1lNOGE9fTZLVhr/36\n9QNg6NChUaYlyZqDJtfYegjFinHa/WwNW9binZAM31WT1pb4SgPWbNeCpTfeeGOUaW0IgBtuuKHJ\n6zWOwnr1bZKOm/qO49ScTGt8u9+sFWkA1llnHSDppLr++usB+OCDD2ozuRLYuWmvv3PPPTfK3n77\n7cT/ULowplbLsZaOdcoNGDAASKZNayrq2LFjo8xq/4cffhhIxj+kDXWa/u1vf4uyBx98MI6LnZtG\nANq/0+8VwJQpUyo+z+bgGt9xMogvfMfJIJk29W1VHVvLX03dN954I8rUdGvEPehLLrkESHbKURP7\n+eefjzJbUPS9994Dko44rTqjXWAguXetnXTs7YEe0zYytfEEF110UbPPp1GZMWNGHJe6dZk/fz6Q\n/L7Ya11vXOM7TgbJpMZXjX7OOedEmY1QU4eWdqOB+m+/LAktD7333oVCSOeffz5QaFMNMGzYsDhW\nrfz1119HmW7nWZk9b43cUycfFLa4rBVw9dVXx7FqvrbA4tJyl4S9Lo3UTr2cTjo9ReQZEXlXRCaI\nyIl5uXfTcZyUUo6p/z1wSghhXWBT4FgRWRfvpuM4qaWcmnuzgFn58ZciMhFYnSp306kmatYffvjh\nRZ+/9dZbgfLLKtcbdSC9+eabUabddWzXIZsksuGGGwLF8+2tE8o20NTmoP37948yjeyze/sa+Qj1\nzzuvBHoOtihnKbSWg3Xu2Vbi9aZZ9/j5+voDgLGU2U3HG2o4TuNR9sIXkRWAB4CTQghfWKfFkrrp\nhBBGACPy71G3vTAbjaa98WwDBFu6+9JLLwUarwx0Kax20dwCWxfQlrjWc7dVY3TrzVoJ9hqoxiqm\nue688844LrefYdqwUYyLfP+B5HdMHaC2pHbqeueJyDLkFv1dIQSNQZyT76JD/v+51Zmi4ziVphyv\nvgA3ARNDCH80T3k3HcdJKeWY+lsABwHjRUS9XWdR5W46lcamRNoIN+Xyyy+P4+Y4cRod61yzVWN0\nbKP09thjD6Dg5IOkSau
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5b720b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6010... Discriminator Loss: 1.0264... Generator Loss: 3.1394\n",
      "Epoch 2/2-Step 6020... Discriminator Loss: 0.5920... Generator Loss: 1.7846\n",
      "Epoch 2/2-Step 6030... Discriminator Loss: 0.4545... Generator Loss: 2.5640\n",
      "Epoch 2/2-Step 6040... Discriminator Loss: 0.8280... Generator Loss: 1.3915\n",
      "Epoch 2/2-Step 6050... Discriminator Loss: 0.5016... Generator Loss: 3.8219\n",
      "Epoch 2/2-Step 6060... Discriminator Loss: 0.5244... Generator Loss: 3.0425\n",
      "Epoch 2/2-Step 6070... Discriminator Loss: 0.6403... Generator Loss: 2.9120\n",
      "Epoch 2/2-Step 6080... Discriminator Loss: 0.6300... Generator Loss: 2.6643\n",
      "Epoch 2/2-Step 6090... Discriminator Loss: 0.6518... Generator Loss: 2.3819\n",
      "Epoch 2/2-Step 6100... Discriminator Loss: 0.5038... Generator Loss: 2.9142\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXlR9NU5Z1UuDKqAQoKH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5b72048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6110... Discriminator Loss: 3.3824... Generator Loss: 6.2745\n",
      "Epoch 2/2-Step 6120... Discriminator Loss: 0.5492... Generator Loss: 2.6041\n",
      "Epoch 2/2-Step 6130... Discriminator Loss: 1.5129... Generator Loss: 1.1538\n",
      "Epoch 2/2-Step 6140... Discriminator Loss: 0.5757... Generator Loss: 2.4481\n",
      "Epoch 2/2-Step 6150... Discriminator Loss: 0.7124... Generator Loss: 2.7547\n",
      "Epoch 2/2-Step 6160... Discriminator Loss: 0.9600... Generator Loss: 2.4202\n",
      "Epoch 2/2-Step 6170... Discriminator Loss: 0.5159... Generator Loss: 3.2544\n",
      "Epoch 2/2-Step 6180... Discriminator Loss: 0.5769... Generator Loss: 2.4911\n",
      "Epoch 2/2-Step 6190... Discriminator Loss: 0.7478... Generator Loss: 2.0725\n",
      "Epoch 2/2-Step 6200... Discriminator Loss: 0.8396... Generator Loss: 2.0547\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5b81ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6210... Discriminator Loss: 0.5053... Generator Loss: 2.2907\n",
      "Epoch 2/2-Step 6220... Discriminator Loss: 0.9117... Generator Loss: 1.2082\n",
      "Epoch 2/2-Step 6230... Discriminator Loss: 0.8933... Generator Loss: 1.6245\n",
      "Epoch 2/2-Step 6240... Discriminator Loss: 0.7024... Generator Loss: 2.7995\n",
      "Epoch 2/2-Step 6250... Discriminator Loss: 0.6028... Generator Loss: 2.9859\n",
      "Epoch 2/2-Step 6260... Discriminator Loss: 1.4206... Generator Loss: 1.4163\n",
      "Epoch 2/2-Step 6270... Discriminator Loss: 0.7542... Generator Loss: 2.7978\n",
      "Epoch 2/2-Step 6280... Discriminator Loss: 0.6178... Generator Loss: 3.6148\n",
      "Epoch 2/2-Step 6290... Discriminator Loss: 0.7727... Generator Loss: 2.8502\n",
      "Epoch 2/2-Step 6300... Discriminator Loss: 0.7728... Generator Loss: 1.8395\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXe4VNX1sN8N2CsoTVEBRbGgiKgoVrA3jAUlxEI0RsWI\n0WgkxvLT2KPRxxj9UKPEgILdaLChRuwUFUGkiCAgXWxEI8r+/phZe9Zwz70zc6ed4az3eXjYd907\nZ845c/astddexXnvMQwjWTSp9gkYhlF5bOIbRgKxiW8YCcQmvmEkEJv4hpFAbOIbRgKxiW8YCaSo\nie+cO8w5N9U5N8M5d2mpTsowjPLiGhvA45xrCkwDDgbmAmOBft77j0p3eoZhlINmRbx2D2CG934m\ngHPuYaAPUO/Ed86VJEzQORc5FlauXFmKt6kYTZo0qTPWX8gbbrghAN99912Q/fDDD2Esf1vuKEy5\n12ussUaQ/fTTT1n/G+VHP/PrrrsuAE2bNgVSz8gPP/xQd1KsQjETf3Ngjvp5LrBnzjdslnrLH3/8\nseA3lIuTY0DmIdST/X//+18Yx/WB1JN97bXXDmOZ5PoaevfuDcCkSZOC7LPPPgtjuZcrVqwIslJ9\nCeiHbK211gKgVatWQfbNN98A8NVXXwVZY754o77Ao77g9bHL8UUXdR5xC2tfc801w7hLly4AbLTR\nRgC89dZbeR2jmImfF865s4Czyv0+hmHkTzFr/L2Aq7z3h6Z/Hgzgvb++gdfE66szhmyzzTYAnHHG\nGUE2cOBAAF5//fUgO+ecc8J4zpyU4VWpJU4uTWxUHrECVqxYwcqVK3Oa+sV49ccCnZxzHZxzawIn\nA08XcTzDMCpEo0197/2PzrnzgOeBpsDfvfeTS3ZmCeXcc88F4Mwzzwyy9ddfH4CDDjooyH7+85+H\n8Q033FChs0uhrUTxu2hZOdfecVtvxwXt7M2Hotb43vt/A/8u5hiGYVQei9wzjARSdq++URiPP/44\nAFtttVWQHXvssUD2Fujdd98dxtU0f8UEl60+gO+//77k7yPbtoWatEY0pvENI4GYxo8Z06ZNA2D6\n9OlBJoE5/fr1C7Jly5YVfOxNN900jGX75/PPP2/UeQqi8bt37x5k48ePD2MdbVgoOrBp2223BWDi\nxImNPt7qTKGBcabxDSOB2MQ3jARipn4ZEYeU7HVDJga/PofcDjvsAEDz5s2D7IorrgDgqaeeKvgc\ndJTd5ZdfHsaHHnooALvsskudcysEWYZ07NgxyFq3bh3Gjz32WMHHFLQjb+7cuUD29diefob11lsP\ngG+//TavvzeNbxgJxCa+YSSQRifpNOrNVtMkHZ1iu8UWW4Rxt27dAHj55ZeDTKevRiHe608++STI\nikkt1suMMWPGhPHuu+8OwK9+9asge+CBBwo+vpjeL730UuR7SphxY9KwG3o/MFMfMvdDlldLlixh\nxYoVZU3SMQyjRkm0c09rau2Q6tGjB5AdPTd79mwgW3uL8+mAAw4IsvPOOy+Mn3/+eSBTrCIfZsyY\nAZQuzVVrRR1RJ9d+2WWXBVkxGl/SiSF7H7/U6bqm5aMRKyuqkEgUpvENI4HYxDeMBJIYU18XiNx/\n//2BzP44ZO9nr7POOkD2UkBMTB3i+t///hfIDoV9//33w/j+++8HCjN3y1nJRtfpE5Nwyy23DDJJ\ntClkP79FixZZrwW45557wjhJlXlymdn692Ka6/p58ox+/fXXQZbr/slzuXTpUsBCdg3DaICKa/xK\nVFLRmnrvvfcGYPjw4UEmjrz6HF/vvfceAKNHj65z7MMPPzyMJcruyy+/DDK9PVaIU69c6GvU1sgp\np5wCZFcsli0+XdsvFyeddBKQbU1E3bdaQe6HfLYA11+fKiO5556ZItJSDRkyz1uuCr369xLxqO/1\naaedBmQ/T/mSKyJ0VXJqfOfc351zi5xzk5SshXPuRefc9PT/zRs6hmEY8SIfU/8B4LBVZJcCo733\nnYDR6Z8Nw6gRcpr63vvXnHPtVxH3AQ5Ij4cCrwK/L+F5FYwkKQDceeedYdynTx8g2+khe/F/+9vf\ngmzKlClhLHv2ukGFmIB6v1rMwYcffjjItMkbB7Tpp6MGRa6XRRdddBGQ29TXkXkXX3wxAMOGDQuy\nOFfJWTXSDWDQoEFhLEVOtSkvDrbly5cHmf6cP/oo1Txq1KhRQSbP0xFHHBFkOsZDkmn++Mc/Btn8\n+fMLvh6h0KVzY517rb33cpYLgNYN/bFhGPGiaOee9943FINvnXQMI340duIvdM619d7Pd861BRbV\n94fe+yHAEEgl6ZTam9+yZUsAnnzyySDbddddw1hMMilYCZmyVvUl1+y8885Atlf+yCOPBLK9+lJW\n6tZbbw2yOO9b61ztqM+hXbt2eR1H79lLeax//etfRZ5d+dDe9JtuugnIeNABNthggzCWZYqETgM8\n9NBDQHZI88KFC8NYkqj0PZVna/PNNw8y/XsJ59Y7LZV8dhpr6j8NyJ07DSi8QoRhGFUjp8Z3zj1E\nypG3qXNuLnAlcAMw0jl3BjAb6FvOk1wViRYDePrpVNcuSYEFWLQoY4AceOCBACxYsCDIRGM98sgj\nQaadMFGxBqIJJFoP4LjjjgMyzsA4orWddoBGoYtbNoTs3UOmTfOSJUsacXaVQUdWnn322UC2g/LV\nV18N4z//+c9AtoMz30hGHRMhCV66y5HW6FIevTFVj0pBPl79fvX8qneJz8UwjAphIbuGkUBqKklH\nzDO9/961a1cg2wTfZ599wlj2RnUyhFS30Xu52gz79NNPgewwX3mfNm3aBNmHH37Y2EupGNrU14lI\nUeGlsoTSTj59XwYPHgxkO8bkMxGTP45svfXWYSyOOJ1IpJO1ZK++MU5obeq3bdsWyDQ8hezl5oQJ\nExr9PqXANL5hJJCa0vgHH3wwAMccc0yQiUbS23Xa2SaabeDAgUEmTiz9rf/732cCD6OSa/r27Vvn\nNRIVKOm3cURr4v322y+MozR+q1atgIzFA9lbnlGIBpUIPshu8V0t55VGR8QNGTIEyC413phz1PdP\n0ri1w1DKl+v7ry3EcvQXLATT+IaRQGziG0YCiX15bW1SiVNOV40ZOXIkAP379w8yfU2yZy/OOcg4\nWebMmRNkuaKm9thjDyB7f3fWrFkAbLfddpHvHQd22223MH7llVfCWDudBDn3fAs2anQJcIl0Axgw\nYECd31ca7djdbLPNgOzlYK7PTJY7OsLvyiuvDGOJ3NRJPG+99RaQ7Vg855xzwvjee+/N/wIKxHtv\n5bUNw6iLTXzDSCCx9+rrfXNJpNGmmXiT6zPXJNT2nXfeKeo8osJdxTOr9291Dn8cOP3008NYe5jl\nfsl+MmS8zieffHKQ6WuT69W1DeSYupip7IAA3HLLLQB88MEHjb+IItHnK2a/DvvWyxDxxuscfVnK\n6eWRXipccsklQPZSQJJz9BLy3//+dxFXUVpM4xtGAom9xtcaS/ZJtRNl2bJlDb6+GGeb/gYXzaW1\ngzhr4qblIaOpjz766CDTe/KSotu7dyblQu6VTt+dOHFiGI8YMQLI3oMW59XYsWODTFsWN998M5DR\npPp9KoXWuhKjoWMvdCck2ZPXn7OUuxaHHcBhh2Wq0YlFseOOOwaZWBbagbx48eLGX0SJMY1vGAnE\nJr5hJJDY7+O/++67Ydy9e3cgU0EHoHPnzkDx5qPeuxbzVRfRFDNu3LhxQSYdeeJYdUccUTpcVTso\nZU9f2lhD5h5qR12uZYwsv7QZrGMHZFkmlZKgcSGy8j6ligfQRVOlZgNkWonrNuViytf3jMmySjdU\nlXtw1lmZqnO6IGk5sX18wzAiib1zT0c+CbrCTjFozXbUUUeFsVRhkdRKyPTMO/HEE4MsjppeEC2k\nt+M02267LZAd1SZbn4U4K+X4Oi1aI84yXQfx7bffzvv4gpyn1DkslpkzZ0aOG/OZSpn1Ll26BJls\n9+lakHEin046WzjnXnH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5fb7e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6310... Discriminator Loss: 0.8769... Generator Loss: 1.6217\n",
      "Epoch 2/2-Step 6320... Discriminator Loss: 0.5226... Generator Loss: 2.1985\n",
      "Epoch 2/2-Step 6330... Discriminator Loss: 1.0925... Generator Loss: 3.7481\n",
      "Epoch 2/2-Step 6340... Discriminator Loss: 1.1286... Generator Loss: 2.2092\n",
      "Epoch 2/2-Step 6350... Discriminator Loss: 0.5047... Generator Loss: 2.7746\n",
      "Epoch 2/2-Step 6360... Discriminator Loss: 0.4286... Generator Loss: 4.3793\n",
      "Epoch 2/2-Step 6370... Discriminator Loss: 0.7580... Generator Loss: 2.8480\n",
      "Epoch 2/2-Step 6380... Discriminator Loss: 1.2151... Generator Loss: 1.8627\n",
      "Epoch 2/2-Step 6390... Discriminator Loss: 0.6954... Generator Loss: 4.3327\n",
      "Epoch 2/2-Step 6400... Discriminator Loss: 0.6671... Generator Loss: 2.2565\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nafVrpMhHv6esi1ezMKn
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db61b0198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6410... Discriminator Loss: 1.0369... Generator Loss: 3.2546\n",
      "Epoch 2/2-Step 6420... Discriminator Loss: 0.4658... Generator Loss: 2.5425\n",
      "Epoch 2/2-Step 6430... Discriminator Loss: 0.6799... Generator Loss: 2.8313\n",
      "Epoch 2/2-Step 6440... Discriminator Loss: 0.7715... Generator Loss: 2.1997\n",
      "Epoch 2/2-Step 6450... Discriminator Loss: 0.5598... Generator Loss: 3.1018\n",
      "Epoch 2/2-Step 6460... Discriminator Loss: 2.4926... Generator Loss: 0.2657\n",
      "Epoch 2/2-Step 6470... Discriminator Loss: 0.5483... Generator Loss: 2.5166\n",
      "Epoch 2/2-Step 6480... Discriminator Loss: 0.8568... Generator Loss: 1.0284\n",
      "Epoch 2/2-Step 6490... Discriminator Loss: 0.9080... Generator Loss: 1.8248\n",
      "Epoch 2/2-Step 6500... Discriminator Loss: 0.7513... Generator Loss: 1.2208\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJztnXeYlNX1+D8nliBqBCyIgoJYIqIU0Vij4lcFRdAYCyrR\nqDGJ/GzRKGpM7LElxBYTo6AYIopINBYMokYsUVCJhQ4BBRFQgShqLLm/P2bOnTPs7O7s7szsvPue\nz/PwcPdMu+8775173lMlhIDjOOniG809AcdxKo8vfMdJIb7wHSeF+MJ3nBTiC99xUogvfMdJIb7w\nHSeFNGnhi0g/EZklInNFZFipJuU4TnmRxgbwiMhawGzgIGARMAUYHEKYXrrpOY5TDtZuwmt3B+aG\nEOYDiMgYYBBQ68IXEQ8TrAcRAcAjKp3GEkKQ+p7TlIW/JfCu+XsR8J36XuQXdk3WWmutOP7GNzJ3\nX19++WVzTacq0evG4tdQDr1u/ve//xX1/KYs/KIQkdOB08v9OY7jFE9TFv5ioJP5u2NWlkcI4Q7g\nDsio+v4rXZOvv/664NjJ4ddN3RS70ytNsepPAbYTkS4isi5wHPBIE97PcZwK0egdP4TwlYj8P+BJ\nYC1gRAjh7ZLNzHGcstFod16jPsyt+o5Tdoqx6nvknuOkEF/4jpNCfOE7Tgopux/fcZwMNlCrud22\nvuM7Tgrxhe84KcRVfccpI9/85jfj+IUXXojj0047DYBp06ZVfE7gO77jpBJf+I6TQhKv6ms64vrr\nrx9ln376aRwXaz1t1apVHN9zzz1AfuLDCSecUEPmtDzU8l4qq7u9Lrt37x7HRx11FOCqvuM4FSTx\nO/6BBx4IwKhRo6Jsgw02iOPZs2cDcMUVV0TZ5MmTAfjqq6+iTHd0gIEDBwL5u/vGG28MwPLly0s2\n91Khu5TNu9BxbbkYWthCNSYrs68ppOG0tBRZew6GDx8OwCWXXBJlH3/8caPfe5tttonjtdfOLbeu\nXbs2+j1Lge/4jpNCfOE7TgpJpKpvDXHXXXcdAJtttlmU2fpsvXr1AuDee++NsiFDhgAwadKkKHvv\nvfdqfI4NsWzfvj1QPar+gAED4njPPfcE4O9//3uUzZw5E8iv3bfhhhvG8f777w/A4MGDo6xLly41\nPsf6oVesWAHA1KlTo+zGG28EcrdUkLxbgU022SSOTzzxRADeeuutKLvjjjsa/d677bZbHNvrct11\n1230e5YC3/EdJ4Ukcsffb7/94ni77bYDCldhtdhdaOnSpUC+cc++Xo091ujTv39/AN5+O1dkqNI7\n2zrrrBPHt9xySxyrtjN06NAoU5dm69ato2y99daLY3tsa2I1HctWW20FQI8ePaJMjaKbbrpplK1e\nvbqOo6g+rAFOz9cBBxwQZU3Z8Q899NCC8qYYDEtBvTu+iIwQkWUi8paRtRORiSIyJ/t/2/JO03Gc\nUlKMqn830G8N2TBgUghhO2BS9m/HcRJCvap+COE5Eem8hngQsH92fA/wLHBhCedVEFV1zznnnCiz\n6mtdPPXUU3H8xhtvAPmqvlXtrL9V0XiBW2+9Nco+++yzoj67VFx++eVx3KlTrrK5qu3W6KkRY9YP\nbyMaX3/9dSA/cWThwoVAvo95r732iuM99tgDyL/l+PDDD4HKn4tSYo9Xb3PsLVJjmsDod7HLLrsU\nfNwaD5uDxhr32ocQlmTH7wPtSzQfx3EqQJONeyGEUFf1XO+k4zjVR2MX/lIR6RBCWCIiHYBltT1x\nzU46jfw8IGe9tr7RuqzTAO+//z4Axx9/fJT997//JTufKOvQoYOdc97/kPPft22bs2OWQ70tpFaq\nWn/uuefWeB7AF198AcBrr70WZQ888AAAr7zySpS9+26u1eEnn3wC5CejqDXe3h5Y3/6MGTNqzG3E\niBE1XpM02rVrV0O2eHGNplANok+fPkC+t8OeozfffLNJ799UGqvqPwKclB2fBDxcmuk4jlMJ6t3x\nReQ+Moa8TURkEfAr4FrgARE5FVgIHFPOSSpbb701UL9Bz+5IZ511FpBvyFPjlDVc9e3bt8b72F9o\nfe6gQYOi7E9/+lON924qhQx106dnOo/baC+NooOcr9ju+LqT15ekY99TjZo2lXTChAlxrOdNNQyA\n22+/vf6DqnIK7fjWEFos9rq8+uqrgfzIR3vebDxIc1CMVX9wLQ8dWOK5OI5TITxk13FSSKJCdhuj\nUp9//vkAHHvssVGmvtXNN988yqzftlDIbufOnQG4/vrro+zzzz8HchV7oOlGLjUG2fBbTbTRBBKA\nMWPGxHFjQof1NWrotFiVtU2bNjVeY9X/Dz74oMGfXW1YI63eAtXnx7fGVb1FOvnkk6Osd+/eNT5n\n1qxZcbxsWa328IrgO77jpJBEdcvV1NiXXnopynQnrg3dgQsl8dSX2FMfK1euBHKJQpCLZGssmjrb\nmLqBpeJb3/pWHC9ZsiSO1binacAAr776auUmViasJnX33XcDuehOgEMOOQTIN85ZbVGjOTUFHHLf\no014Ovroo+N4/PjxpZh6QbxbruM4BfGF7zgpJFHGvY8++giAK6+8MspuvvlmIN8YYylkmNGxVfVr\nG9eF+tpLGbXW3HnakG8ItX5+jV5s7qizUvP888/HsRqQd9hhhyh7+eWXgfxrzBo99VZMo0Qhd7tk\nb9NsolipaWgike/4jpNCfOE7TgpJlKqvKvUTTzwRZVoW6eCDD44ya5VWH60NcdVClDbvvlu3bnGs\niSk2dNXmoCuqxmnCS9LR8/GjH/2o4OMaomyt2y2BRYsWxfGLL74I5Fvo9ZbO+t6fffbZOL7ssssA\n2GeffaLsD3/4A5Af51DO60RjTor1APmO7zgpJFE7vvrxd9xxxyh78sknAbjvvvuizP7Kqq/d+sXV\nAGLLTR9++OFxrF13rOZQCC0pXcokneZEOxDZVFy7g4wePbric1IaUwWnWOz3p5GeWnEJ4JlnngHy\nE2sKRTxaTUnna6McyxkzoynrxUZS+o7vOCnEF77jpJCqV/WtH1kNKrZazjvvvAPAT3/60yizlWYK\nGTvUUHfkkUdGmU2+sVV21sS+37BhmeLCSescUxtaX942HdVEJGh6VZqmUKlzrIk0NlbBdiMqhBpF\nrYFZDdGVqlegBtdiY0p8x3ecFFL1O76NolPjnnWzqaHPpqnaX965c+cCuS4wAH/5y1+A/I4wtXWP\nUfRX/69//WuUPffcc0UeRd3Yz650Qo49v3o+rJvT9hS0u39LRY3ADdEwtIKPVoiCnOuuUlGOGtVa\nssg9EekkIs+IyHQReVtEzs7KvZuO4ySUYlT9r4DzQgjdgD2AoSLSDe+m4ziJpZiae0uAJdnxxyIy\nA9iSCnXTsf7SkSNHAnDmmWdGmaqqNj9au8RAzkdrI+/qK8mtBhKbWz98+HAAbrrppoJzawrNWZra\nqvpaV8CeH+sXTnIJ7WIpWlU250hbsNvKRePGjQMq12GoocbPBt3jZ1tp9QJepshuOt5Qw3Gqj6IX\nvohsAIwDzgkh/MfuFHV10yllQ40LLrgAyO9z1717d51flFljWX1GO8W6bKZNmwbk173T0tUtbdez\n561jx441Hrdpwi3FbVkKrAFZr0d7fn71q1/VkFUTRbnzRGQdMot+dAjhoax4abaLDvV103Ecp7oo\nxqovwF3AjBDCb81D3k3HcRJKMar+3sAQ4E0RmZaVXUwzdNPR6CTtSwYwatQoAAYOHBhltntJIVSt\nVx8/5BJzAB599FGgeg0z5frsQsbKSscVVDM2fkTTbiF3O7lq1aoomz9/fuUm1giKseo/D9RWi8q7\n6ThOAvGQXcdJIVUfslsIa4EfPDjT2s8m82hoL+TylK3KumDBAgD+85//RFlLs9YXi1X158yZU0NW\nX02ClopV6zVpy3p5ttxyyxqvseG51V6jwXd8x0khidzxC2HrwNm0XDt2amJ390LnyiYy6S64evXq\n8k+smdC4BtUkIVdZx9Z
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5dea2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6510... Discriminator Loss: 0.8845... Generator Loss: 1.4787\n",
      "Epoch 2/2-Step 6520... Discriminator Loss: 0.6351... Generator Loss: 2.3245\n",
      "Epoch 2/2-Step 6530... Discriminator Loss: 0.6925... Generator Loss: 2.7838\n",
      "Epoch 2/2-Step 6540... Discriminator Loss: 1.0024... Generator Loss: 1.8678\n",
      "Epoch 2/2-Step 6550... Discriminator Loss: 0.4503... Generator Loss: 3.6231\n",
      "Epoch 2/2-Step 6560... Discriminator Loss: 0.5370... Generator Loss: 1.9547\n",
      "Epoch 2/2-Step 6570... Discriminator Loss: 0.8754... Generator Loss: 2.7954\n",
      "Epoch 2/2-Step 6580... Discriminator Loss: 0.5483... Generator Loss: 1.0366\n",
      "Epoch 2/2-Step 6590... Discriminator Loss: 0.8964... Generator Loss: 2.2711\n",
      "Epoch 2/2-Step 6600... Discriminator Loss: 1.4574... Generator Loss: 1.0392\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXgjAsGHDoky1u9VM2hw
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5b81d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6610... Discriminator Loss: 0.7733... Generator Loss: 2.3364\n",
      "Epoch 2/2-Step 6620... Discriminator Loss: 0.6323... Generator Loss: 2.2168\n",
      "Epoch 2/2-Step 6630... Discriminator Loss: 0.7862... Generator Loss: 3.1674\n",
      "Epoch 2/2-Step 6640... Discriminator Loss: 0.6309... Generator Loss: 2.5274\n",
      "Epoch 2/2-Step 6650... Discriminator Loss: 0.4759... Generator Loss: 3.2742\n",
      "Epoch 2/2-Step 6660... Discriminator Loss: 0.8597... Generator Loss: 2.6065\n",
      "Epoch 2/2-Step 6670... Discriminator Loss: 0.4565... Generator Loss: 5.1126\n",
      "Epoch 2/2-Step 6680... Discriminator Loss: 0.7354... Generator Loss: 2.6744\n",
      "Epoch 2/2-Step 6690... Discriminator Loss: 1.1391... Generator Loss: 1.2024\n",
      "Epoch 2/2-Step 6700... Discriminator Loss: 0.4862... Generator Loss: 2.8869\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIbuO49SlpM69EELcT0+
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc203940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6710... Discriminator Loss: 1.7944... Generator Loss: 0.7128\n",
      "Epoch 2/2-Step 6720... Discriminator Loss: 1.2603... Generator Loss: 1.0764\n",
      "Epoch 2/2-Step 6730... Discriminator Loss: 0.7676... Generator Loss: 2.2555\n",
      "Epoch 2/2-Step 6740... Discriminator Loss: 0.4053... Generator Loss: 3.8977\n",
      "Epoch 2/2-Step 6750... Discriminator Loss: 0.5686... Generator Loss: 2.4416\n",
      "Epoch 2/2-Step 6760... Discriminator Loss: 1.4201... Generator Loss: 0.9679\n",
      "Epoch 2/2-Step 6770... Discriminator Loss: 1.9880... Generator Loss: 1.1824\n",
      "Epoch 2/2-Step 6780... Discriminator Loss: 0.7021... Generator Loss: 3.8395\n",
      "Epoch 2/2-Step 6790... Discriminator Loss: 0.5372... Generator Loss: 2.7730\n",
      "Epoch 2/2-Step 6800... Discriminator Loss: 0.6950... Generator Loss: 2.6168\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJj51ddMxDCNaZOPVd8C
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dc43e1080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6810... Discriminator Loss: 0.6412... Generator Loss: 3.2091\n",
      "Epoch 2/2-Step 6820... Discriminator Loss: 1.2548... Generator Loss: 1.8086\n",
      "Epoch 2/2-Step 6830... Discriminator Loss: 0.5971... Generator Loss: 2.0719\n",
      "Epoch 2/2-Step 6840... Discriminator Loss: 0.8948... Generator Loss: 3.3392\n",
      "Epoch 2/2-Step 6850... Discriminator Loss: 0.7414... Generator Loss: 2.0902\n",
      "Epoch 2/2-Step 6860... Discriminator Loss: 1.1879... Generator Loss: 1.7849\n",
      "Epoch 2/2-Step 6870... Discriminator Loss: 0.6110... Generator Loss: 2.6223\n",
      "Epoch 2/2-Step 6880... Discriminator Loss: 0.8266... Generator Loss: 1.6606\n",
      "Epoch 2/2-Step 6890... Discriminator Loss: 0.7198... Generator Loss: 2.5820\n",
      "Epoch 2/2-Step 6900... Discriminator Loss: 0.8805... Generator Loss: 3.3767\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzr1aKxdeTOReMV79fwG
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcbe4f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 6910... Discriminator Loss: 0.7295... Generator Loss: 2.8159\n",
      "Epoch 2/2-Step 6920... Discriminator Loss: 0.4606... Generator Loss: 3.3780\n",
      "Epoch 2/2-Step 6930... Discriminator Loss: 0.4996... Generator Loss: 3.2139\n",
      "Epoch 2/2-Step 6940... Discriminator Loss: 0.7106... Generator Loss: 2.6001\n",
      "Epoch 2/2-Step 6950... Discriminator Loss: 0.5668... Generator Loss: 3.2564\n",
      "Epoch 2/2-Step 6960... Discriminator Loss: 0.5170... Generator Loss: 1.9829\n",
      "Epoch 2/2-Step 6970... Discriminator Loss: 0.7273... Generator Loss: 2.1486\n",
      "Epoch 2/2-Step 6980... Discriminator Loss: 1.0883... Generator Loss: 0.9216\n",
      "Epoch 2/2-Step 6990... Discriminator Loss: 0.6871... Generator Loss: 1.9921\n",
      "Epoch 2/2-Step 7000... Discriminator Loss: 0.9391... Generator Loss: 1.9562\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6a4b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 7010... Discriminator Loss: 1.3753... Generator Loss: 2.6379\n",
      "Epoch 2/2-Step 7020... Discriminator Loss: 1.4447... Generator Loss: 2.1549\n",
      "Epoch 2/2-Step 7030... Discriminator Loss: 0.6425... Generator Loss: 5.0186\n",
      "Epoch 2/2-Step 7040... Discriminator Loss: 0.6218... Generator Loss: 2.4475\n",
      "Epoch 2/2-Step 7050... Discriminator Loss: 0.4471... Generator Loss: 2.5640\n",
      "Epoch 2/2-Step 7060... Discriminator Loss: 1.1542... Generator Loss: 2.3678\n",
      "Epoch 2/2-Step 7070... Discriminator Loss: 0.9305... Generator Loss: 1.9455\n",
      "Epoch 2/2-Step 7080... Discriminator Loss: 0.9533... Generator Loss: 2.5780\n",
      "Epoch 2/2-Step 7090... Discriminator Loss: 0.6289... Generator Loss: 2.9755\n",
      "Epoch 2/2-Step 7100... Discriminator Loss: 0.5540... Generator Loss: 2.8860\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n03Ecp74oxasvwA3AlBD
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbcf4c320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 7110... Discriminator Loss: 1.1568... Generator Loss: 0.4607\n",
      "Epoch 2/2-Step 7120... Discriminator Loss: 0.5529... Generator Loss: 2.9060\n",
      "Epoch 2/2-Step 7130... Discriminator Loss: 0.5750... Generator Loss: 1.9021\n",
      "Epoch 2/2-Step 7140... Discriminator Loss: 0.5779... Generator Loss: 3.6781\n",
      "Epoch 2/2-Step 7150... Discriminator Loss: 2.8000... Generator Loss: 0.6050\n",
      "Epoch 2/2-Step 7160... Discriminator Loss: 0.6821... Generator Loss: 1.6332\n",
      "Epoch 2/2-Step 7170... Discriminator Loss: 0.5249... Generator Loss: 4.1212\n",
      "Epoch 2/2-Step 7180... Discriminator Loss: 0.7032... Generator Loss: 2.5562\n",
      "Epoch 2/2-Step 7190... Discriminator Loss: 1.2260... Generator Loss: 2.8265\n",
      "Epoch 2/2-Step 7200... Discriminator Loss: 0.5276... Generator Loss: 3.0845\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd295da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 7210... Discriminator Loss: 0.5545... Generator Loss: 5.3410\n",
      "Epoch 2/2-Step 7220... Discriminator Loss: 0.6709... Generator Loss: 1.7314\n",
      "Epoch 2/2-Step 7230... Discriminator Loss: 0.9212... Generator Loss: 1.9842\n",
      "Epoch 2/2-Step 7240... Discriminator Loss: 1.1896... Generator Loss: 1.3528\n",
      "Epoch 2/2-Step 7250... Discriminator Loss: 1.8491... Generator Loss: 1.0279\n",
      "Epoch 2/2-Step 7260... Discriminator Loss: 1.1249... Generator Loss: 1.9241\n",
      "Epoch 2/2-Step 7270... Discriminator Loss: 0.6457... Generator Loss: 1.8654\n",
      "Epoch 2/2-Step 7280... Discriminator Loss: 1.0057... Generator Loss: 1.0534\n",
      "Epoch 2/2-Step 7290... Discriminator Loss: 0.5613... Generator Loss: 2.1446\n",
      "Epoch 2/2-Step 7300... Discriminator Loss: 1.4272... Generator Loss: 4.6810\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAk4AXheRyXndMKrUTWf
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc851048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 7310... Discriminator Loss: 0.4885... Generator Loss: 3.5997\n",
      "Epoch 2/2-Step 7320... Discriminator Loss: 0.5702... Generator Loss: 3.4760\n",
      "Epoch 2/2-Step 7330... Discriminator Loss: 0.4456... Generator Loss: 2.9317\n",
      "Epoch 2/2-Step 7340... Discriminator Loss: 0.6239... Generator Loss: 4.1287\n",
      "Epoch 2/2-Step 7350... Discriminator Loss: 0.6633... Generator Loss: 1.7932\n",
      "Epoch 2/2-Step 7360... Discriminator Loss: 0.7431... Generator Loss: 2.4085\n",
      "Epoch 2/2-Step 7370... Discriminator Loss: 1.0659... Generator Loss: 1.5433\n",
      "Epoch 2/2-Step 7380... Discriminator Loss: 0.5794... Generator Loss: 3.3885\n",
      "Epoch 2/2-Step 7390... Discriminator Loss: 0.8348... Generator Loss: 2.1125\n",
      "Epoch 2/2-Step 7400... Discriminator Loss: 0.6078... Generator Loss: 2.4070\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFWsDCShE7tkYbRvHfu2
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc5e4b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/2-Step 7410... Discriminator Loss: 0.5395... Generator Loss: 2.3707\n",
      "Epoch 2/2-Step 7420... Discriminator Loss: 0.5152... Generator Loss: 2.2250\n",
      "Epoch 2/2-Step 7430... Discriminator Loss: 0.6904... Generator Loss: 2.1426\n",
      "Epoch 2/2-Step 7440... Discriminator Loss: 2.0568... Generator Loss: 1.2556\n",
      "Epoch 2/2-Step 7450... Discriminator Loss: 0.5332... Generator Loss: 3.8167\n",
      "Epoch 2/2-Step 7460... Discriminator Loss: 1.0589... Generator Loss: 1.5023\n",
      "Epoch 2/2-Step 7470... Discriminator Loss: 0.5698... Generator Loss: 2.2062\n",
      "Epoch 2/2-Step 7480... Discriminator Loss: 0.8149... Generator Loss: 2.4610\n",
      "Epoch 2/2-Step 7490... Discriminator Loss: 0.6703... Generator Loss: 2.3635\n",
      "Epoch 2/2-Step 7500... Discriminator Loss: 0.4998... Generator Loss: 3.8136\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nH2fTd2mrzig2dz6rsQ6
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7e5b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size = 16\n",
    "z_dim = 100\n",
    "learning_rate = 0.0008\n",
    "beta1 = 0.3\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 2\n",
    "\n",
    "mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,\n",
    "          mnist_dataset.shape, mnist_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CelebA\n",
    "Run your GANs on CelebA.  It will take around 20 minutes on the average GPU to run one epoch.  You can run the whole epoch or stop when it starts to generate realistic faces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10... Discriminator Loss: 0.6948... Generator Loss: 3.3452\n",
      "Epoch 1/1-Step 20... Discriminator Loss: 0.5769... Generator Loss: 2.6424\n",
      "Epoch 1/1-Step 30... Discriminator Loss: 3.4842... Generator Loss: 0.3680\n",
      "Epoch 1/1-Step 40... Discriminator Loss: 2.2605... Generator Loss: 1.0264\n",
      "Epoch 1/1-Step 50... Discriminator Loss: 2.2086... Generator Loss: 1.0365\n",
      "Epoch 1/1-Step 60... Discriminator Loss: 1.3705... Generator Loss: 1.3561\n",
      "Epoch 1/1-Step 70... Discriminator Loss: 0.8398... Generator Loss: 3.7831\n",
      "Epoch 1/1-Step 80... Discriminator Loss: 0.9082... Generator Loss: 3.3262\n",
      "Epoch 1/1-Step 90... Discriminator Loss: 0.6524... Generator Loss: 4.4925\n",
      "Epoch 1/1-Step 100... Discriminator Loss: 0.8634... Generator Loss: 2.0833\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGnF+Pe3cddJcrQzT5ku
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dc43f05f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 110... Discriminator Loss: 0.5612... Generator Loss: 6.2238\n",
      "Epoch 1/1-Step 120... Discriminator Loss: 0.4266... Generator Loss: 4.6407\n",
      "Epoch 1/1-Step 130... Discriminator Loss: 0.5806... Generator Loss: 3.8676\n",
      "Epoch 1/1-Step 140... Discriminator Loss: 0.6074... Generator Loss: 3.3198\n",
      "Epoch 1/1-Step 150... Discriminator Loss: 0.6334... Generator Loss: 3.1457\n",
      "Epoch 1/1-Step 160... Discriminator Loss: 1.4266... Generator Loss: 1.7842\n",
      "Epoch 1/1-Step 170... Discriminator Loss: 0.6803... Generator Loss: 4.0512\n",
      "Epoch 1/1-Step 180... Discriminator Loss: 0.5272... Generator Loss: 5.1044\n",
      "Epoch 1/1-Step 190... Discriminator Loss: 0.8611... Generator Loss: 3.0120\n",
      "Epoch 1/1-Step 200... Discriminator Loss: 0.5037... Generator Loss: 3.2993\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+pSfTHN64XrWoJctkpj
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7bcc278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 210... Discriminator Loss: 2.2002... Generator Loss: 0.6803\n",
      "Epoch 1/1-Step 220... Discriminator Loss: 2.9776... Generator Loss: 7.8913\n",
      "Epoch 1/1-Step 230... Discriminator Loss: 0.9352... Generator Loss: 3.2061\n",
      "Epoch 1/1-Step 240... Discriminator Loss: 1.5503... Generator Loss: 0.8233\n",
      "Epoch 1/1-Step 250... Discriminator Loss: 2.4947... Generator Loss: 5.8038\n",
      "Epoch 1/1-Step 260... Discriminator Loss: 1.3574... Generator Loss: 3.7615\n",
      "Epoch 1/1-Step 270... Discriminator Loss: 0.9844... Generator Loss: 1.6795\n",
      "Epoch 1/1-Step 280... Discriminator Loss: 1.3204... Generator Loss: 1.8406\n",
      "Epoch 1/1-Step 290... Discriminator Loss: 1.5406... Generator Loss: 1.4763\n",
      "Epoch 1/1-Step 300... Discriminator Loss: 1.6007... Generator Loss: 0.9111\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4fZooO3VDPf6c4BzzYj
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc637358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 310... Discriminator Loss: 1.4419... Generator Loss: 0.5854\n",
      "Epoch 1/1-Step 320... Discriminator Loss: 1.4496... Generator Loss: 1.6651\n",
      "Epoch 1/1-Step 330... Discriminator Loss: 1.5146... Generator Loss: 2.6899\n",
      "Epoch 1/1-Step 340... Discriminator Loss: 1.1532... Generator Loss: 1.6639\n",
      "Epoch 1/1-Step 350... Discriminator Loss: 1.6224... Generator Loss: 4.0044\n",
      "Epoch 1/1-Step 360... Discriminator Loss: 1.8819... Generator Loss: 0.6789\n",
      "Epoch 1/1-Step 370... Discriminator Loss: 2.3529... Generator Loss: 0.6585\n",
      "Epoch 1/1-Step 380... Discriminator Loss: 1.6916... Generator Loss: 1.7879\n",
      "Epoch 1/1-Step 390... Discriminator Loss: 1.1988... Generator Loss: 1.9067\n",
      "Epoch 1/1-Step 400... Discriminator Loss: 1.2352... Generator Loss: 2.3775\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfKnGENFEGDKdxzJr4og
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db76ad940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 410... Discriminator Loss: 1.3067... Generator Loss: 0.9307\n",
      "Epoch 1/1-Step 420... Discriminator Loss: 1.7364... Generator Loss: 1.5132\n",
      "Epoch 1/1-Step 430... Discriminator Loss: 1.5336... Generator Loss: 0.5122\n",
      "Epoch 1/1-Step 440... Discriminator Loss: 1.1227... Generator Loss: 1.1711\n",
      "Epoch 1/1-Step 450... Discriminator Loss: 1.4362... Generator Loss: 0.8495\n",
      "Epoch 1/1-Step 460... Discriminator Loss: 1.5072... Generator Loss: 1.4957\n",
      "Epoch 1/1-Step 470... Discriminator Loss: 1.5810... Generator Loss: 0.6281\n",
      "Epoch 1/1-Step 480... Discriminator Loss: 1.5124... Generator Loss: 1.3238\n",
      "Epoch 1/1-Step 490... Discriminator Loss: 1.3301... Generator Loss: 1.0135\n",
      "Epoch 1/1-Step 500... Discriminator Loss: 1.2923... Generator Loss: 1.4120\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbd291208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 510... Discriminator Loss: 1.2858... Generator Loss: 1.0359\n",
      "Epoch 1/1-Step 520... Discriminator Loss: 1.2062... Generator Loss: 0.6443\n",
      "Epoch 1/1-Step 530... Discriminator Loss: 2.0740... Generator Loss: 0.6347\n",
      "Epoch 1/1-Step 540... Discriminator Loss: 2.0029... Generator Loss: 0.9360\n",
      "Epoch 1/1-Step 550... Discriminator Loss: 1.6063... Generator Loss: 1.0159\n",
      "Epoch 1/1-Step 560... Discriminator Loss: 1.8288... Generator Loss: 1.1525\n",
      "Epoch 1/1-Step 570... Discriminator Loss: 1.4487... Generator Loss: 0.8964\n",
      "Epoch 1/1-Step 580... Discriminator Loss: 1.6944... Generator Loss: 0.6237\n",
      "Epoch 1/1-Step 590... Discriminator Loss: 1.6855... Generator Loss: 0.6829\n",
      "Epoch 1/1-Step 600... Discriminator Loss: 2.1247... Generator Loss: 0.6535\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nfGTsccJMx9hoqfcP0o6
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6a6b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 610... Discriminator Loss: 1.6402... Generator Loss: 0.8048\n",
      "Epoch 1/1-Step 620... Discriminator Loss: 1.1703... Generator Loss: 0.9652\n",
      "Epoch 1/1-Step 630... Discriminator Loss: 1.3092... Generator Loss: 1.4569\n",
      "Epoch 1/1-Step 640... Discriminator Loss: 1.6696... Generator Loss: 1.1008\n",
      "Epoch 1/1-Step 650... Discriminator Loss: 1.5877... Generator Loss: 0.7111\n",
      "Epoch 1/1-Step 660... Discriminator Loss: 1.8134... Generator Loss: 0.7386\n",
      "Epoch 1/1-Step 670... Discriminator Loss: 2.1744... Generator Loss: 0.7673\n",
      "Epoch 1/1-Step 680... Discriminator Loss: 2.2540... Generator Loss: 0.3870\n",
      "Epoch 1/1-Step 690... Discriminator Loss: 1.6325... Generator Loss: 0.7615\n",
      "Epoch 1/1-Step 700... Discriminator Loss: 1.6979... Generator Loss: 1.1176\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6b89710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 710... Discriminator Loss: 1.9598... Generator Loss: 0.8296\n",
      "Epoch 1/1-Step 720... Discriminator Loss: 1.7752... Generator Loss: 0.7731\n",
      "Epoch 1/1-Step 730... Discriminator Loss: 1.5313... Generator Loss: 0.7340\n",
      "Epoch 1/1-Step 740... Discriminator Loss: 1.1701... Generator Loss: 1.2715\n",
      "Epoch 1/1-Step 750... Discriminator Loss: 1.2427... Generator Loss: 0.8173\n",
      "Epoch 1/1-Step 760... Discriminator Loss: 1.0936... Generator Loss: 1.4787\n",
      "Epoch 1/1-Step 770... Discriminator Loss: 2.0332... Generator Loss: 0.8092\n",
      "Epoch 1/1-Step 780... Discriminator Loss: 1.2748... Generator Loss: 1.9173\n",
      "Epoch 1/1-Step 790... Discriminator Loss: 1.3099... Generator Loss: 1.0320\n",
      "Epoch 1/1-Step 800... Discriminator Loss: 1.5100... Generator Loss: 0.8716\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt1MiiUBog6zsFDLZLLw
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db654bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 810... Discriminator Loss: 1.6640... Generator Loss: 0.7023\n",
      "Epoch 1/1-Step 820... Discriminator Loss: 1.9084... Generator Loss: 0.7063\n",
      "Epoch 1/1-Step 830... Discriminator Loss: 2.1551... Generator Loss: 0.5465\n",
      "Epoch 1/1-Step 840... Discriminator Loss: 1.4073... Generator Loss: 0.9880\n",
      "Epoch 1/1-Step 850... Discriminator Loss: 1.7260... Generator Loss: 1.0925\n",
      "Epoch 1/1-Step 860... Discriminator Loss: 1.6906... Generator Loss: 1.1531\n",
      "Epoch 1/1-Step 870... Discriminator Loss: 1.6929... Generator Loss: 1.0397\n",
      "Epoch 1/1-Step 880... Discriminator Loss: 1.7217... Generator Loss: 0.6234\n",
      "Epoch 1/1-Step 890... Discriminator Loss: 1.5626... Generator Loss: 0.8849\n",
      "Epoch 1/1-Step 900... Discriminator Loss: 1.6365... Generator Loss: 0.7064\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db677f518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 910... Discriminator Loss: 1.9951... Generator Loss: 0.8262\n",
      "Epoch 1/1-Step 920... Discriminator Loss: 1.7126... Generator Loss: 0.7016\n",
      "Epoch 1/1-Step 930... Discriminator Loss: 1.5343... Generator Loss: 0.8135\n",
      "Epoch 1/1-Step 940... Discriminator Loss: 1.7239... Generator Loss: 0.6900\n",
      "Epoch 1/1-Step 950... Discriminator Loss: 2.1409... Generator Loss: 0.4529\n",
      "Epoch 1/1-Step 960... Discriminator Loss: 1.6916... Generator Loss: 1.2056\n",
      "Epoch 1/1-Step 970... Discriminator Loss: 1.5968... Generator Loss: 1.1107\n",
      "Epoch 1/1-Step 980... Discriminator Loss: 1.7231... Generator Loss: 0.7267\n",
      "Epoch 1/1-Step 990... Discriminator Loss: 1.6630... Generator Loss: 1.0557\n",
      "Epoch 1/1-Step 1000... Discriminator Loss: 1.8460... Generator Loss: 0.5424\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQrZTmMtcPKgB1EKl3vN
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc5a1898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1010... Discriminator Loss: 1.5158... Generator Loss: 1.2338\n",
      "Epoch 1/1-Step 1020... Discriminator Loss: 1.5908... Generator Loss: 0.7055\n",
      "Epoch 1/1-Step 1030... Discriminator Loss: 1.8326... Generator Loss: 0.4710\n",
      "Epoch 1/1-Step 1040... Discriminator Loss: 1.7935... Generator Loss: 0.8422\n",
      "Epoch 1/1-Step 1050... Discriminator Loss: 2.0195... Generator Loss: 1.3330\n",
      "Epoch 1/1-Step 1060... Discriminator Loss: 1.3615... Generator Loss: 1.0103\n",
      "Epoch 1/1-Step 1070... Discriminator Loss: 1.7121... Generator Loss: 0.9841\n",
      "Epoch 1/1-Step 1080... Discriminator Loss: 1.3754... Generator Loss: 0.8956\n",
      "Epoch 1/1-Step 1090... Discriminator Loss: 1.7888... Generator Loss: 0.5945\n",
      "Epoch 1/1-Step 1100... Discriminator Loss: 1.5493... Generator Loss: 0.8137\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc598828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1110... Discriminator Loss: 1.7020... Generator Loss: 1.1219\n",
      "Epoch 1/1-Step 1120... Discriminator Loss: 1.4937... Generator Loss: 0.7029\n",
      "Epoch 1/1-Step 1130... Discriminator Loss: 1.7009... Generator Loss: 0.7746\n",
      "Epoch 1/1-Step 1140... Discriminator Loss: 1.2564... Generator Loss: 0.6284\n",
      "Epoch 1/1-Step 1150... Discriminator Loss: 1.7295... Generator Loss: 0.9546\n",
      "Epoch 1/1-Step 1160... Discriminator Loss: 1.8282... Generator Loss: 0.8065\n",
      "Epoch 1/1-Step 1170... Discriminator Loss: 1.3862... Generator Loss: 0.8113\n",
      "Epoch 1/1-Step 1180... Discriminator Loss: 1.5680... Generator Loss: 0.7849\n",
      "Epoch 1/1-Step 1190... Discriminator Loss: 1.6002... Generator Loss: 1.1626\n",
      "Epoch 1/1-Step 1200... Discriminator Loss: 2.1050... Generator Loss: 0.7167\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyk7V4/seUVNIkfVKXni
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5cb2f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1210... Discriminator Loss: 1.3510... Generator Loss: 0.8378\n",
      "Epoch 1/1-Step 1220... Discriminator Loss: 1.6236... Generator Loss: 0.9097\n",
      "Epoch 1/1-Step 1230... Discriminator Loss: 1.8415... Generator Loss: 0.8852\n",
      "Epoch 1/1-Step 1240... Discriminator Loss: 1.7693... Generator Loss: 0.7753\n",
      "Epoch 1/1-Step 1250... Discriminator Loss: 1.2858... Generator Loss: 0.8492\n",
      "Epoch 1/1-Step 1260... Discriminator Loss: 1.6172... Generator Loss: 1.2246\n",
      "Epoch 1/1-Step 1270... Discriminator Loss: 1.7066... Generator Loss: 1.0093\n",
      "Epoch 1/1-Step 1280... Discriminator Loss: 1.5132... Generator Loss: 0.5467\n",
      "Epoch 1/1-Step 1290... Discriminator Loss: 1.7384... Generator Loss: 1.1565\n",
      "Epoch 1/1-Step 1300... Discriminator Loss: 1.5952... Generator Loss: 0.9479\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRfhdnGoZ9zzp1+opWwL
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dadc4be80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1310... Discriminator Loss: 1.5791... Generator Loss: 0.7702\n",
      "Epoch 1/1-Step 1320... Discriminator Loss: 1.5233... Generator Loss: 0.9316\n",
      "Epoch 1/1-Step 1330... Discriminator Loss: 1.6127... Generator Loss: 0.7162\n",
      "Epoch 1/1-Step 1340... Discriminator Loss: 1.3950... Generator Loss: 1.0200\n",
      "Epoch 1/1-Step 1350... Discriminator Loss: 1.6048... Generator Loss: 0.9939\n",
      "Epoch 1/1-Step 1360... Discriminator Loss: 1.5611... Generator Loss: 0.7043\n",
      "Epoch 1/1-Step 1370... Discriminator Loss: 1.2425... Generator Loss: 0.6983\n",
      "Epoch 1/1-Step 1380... Discriminator Loss: 1.4157... Generator Loss: 0.8952\n",
      "Epoch 1/1-Step 1390... Discriminator Loss: 1.5383... Generator Loss: 0.7594\n",
      "Epoch 1/1-Step 1400... Discriminator Loss: 1.3918... Generator Loss: 0.7269\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n32aWEiEzHMo7qLZzvww
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5cf85f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1410... Discriminator Loss: 1.5596... Generator Loss: 0.8909\n",
      "Epoch 1/1-Step 1420... Discriminator Loss: 1.2898... Generator Loss: 0.8900\n",
      "Epoch 1/1-Step 1430... Discriminator Loss: 1.2925... Generator Loss: 0.8084\n",
      "Epoch 1/1-Step 1440... Discriminator Loss: 1.5554... Generator Loss: 0.8446\n",
      "Epoch 1/1-Step 1450... Discriminator Loss: 1.5379... Generator Loss: 0.7816\n",
      "Epoch 1/1-Step 1460... Discriminator Loss: 1.5530... Generator Loss: 0.6510\n",
      "Epoch 1/1-Step 1470... Discriminator Loss: 1.4320... Generator Loss: 0.9174\n",
      "Epoch 1/1-Step 1480... Discriminator Loss: 1.2774... Generator Loss: 0.9591\n",
      "Epoch 1/1-Step 1490... Discriminator Loss: 1.5630... Generator Loss: 0.9092\n",
      "Epoch 1/1-Step 1500... Discriminator Loss: 1.3316... Generator Loss: 0.9412\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db618c358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1510... Discriminator Loss: 1.5263... Generator Loss: 1.1853\n",
      "Epoch 1/1-Step 1520... Discriminator Loss: 1.4308... Generator Loss: 0.7497\n",
      "Epoch 1/1-Step 1530... Discriminator Loss: 1.2168... Generator Loss: 0.8292\n",
      "Epoch 1/1-Step 1540... Discriminator Loss: 1.7063... Generator Loss: 0.8995\n",
      "Epoch 1/1-Step 1550... Discriminator Loss: 1.4971... Generator Loss: 0.7941\n",
      "Epoch 1/1-Step 1560... Discriminator Loss: 1.3980... Generator Loss: 0.8901\n",
      "Epoch 1/1-Step 1570... Discriminator Loss: 1.5415... Generator Loss: 0.7382\n",
      "Epoch 1/1-Step 1580... Discriminator Loss: 1.7079... Generator Loss: 0.7632\n",
      "Epoch 1/1-Step 1590... Discriminator Loss: 1.7465... Generator Loss: 0.9167\n",
      "Epoch 1/1-Step 1600... Discriminator Loss: 1.5987... Generator Loss: 0.7269\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\njFcsSQf03TOR6KP9TQh
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dada12b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1610... Discriminator Loss: 1.7159... Generator Loss: 0.5133\n",
      "Epoch 1/1-Step 1620... Discriminator Loss: 1.6374... Generator Loss: 1.0111\n",
      "Epoch 1/1-Step 1630... Discriminator Loss: 1.7641... Generator Loss: 0.6404\n",
      "Epoch 1/1-Step 1640... Discriminator Loss: 1.2147... Generator Loss: 0.8429\n",
      "Epoch 1/1-Step 1650... Discriminator Loss: 1.3339... Generator Loss: 0.8591\n",
      "Epoch 1/1-Step 1660... Discriminator Loss: 1.8727... Generator Loss: 0.6742\n",
      "Epoch 1/1-Step 1670... Discriminator Loss: 1.5134... Generator Loss: 0.7987\n",
      "Epoch 1/1-Step 1680... Discriminator Loss: 1.4782... Generator Loss: 0.7062\n",
      "Epoch 1/1-Step 1690... Discriminator Loss: 1.6095... Generator Loss: 0.7445\n",
      "Epoch 1/1-Step 1700... Discriminator Loss: 1.7461... Generator Loss: 0.9126\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dadc7bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1710... Discriminator Loss: 2.0156... Generator Loss: 0.3896\n",
      "Epoch 1/1-Step 1720... Discriminator Loss: 1.7540... Generator Loss: 0.6926\n",
      "Epoch 1/1-Step 1730... Discriminator Loss: 1.7513... Generator Loss: 0.6261\n",
      "Epoch 1/1-Step 1740... Discriminator Loss: 1.6404... Generator Loss: 0.7288\n",
      "Epoch 1/1-Step 1750... Discriminator Loss: 1.5012... Generator Loss: 0.8576\n",
      "Epoch 1/1-Step 1760... Discriminator Loss: 1.6337... Generator Loss: 0.8478\n",
      "Epoch 1/1-Step 1770... Discriminator Loss: 1.2683... Generator Loss: 0.8922\n",
      "Epoch 1/1-Step 1780... Discriminator Loss: 1.5020... Generator Loss: 0.9028\n",
      "Epoch 1/1-Step 1790... Discriminator Loss: 1.4300... Generator Loss: 0.8794\n",
      "Epoch 1/1-Step 1800... Discriminator Loss: 1.4853... Generator Loss: 0.7850\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOKfX7cpCjynoBCd41ZP
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad5907f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1810... Discriminator Loss: 1.4165... Generator Loss: 0.8949\n",
      "Epoch 1/1-Step 1820... Discriminator Loss: 1.5920... Generator Loss: 0.8032\n",
      "Epoch 1/1-Step 1830... Discriminator Loss: 1.4096... Generator Loss: 0.7035\n",
      "Epoch 1/1-Step 1840... Discriminator Loss: 1.5037... Generator Loss: 0.7921\n",
      "Epoch 1/1-Step 1850... Discriminator Loss: 2.2428... Generator Loss: 0.6911\n",
      "Epoch 1/1-Step 1860... Discriminator Loss: 1.6131... Generator Loss: 0.7686\n",
      "Epoch 1/1-Step 1870... Discriminator Loss: 1.5115... Generator Loss: 0.8898\n",
      "Epoch 1/1-Step 1880... Discriminator Loss: 1.4410... Generator Loss: 0.8098\n",
      "Epoch 1/1-Step 1890... Discriminator Loss: 1.4782... Generator Loss: 0.7205\n",
      "Epoch 1/1-Step 1900... Discriminator Loss: 1.5556... Generator Loss: 0.9096\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad745da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 1910... Discriminator Loss: 1.5507... Generator Loss: 0.7454\n",
      "Epoch 1/1-Step 1920... Discriminator Loss: 1.4829... Generator Loss: 0.9165\n",
      "Epoch 1/1-Step 1930... Discriminator Loss: 1.4526... Generator Loss: 0.9627\n",
      "Epoch 1/1-Step 1940... Discriminator Loss: 1.6984... Generator Loss: 0.8435\n",
      "Epoch 1/1-Step 1950... Discriminator Loss: 1.2613... Generator Loss: 0.7659\n",
      "Epoch 1/1-Step 1960... Discriminator Loss: 1.6145... Generator Loss: 0.5836\n",
      "Epoch 1/1-Step 1970... Discriminator Loss: 1.7177... Generator Loss: 0.8831\n",
      "Epoch 1/1-Step 1980... Discriminator Loss: 1.5243... Generator Loss: 0.8181\n",
      "Epoch 1/1-Step 1990... Discriminator Loss: 1.4158... Generator Loss: 0.7466\n",
      "Epoch 1/1-Step 2000... Discriminator Loss: 1.4195... Generator Loss: 0.6145\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsbdlxHlZrj2ce7nzf3BPZzUFsiowsyXKswQrkDFKA\nGIIUIzASIfqTBAoSwJbzw0mABLD/JPEvI0bsRAEcW4pjwYIiWBYUy7IyyKJGimx2s1/3e91vuvM9\n8zl7WvlRtXZ9l2/o+9jkJVt3FfBw99vn7GntfXZ966uqr4y1lrx583a5LPhWn4A3b94u3vwP35u3\nS2j+h+/N2yU0/8P35u0Smv/he/N2Cc3/8L15u4Tmf/jevF1C+1A/fGPMjxhj3jTGvG2M+dlv1El5\n8+btm2vm603gMcaERPQWEf0wEd0jot8hop+01n75G3d63rx5+2ZY9CG2/S4ietta+w4RkTHmHxDR\njxHRU3/4rUZiB90m8fd5nbGm/jwQ/BEYXRficmDke7pOl/QFVpZlvVwUBRERZUUJn1f8F156ZQXL\nsljB55V8fvY1+fj5kFEQFYQhERElSaLXEEayJZw57PRkdCrHqx47tsHD4FkYdx66zo3bmbH8IHzn\nzgN2jo7BPvFz/ovjB4tP3KYeNzihOI71UxPKt+DZkLFsBDnsRQ8UyHIE+3TL+D3r7qPV8aUz95nX\nF2X12Mf2Cc8DEVEuD0z5hOuGo1CBz1jlPn/c8T7pGSPS5yyKw3qdG7cw5L+z6ZyWq+zMaD/JPswP\n/yoRvQ//v0dEf+pZGwy6TfoP/53v4wPL/UkrvVHNlC+onepptRP9vN9KiYioBT+khoyBsfpAnI5O\n6uW9g2MiInpwoOtO50v+u9RtxquiXp7I8nSlL4uZLBdwJ00gD2MKP+y0oefW7RER0c0bL9brev11\nIiJKSG/eKtOd/sNf+cd8vGWmn8t5RvDDhXtPqXzQSnVlRwamk+i6QQM2qh/ixx9mfI8tc3gBPeFH\nvJIBOZnr+M3henLZJgj02EZefo1Oq163e2W3Xk7SLl8j6bg2W7zuE+29el1kdYxaAR9orZfW6zZ6\n7GSSAH6ky5X8Xep15Ss9d3k2jkZz3UacRp7pNc6W+mw8GvP9mcB1r+QtsIS3wf5ctxmvqjPfI9KX\nRI6/fHibNzs8Hpvbw3rdld0dIiLqdbaIiOhXfvW36Dz2TSf3jDE/bYz5gjHmC3N4mL158/atsw/j\n8e8T0XX4/zVZd8astX+biP42EdH17b7tNfh11m7x26sTN+vvtsVzrnV0Xb+lp9hKGc4k4JKctwsj\nfU3uzNXr3tzifS0X/XpdRfwCWgGcXuX6Nnfv5el8oRe7NyYiooOReodJwceuIoWpR4Web9FsExHR\n8MZ2vW53h71/CMjh3nvqxVbikahS7xCJs+w0dN/rHfWGa03+wmZP1211eXmnqx5wu6Pb53K9ZaHX\nrbMCPbnRVFGRAwfNll6vm0K9s68e8v1TfcGPZPc2VI+/NHwerWFXz+1jN2CffM4nD0/rdaucj5NW\ns3pdt6n33I3Hzoae2/aQ730aAtRfyEUCGg5KXc6WfO3jY5hSlDJlAHS6nOnndx7wRZ4sYPomCKcA\nRPXusS6/N+HlvaWum8viaaHrCgNTDnky41SfjdaAr9c6CPiBIJ/tw3j83yGiV4wxLxhjEiL6CSL6\npQ+xP2/evF2Qfd0e31pbGGP+YyL6VSIKiejvWmu/9MyDBYbWxdO3ZS42aOk8r9vgN3S/pR6/legr\nrJHyeyqJ9bQDy28/W+qcLWzr540Ge3pj1OObgN+iBgmlOIHPeb2p1BvmC97/dDSq1+09FBQw1WPv\nrxRtHITs8U1rUK9rNnl+1uvp+RyO1Hvki8JdWL2ulfJYXRn06nXXBu162Xny62vq3Xf6fD1DmNd3\nI/UUVcHHzEv0+DzWFq7b6iGpkci46m6okOnbjUiPfa+jHv/2hMf6GLxYHvO5d3fUyw/6t+rlg5mQ\nr5Gex0LONyoUcSXgDYeCcDY39D722vK8WN1PIB+Hpfo8u9D9lCFfXB8JFUfuTfXCpwtAiPI89QB1\nZsLeIQk4SHWfV+SQvwcT+juCNmIYXwO0TJXxd6cTHd+5ILYkfhL5/HT7MFCfrLW/QkS/8mH24c2b\nt4s3n7nnzdsltA/l8Z/XKmtpmQlkc7xZSwmeNGa4mIYKG2MghUIHZAAKlTnD7GKlRFwF8DVMGLZH\nAEUDYUBMiTF3/dwQbxMAaec4yKjSUEp5yqTc/sN79br9AyWf7gcMyU4P3tNze59JsM3her1uOp7U\ny47Ewph8IORUUQAJlekXTM63MVjCutTtT6cRK5gOLSScFUe6z65MwwIgHudThZXjkmH2dK77LIXc\ny5D4Aj4wE8LrZAmEoeyHpkpqxhOduqyIB/v04aN6nYPoJ1bvcwax9sGAp1hncghkLPF5CBxpCuQp\nAbFbycmXy8fzBSoIWS5O9POHezxG9+Z6cEfaYWwf8xLc0UdA7h3I8ilsBGdJlXy+KJVIrWIew/Wd\nVC4Ft3i6eY/vzdsltAv1+NYSFfJ2Da0k6zTVq7YavJxCQkwKCSh1kg6QT+4tGqdKCJoAPbl4Q0gg\nCVwYD8J5Joc3pWR1WVJvZyQjLwl0yHr9DhERbaV6DV/N1HtPlvxmPowhiWO6T0RE76WKMNqAelxW\noT2THsd/MIEkXynJFdUklm6Sz/hzo6dGodFrTCWrrdfW8+gOhGiFfIvlRD3sw8MpERG9DyHNkZBc\nY/DyC/D+U8koGxW6bixE3WqiGwWZjmtp+F7Ojh/oNUooc9TSY2MSU0MSvRJAiEaIr6DQ41hBiFUF\nJ1wihBRSbgnPmCP3wBOjxyyFdDsC8u/uitdNYNfwuFEsO5hC5qpL5llhOA+O43ZQAeI6lDBzFRzx\n9/MzWzzVvMf35u0Smv/he/N2Ce1CoX4YBNRpMTzeWOO/g57C3KYj9xIg1RI4RZdhBVAykJi+gbgr\nFrM4mIzEimOADFbHPKFowxZPgINWj5NKLv7uzav1uusjhVr3hOhbNXUacjxh+D860dqBEPISXCw9\njhSyRkIyYpFNDNloPcnca8Z6jY6z67QUyq8NlUBLpR4Cxzd0x2wqnF4tdAwWMtVYwFhlMkX66qlu\nc4jJfolMXSKdvuWFK5LS71UwdSGpxcgzIGwrvqAOPBu7A8hU7PO9aEDgO5R9hpCJaBwhCPAe0gEo\nktyOKoEaBYHeWIgUwZRiINOM9Vi3OZDHoIBn7Agg/EKuB12vZqTic0lPWAaSV2pIFjK1w7yBZ5n3\n+N68XULzP3xv3i6hXSjUj8KI1gccB+82GYLGRk8hDhwDr9tgPblxcXWLtdsMdc7UXD/h2BXA03qP\neCA4jnVxXygGcgUSFstUc2a/sd5+e7CmyxPefxFpWvJ7BUP91URjscsuwNz6dCClVOBrBe/pAKY2\nsaQ1x1DC3BZI3FvTnNv1nY16uSHTj3yluHw5W8q16nU3+5puvO0iGhAhiWWo7o6hTBX2OXflzpgi\nK+W2JWD9YjSul10OAuYTVALRt3o6bVqHoqSmPBsBRD6s5CpYA+sKvmcGmXyMfcuiS9sm0lyF+RgK\najRdg3Jh5gtg6J3WQwnrYAZEM1fDD9dYPuHBrZ7wnwLSfHOJKMylaMhDfW/evD3VLtTjB8ZQR4Qq\nAvdGXKi3s6G8wcsQ1ukbzDlBg+ydvAXtGZUVKGV0GyFfIh4LveoZLZ/KZXzBfmpyD7KqpGDEEuYV\nQKbcgr16ZbRwJxSXkq2W8D0lsdzuMQPLSlZipjwdmUD3mYqnbzbUAw6EyOv31OM3WkruRUK2VZU+\nApFDDBE8FlglIuO1BV4sEo+zeaCx/weQpbeQ1Rl44kqQloXHr8whZ8KyO62gIMep8qQg0oLeLZvx\nGBUWiNKMx7iE/DdHIgb4QKA6k5CVq5Vew1KKeMZzXXcCWXr3JaPuzkrXncgup+DGx7CcudwAPQty\n3N+TlHz4u4501rHMpIw
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dada23c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2010... Discriminator Loss: 1.2077... Generator Loss: 0.8097\n",
      "Epoch 1/1-Step 2020... Discriminator Loss: 1.6748... Generator Loss: 0.6769\n",
      "Epoch 1/1-Step 2030... Discriminator Loss: 1.2325... Generator Loss: 1.1092\n",
      "Epoch 1/1-Step 2040... Discriminator Loss: 1.6127... Generator Loss: 0.6180\n",
      "Epoch 1/1-Step 2050... Discriminator Loss: 1.9700... Generator Loss: 0.4623\n",
      "Epoch 1/1-Step 2060... Discriminator Loss: 1.7036... Generator Loss: 0.7239\n",
      "Epoch 1/1-Step 2070... Discriminator Loss: 1.7783... Generator Loss: 0.7530\n",
      "Epoch 1/1-Step 2080... Discriminator Loss: 1.6162... Generator Loss: 0.6914\n",
      "Epoch 1/1-Step 2090... Discriminator Loss: 1.3737... Generator Loss: 0.8860\n",
      "Epoch 1/1-Step 2100... Discriminator Loss: 1.6765... Generator Loss: 0.8732\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad662160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2110... Discriminator Loss: 1.5207... Generator Loss: 0.9026\n",
      "Epoch 1/1-Step 2120... Discriminator Loss: 1.5098... Generator Loss: 0.8168\n",
      "Epoch 1/1-Step 2130... Discriminator Loss: 1.8388... Generator Loss: 0.3753\n",
      "Epoch 1/1-Step 2140... Discriminator Loss: 1.7838... Generator Loss: 0.6771\n",
      "Epoch 1/1-Step 2150... Discriminator Loss: 1.6525... Generator Loss: 0.9387\n",
      "Epoch 1/1-Step 2160... Discriminator Loss: 1.4477... Generator Loss: 0.7868\n",
      "Epoch 1/1-Step 2170... Discriminator Loss: 1.5385... Generator Loss: 0.5039\n",
      "Epoch 1/1-Step 2180... Discriminator Loss: 1.2671... Generator Loss: 1.0103\n",
      "Epoch 1/1-Step 2190... Discriminator Loss: 1.3645... Generator Loss: 0.7141\n",
      "Epoch 1/1-Step 2200... Discriminator Loss: 1.4519... Generator Loss: 0.7778\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBKJSwrDRBBACGyYnrjG
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad9deb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2210... Discriminator Loss: 1.6324... Generator Loss: 0.6715\n",
      "Epoch 1/1-Step 2220... Discriminator Loss: 1.6588... Generator Loss: 0.6475\n",
      "Epoch 1/1-Step 2230... Discriminator Loss: 1.6004... Generator Loss: 0.6738\n",
      "Epoch 1/1-Step 2240... Discriminator Loss: 1.7856... Generator Loss: 0.4941\n",
      "Epoch 1/1-Step 2250... Discriminator Loss: 2.0594... Generator Loss: 0.8119\n",
      "Epoch 1/1-Step 2260... Discriminator Loss: 1.5741... Generator Loss: 0.6967\n",
      "Epoch 1/1-Step 2270... Discriminator Loss: 1.5061... Generator Loss: 0.7997\n",
      "Epoch 1/1-Step 2280... Discriminator Loss: 1.6878... Generator Loss: 0.5264\n",
      "Epoch 1/1-Step 2290... Discriminator Loss: 1.5639... Generator Loss: 0.9190\n",
      "Epoch 1/1-Step 2300... Discriminator Loss: 1.4255... Generator Loss: 1.0749\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad2b64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2310... Discriminator Loss: 1.4793... Generator Loss: 0.7750\n",
      "Epoch 1/1-Step 2320... Discriminator Loss: 1.4188... Generator Loss: 0.7339\n",
      "Epoch 1/1-Step 2330... Discriminator Loss: 1.5441... Generator Loss: 0.6852\n",
      "Epoch 1/1-Step 2340... Discriminator Loss: 1.7626... Generator Loss: 0.8259\n",
      "Epoch 1/1-Step 2350... Discriminator Loss: 1.4334... Generator Loss: 1.2131\n",
      "Epoch 1/1-Step 2360... Discriminator Loss: 1.4948... Generator Loss: 0.8043\n",
      "Epoch 1/1-Step 2370... Discriminator Loss: 1.3628... Generator Loss: 0.9632\n",
      "Epoch 1/1-Step 2380... Discriminator Loss: 1.3724... Generator Loss: 0.7632\n",
      "Epoch 1/1-Step 2390... Discriminator Loss: 1.4073... Generator Loss: 1.0515\n",
      "Epoch 1/1-Step 2400... Discriminator Loss: 1.6425... Generator Loss: 1.0943\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nD2ZIeshlB/A2Be+TjkU
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad247f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2410... Discriminator Loss: 1.2448... Generator Loss: 0.8053\n",
      "Epoch 1/1-Step 2420... Discriminator Loss: 1.5791... Generator Loss: 0.8825\n",
      "Epoch 1/1-Step 2430... Discriminator Loss: 1.4721... Generator Loss: 0.7852\n",
      "Epoch 1/1-Step 2440... Discriminator Loss: 1.1960... Generator Loss: 0.6903\n",
      "Epoch 1/1-Step 2450... Discriminator Loss: 1.6489... Generator Loss: 0.7352\n",
      "Epoch 1/1-Step 2460... Discriminator Loss: 1.6383... Generator Loss: 0.5401\n",
      "Epoch 1/1-Step 2470... Discriminator Loss: 1.7272... Generator Loss: 0.7256\n",
      "Epoch 1/1-Step 2480... Discriminator Loss: 1.5266... Generator Loss: 0.6885\n",
      "Epoch 1/1-Step 2490... Discriminator Loss: 1.3964... Generator Loss: 0.8389\n",
      "Epoch 1/1-Step 2500... Discriminator Loss: 1.6126... Generator Loss: 1.0305\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacd24908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2510... Discriminator Loss: 1.4988... Generator Loss: 0.8729\n",
      "Epoch 1/1-Step 2520... Discriminator Loss: 1.7505... Generator Loss: 0.9465\n",
      "Epoch 1/1-Step 2530... Discriminator Loss: 1.6174... Generator Loss: 0.5799\n",
      "Epoch 1/1-Step 2540... Discriminator Loss: 1.5262... Generator Loss: 0.7723\n",
      "Epoch 1/1-Step 2550... Discriminator Loss: 1.3262... Generator Loss: 0.9153\n",
      "Epoch 1/1-Step 2560... Discriminator Loss: 1.5274... Generator Loss: 0.6387\n",
      "Epoch 1/1-Step 2570... Discriminator Loss: 1.6057... Generator Loss: 0.7459\n",
      "Epoch 1/1-Step 2580... Discriminator Loss: 1.3637... Generator Loss: 0.9186\n",
      "Epoch 1/1-Step 2590... Discriminator Loss: 1.9831... Generator Loss: 0.5967\n",
      "Epoch 1/1-Step 2600... Discriminator Loss: 1.4500... Generator Loss: 0.7985\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWWYRYpEr2ZghO3FFQhu
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad4d0c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2610... Discriminator Loss: 1.6789... Generator Loss: 0.6164\n",
      "Epoch 1/1-Step 2620... Discriminator Loss: 1.6104... Generator Loss: 0.7157\n",
      "Epoch 1/1-Step 2630... Discriminator Loss: 1.6175... Generator Loss: 0.6879\n",
      "Epoch 1/1-Step 2640... Discriminator Loss: 1.4824... Generator Loss: 0.7394\n",
      "Epoch 1/1-Step 2650... Discriminator Loss: 1.5183... Generator Loss: 0.7989\n",
      "Epoch 1/1-Step 2660... Discriminator Loss: 1.4074... Generator Loss: 0.8263\n",
      "Epoch 1/1-Step 2670... Discriminator Loss: 1.7016... Generator Loss: 1.0771\n",
      "Epoch 1/1-Step 2680... Discriminator Loss: 1.3518... Generator Loss: 0.9976\n",
      "Epoch 1/1-Step 2690... Discriminator Loss: 1.7289... Generator Loss: 0.8812\n",
      "Epoch 1/1-Step 2700... Discriminator Loss: 1.4264... Generator Loss: 0.7199\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacee7e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2710... Discriminator Loss: 1.9444... Generator Loss: 0.5275\n",
      "Epoch 1/1-Step 2720... Discriminator Loss: 1.5196... Generator Loss: 0.5546\n",
      "Epoch 1/1-Step 2730... Discriminator Loss: 1.5974... Generator Loss: 0.6296\n",
      "Epoch 1/1-Step 2740... Discriminator Loss: 1.3963... Generator Loss: 0.8431\n",
      "Epoch 1/1-Step 2750... Discriminator Loss: 1.4813... Generator Loss: 0.7904\n",
      "Epoch 1/1-Step 2760... Discriminator Loss: 1.6590... Generator Loss: 0.5457\n",
      "Epoch 1/1-Step 2770... Discriminator Loss: 1.4524... Generator Loss: 0.8106\n",
      "Epoch 1/1-Step 2780... Discriminator Loss: 1.8156... Generator Loss: 0.7884\n",
      "Epoch 1/1-Step 2790... Discriminator Loss: 1.4598... Generator Loss: 0.8142\n",
      "Epoch 1/1-Step 2800... Discriminator Loss: 1.4186... Generator Loss: 0.6926\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nuOi5OzJzBkU4We5QGhS
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacad0940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2810... Discriminator Loss: 1.7492... Generator Loss: 0.5969\n",
      "Epoch 1/1-Step 2820... Discriminator Loss: 1.3586... Generator Loss: 0.6138\n",
      "Epoch 1/1-Step 2830... Discriminator Loss: 1.4274... Generator Loss: 0.8394\n",
      "Epoch 1/1-Step 2840... Discriminator Loss: 1.4150... Generator Loss: 1.0987\n",
      "Epoch 1/1-Step 2850... Discriminator Loss: 1.3768... Generator Loss: 0.9213\n",
      "Epoch 1/1-Step 2860... Discriminator Loss: 1.5966... Generator Loss: 0.9126\n",
      "Epoch 1/1-Step 2870... Discriminator Loss: 1.4296... Generator Loss: 0.7678\n",
      "Epoch 1/1-Step 2880... Discriminator Loss: 1.3032... Generator Loss: 0.7903\n",
      "Epoch 1/1-Step 2890... Discriminator Loss: 1.7742... Generator Loss: 0.7110\n",
      "Epoch 1/1-Step 2900... Discriminator Loss: 1.6297... Generator Loss: 0.7091\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacd589b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 2910... Discriminator Loss: 1.7591... Generator Loss: 0.9087\n",
      "Epoch 1/1-Step 2920... Discriminator Loss: 1.5827... Generator Loss: 0.6977\n",
      "Epoch 1/1-Step 2930... Discriminator Loss: 1.4514... Generator Loss: 0.8571\n",
      "Epoch 1/1-Step 2940... Discriminator Loss: 1.4127... Generator Loss: 0.7843\n",
      "Epoch 1/1-Step 2950... Discriminator Loss: 1.6538... Generator Loss: 0.5601\n",
      "Epoch 1/1-Step 2960... Discriminator Loss: 1.4198... Generator Loss: 0.7983\n",
      "Epoch 1/1-Step 2970... Discriminator Loss: 1.2191... Generator Loss: 0.6956\n",
      "Epoch 1/1-Step 2980... Discriminator Loss: 1.3358... Generator Loss: 0.7731\n",
      "Epoch 1/1-Step 2990... Discriminator Loss: 1.5513... Generator Loss: 0.8735\n",
      "Epoch 1/1-Step 3000... Discriminator Loss: 1.3683... Generator Loss: 0.9643\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKjEC56HNw0QdWfQEes5
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac836390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3010... Discriminator Loss: 1.7157... Generator Loss: 0.8457\n",
      "Epoch 1/1-Step 3020... Discriminator Loss: 1.5131... Generator Loss: 0.9042\n",
      "Epoch 1/1-Step 3030... Discriminator Loss: 1.6572... Generator Loss: 0.7466\n",
      "Epoch 1/1-Step 3040... Discriminator Loss: 1.4736... Generator Loss: 0.9716\n",
      "Epoch 1/1-Step 3050... Discriminator Loss: 1.4429... Generator Loss: 0.7000\n",
      "Epoch 1/1-Step 3060... Discriminator Loss: 1.5794... Generator Loss: 0.6830\n",
      "Epoch 1/1-Step 3070... Discriminator Loss: 1.2576... Generator Loss: 0.8349\n",
      "Epoch 1/1-Step 3080... Discriminator Loss: 1.5124... Generator Loss: 0.9520\n",
      "Epoch 1/1-Step 3090... Discriminator Loss: 1.2616... Generator Loss: 0.7677\n",
      "Epoch 1/1-Step 3100... Discriminator Loss: 1.4092... Generator Loss: 0.8760\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacb332b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3110... Discriminator Loss: 1.5141... Generator Loss: 0.7706\n",
      "Epoch 1/1-Step 3120... Discriminator Loss: 1.5931... Generator Loss: 0.8096\n",
      "Epoch 1/1-Step 3130... Discriminator Loss: 1.3764... Generator Loss: 0.7593\n",
      "Epoch 1/1-Step 3140... Discriminator Loss: 1.5350... Generator Loss: 0.8338\n",
      "Epoch 1/1-Step 3150... Discriminator Loss: 1.4308... Generator Loss: 0.7347\n",
      "Epoch 1/1-Step 3160... Discriminator Loss: 1.9351... Generator Loss: 0.8502\n",
      "Epoch 1/1-Step 3170... Discriminator Loss: 1.4762... Generator Loss: 0.6605\n",
      "Epoch 1/1-Step 3180... Discriminator Loss: 1.2701... Generator Loss: 0.6483\n",
      "Epoch 1/1-Step 3190... Discriminator Loss: 1.6003... Generator Loss: 0.4033\n",
      "Epoch 1/1-Step 3200... Discriminator Loss: 1.4458... Generator Loss: 0.8985\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsJFl2HnZubLlnvn2rqq6lu6f3mZ6eHs6IM6SHpEjQ\noiUasE2TNgTBIsA/XmjYgEX7hxfABuQ/svWLsGDJpmGZiyXRIgRRNEGRNmya1OzTM93TS3XXXm/L\nl3tmZGzXP+65cb7s96r6VS+PU3z3AIUXdTMj4saNyLjf/c4531Faa3LmzNn5Mu/PugPOnDk7e3M/\nfGfOzqG5H74zZ+fQ3A/fmbNzaO6H78zZOTT3w3fm7Bya++E7c3YO7SP98JVSP62UelMp9Y5S6lc+\nrk45c+bskzX1YQN4lFI+Eb1FRD9JRHeI6KtE9Ata69c/vu45c+bsk7DgI+z7Q0T0jtb6XSIipdRv\nENHPEtEDf/gry0v60s4OERF5vk+8X/l5URRERKR1cayNiChJEiIimk3jsi1OUiIims/nZVsO+/ie\nATWVSlS2Net1IiKq818ioiCSzz3bJ+gbbB4zfHlqOHeeZ+Yv95uIaB6bvieptBHJwedZTkREaZqW\nbWlq2jI+HhERvq7tNfqB3E5FZnzTPC/bkkTGqChy7rv096MY3kcF1+N5ZttfGEuz7UFb4Av4tNcT\nhqG0+ebaaq2GHMfzy2077kUmY1SOf47XqPkceG45jh1DpaQ/yn4XBh2fsYzPic+B5x0H0wV8Xj7r\nMFYZ9zODayjg/tnj6xOe74DHZzgZ02w+f8jTauyj/PAvENFt+P8dIvrCw3a4tLNDv/db/ysREdWa\nLSIi8qNK+XkST83fRH7Yk9Go3L596y4REb322vfLtrdu3CEionfeu1G29SfjcrvTMA/K05cvl20/\n/LlXiIjo5c99tmzbuHSx3I64Tz48EF7A2ycsjnL4ESfTifS9f0hERN07t8q262++QUREd3bvwRHk\nAX93r0dERPfu75dtu3tdIiI67PfKtkLLA9Hga+ysbsoRAzO+u91+2Xb77rvl9nhsjpVl0vfyJfAA\nFKj4IcOnyv548UGvwAuoHplra8KLtxqZz6uBXPf6UqvcbvMLeXtru2xb7qwREdGLP/qqXGNN9klm\n5jrGvYOybTA4IiKi4WAo18gvvE5Nnru1pXa53VldJiKioCKTQlStmY1CxmUwlmfsqGfGEl/wNT4+\nvlenqdyz8cw845mW0eyNzPO/f9At2/D5z3hyS2HiazdN39baK0RE9Bu//3t0GvvEyT2l1C8ppb6m\nlPpat9f74B2cOXP2idtHmfHvEtEl+P9FblswrfXfIaK/Q0T0ymc+rZvL60REFNXMG1UhzArNrFAB\nSBsG1XK7uz8gIqIDmC339m6Yfb1Z2fbcFZn5vvyymd1f+azM7k88+wIRETXX1uTcVTmPndMQUinv\nOHqykBW/Z6+LiKjC27WKzGzV0Lz1202ZAY9603L73j3zcgxjeatHqdleDqQPy/yGJyK6uGPQSqW5\nVbbdPTBo4048KNt0IccM+Fi+L32zy69wYckgc0PA16tgGjthWMgHxBAyBO3AsqpV4dk2lf60SI75\nBI9NJ4Rz85JuuSnXHVQF9g8Lc/+nShBOg8e/HshxIkYZ2ytLZdv29ob0Y808n8qX+0M8RnZ5REQ0\nGAmKqNw13z3a25NzN+xysiaHCeSYdhnihfLc9cbmGm7chOf73m65PRkalDH35dwRL3dUwr+ZU3J2\nH2XG/yoRPa2UuqqUiojo54nodz7C8Zw5c3ZG9qFnfK11ppT694jo94jIJ6K/p7X+3sP2UcqjkN/S\nHs/uwKGQr82bFUkffIONjswbNR3J+nerYw7w8pdfKds+98oPl9tXnvk0ERHVltblPHWzNvRgjbnQ\nEXtqnPHlIvCCeNeTkYH9qqcAWfhmnRZV5Hu1W/KG/9bXDUeg5vJWr2szM+4syeyxvbFableZ8Hpr\n92bZ9uat94iI6IB5BiKiWlWucWvdrGUvb3bkcybTAphpD49kVk6YcMwymfmIr703FMQVT2SfGfM1\nI+CbIkYbVRxKQDMrS01zaOBO4pkZj1DJrJnFggwTXgsXsaAnrzBj3W7LrLqybK57Z0ueh6VlGcuI\nuSflCwegPfMzSYEczbSQr6uMHnQs637Lh9QASXaWlsttiwTs74CIaO/Q8BPTsaC0ZCg/0XSc8bmB\nGJ6b38ec+QMNqORh9lGgPmmt/ykR/dOPcgxnzpydvbnIPWfOzqF9pBn/kU2pEgJZ7LzgR2YXVZEJ\njBp0hdy4ef01IiJabQos/MrnjAfxpR/6sbKttSxuoCpDfK8iRFAJ8RHen+CoV+iLtf08Aerj+1Mp\nWZrY/f1QYGNYNVCy1hIXUr0tLpsiM1C/Hcpxgoo5zkpTjpPnMkbXbxtYf6srsJ60gbyX1uW6v/L5\nZ8vty2vm/DtbQpZNmFzqgbvorfful9uHQ9O3PJV7VmsYKNsfCszd2xeoetA17TnA5LHtO/rpVbPc\ntq5KjOEocjMeQ3Bvoc1n5pxpKu5UYsIvCmSJVK0wgQlxGyoQgrn0tWtZRpRxCQsrUCDRGPYXsE+e\nmGuIJ7JTJUSS0VxbLZIxiDxzzHoAfnotS6iqx+MSwLPBz2D6iHF4bsZ35uwc2tnO+CSzoBBnEJnE\nARI5RC4d3L5Tbsc9E5Dx6kufKtue+/yXiYios3O1bPMrLdg2M57yTyDyHhaO9/7P9UO+u4ACYPZn\nV5YXyExtA0Oiqsz41bq4oCZTM0PC5FAGmFSbss8ogdmQCbQnYXZ//poZj2eflXF5+tlrclAmgQog\nl/o9Q6DNIGrwApBgR10z/v1D6S+xqywnQQ79vszK3/6eifG6sy9k5YiDV0IYNw3k3sHQfDeN5RpD\nZcbt5i3xGHfqQpwVuZkZGxCYE/JMvgwBOq2mQRY+BBQVePs4PM+D6b2MCgSUlc2FRJzNDMpIAKlO\npuZzJAQD/zjBaSPuzDWae7K6LP2db8v4FxzAkwi4Kl2wAR2PhH2YuRnfmbNzaO6H78zZObQzJ/cs\nLJYoKID6/FkGMc3vcmw7EVFYGP/l1RdeKtta6yZqLQDorCAaitgH+4GwHrtp+/P+vp9qZ/TjG/il\nAc55oYGsQU36G0YC0dPYwDkvleVOlaPAxolAycOR4L160xz/2WsSsfiZpy4QEdH2hR35HiyBZnMD\nt9NM3v2eMiRYBZYm7RosKZqmT0Us92fKywINy4NqKNe7w3ECk7l8PuW4+hFEJ+73hBC0fvd6VfoR\nD83+e7sC9f118Yu36iH3Xa6nxtGCjYZcd2Rj8JX0EfJgSJWXC0k4DPGnE1nCTCAnI56ZZQYm18x5\nOZPHAvUjeA4ULx+SmXxuE7cy2CeEsVxqm7HUuTyZGcdH7DKhWpxB5J4zZ84eU3M/fGfOzqGdOatv\nTTOQXsjj5oSDyVgg1Te+8+1y+8e+8BwREa09/XLZFrZMOKwXia9WAaQqoffpkT6dCJbscR5FuKQM\n6RU/seJEjRPTPoloOGXfM/iw7aeYCrrfF2b95RcMW//MC+KnX19mvzj4yqdzCfXMNC9DIIzCY4+E\nB0lSvidjGbEHIAIIPmXWWgE0juB6m3VOuGnLPt2e+TyGkNvuSKBzm2MCOp70t5ib69g/krTbuhyS\nltom0aYNjdWGOXetJku/iP3mC+nyEHuR5+acOaQrz3lZNB7JciSeiJeCcjMGPkEefWGWBxOA7ZVI\nxiVLOKloItedFZzXvxAwIOMaVM29qDQl5kH55vM0Nx6X0wrruBnfmbNzaGc+49v3kVU4UQufmbfX\neChv0wQitZ55yaTWVjoSmeeFZj480U9vTnS8E2WTPqnx9Gbfrg8k/k6K7Dvu28dtO9FXIIXTvvVj\nECjZXBFy8IuvGnGKzVVJNvHYT5xjXEEOKdC2Had8HkMNkWFhKJ9XGKW0W8KGZUxCTmcwcwGRV2N0\n0Aafe5XTlMcz+R5OVFY
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac425710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3210... Discriminator Loss: 1.5244... Generator Loss: 0.7205\n",
      "Epoch 1/1-Step 3220... Discriminator Loss: 1.4948... Generator Loss: 0.7835\n",
      "Epoch 1/1-Step 3230... Discriminator Loss: 1.7276... Generator Loss: 0.6534\n",
      "Epoch 1/1-Step 3240... Discriminator Loss: 1.7726... Generator Loss: 0.7834\n",
      "Epoch 1/1-Step 3250... Discriminator Loss: 1.4260... Generator Loss: 0.8314\n",
      "Epoch 1/1-Step 3260... Discriminator Loss: 1.3410... Generator Loss: 0.8914\n",
      "Epoch 1/1-Step 3270... Discriminator Loss: 1.3016... Generator Loss: 0.5628\n",
      "Epoch 1/1-Step 3280... Discriminator Loss: 1.1748... Generator Loss: 0.7641\n",
      "Epoch 1/1-Step 3290... Discriminator Loss: 1.5931... Generator Loss: 0.6746\n",
      "Epoch 1/1-Step 3300... Discriminator Loss: 1.4556... Generator Loss: 0.6975\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac3856a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3310... Discriminator Loss: 1.6819... Generator Loss: 0.7399\n",
      "Epoch 1/1-Step 3320... Discriminator Loss: 1.5214... Generator Loss: 0.6296\n",
      "Epoch 1/1-Step 3330... Discriminator Loss: 1.6875... Generator Loss: 0.5568\n",
      "Epoch 1/1-Step 3340... Discriminator Loss: 1.4709... Generator Loss: 0.7251\n",
      "Epoch 1/1-Step 3350... Discriminator Loss: 1.3091... Generator Loss: 0.9523\n",
      "Epoch 1/1-Step 3360... Discriminator Loss: 1.8484... Generator Loss: 0.5126\n",
      "Epoch 1/1-Step 3370... Discriminator Loss: 1.4600... Generator Loss: 0.7444\n",
      "Epoch 1/1-Step 3380... Discriminator Loss: 1.4035... Generator Loss: 0.7333\n",
      "Epoch 1/1-Step 3390... Discriminator Loss: 1.3388... Generator Loss: 0.6791\n",
      "Epoch 1/1-Step 3400... Discriminator Loss: 1.6287... Generator Loss: 0.7695\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJT23XNHQEAq0IEvVlXI
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac678fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3410... Discriminator Loss: 1.6047... Generator Loss: 0.9408\n",
      "Epoch 1/1-Step 3420... Discriminator Loss: 1.6477... Generator Loss: 0.6902\n",
      "Epoch 1/1-Step 3430... Discriminator Loss: 1.6805... Generator Loss: 0.7269\n",
      "Epoch 1/1-Step 3440... Discriminator Loss: 1.3426... Generator Loss: 0.7700\n",
      "Epoch 1/1-Step 3450... Discriminator Loss: 1.3337... Generator Loss: 0.9089\n",
      "Epoch 1/1-Step 3460... Discriminator Loss: 1.7269... Generator Loss: 0.4634\n",
      "Epoch 1/1-Step 3470... Discriminator Loss: 1.5530... Generator Loss: 0.6510\n",
      "Epoch 1/1-Step 3480... Discriminator Loss: 1.4688... Generator Loss: 0.5716\n",
      "Epoch 1/1-Step 3490... Discriminator Loss: 1.8038... Generator Loss: 0.7882\n",
      "Epoch 1/1-Step 3500... Discriminator Loss: 1.4409... Generator Loss: 0.9086\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac4f4358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3510... Discriminator Loss: 1.4427... Generator Loss: 1.1050\n",
      "Epoch 1/1-Step 3520... Discriminator Loss: 1.4272... Generator Loss: 0.7727\n",
      "Epoch 1/1-Step 3530... Discriminator Loss: 1.4006... Generator Loss: 0.8221\n",
      "Epoch 1/1-Step 3540... Discriminator Loss: 1.6652... Generator Loss: 0.5984\n",
      "Epoch 1/1-Step 3550... Discriminator Loss: 1.6726... Generator Loss: 1.0027\n",
      "Epoch 1/1-Step 3560... Discriminator Loss: 1.5106... Generator Loss: 0.5861\n",
      "Epoch 1/1-Step 3570... Discriminator Loss: 1.4999... Generator Loss: 0.6522\n",
      "Epoch 1/1-Step 3580... Discriminator Loss: 1.5615... Generator Loss: 0.6770\n",
      "Epoch 1/1-Step 3590... Discriminator Loss: 1.8390... Generator Loss: 0.6999\n",
      "Epoch 1/1-Step 3600... Discriminator Loss: 1.7242... Generator Loss: 0.9474\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMJVl2HnZuLC/e/jJfblVZS1dVd3X19ExPL7NxnRkO\nTZukKVGwDWIow6AsGjRgy6BsAxalH6YNL5BkQLb+WLBg0aIMWiRlihQhERRH5Iw55HD2nq236q6u\nNasq95dvXyLi+sc5N873pqqrs6a7c6aZ9wCNir4vXyw34sX57nfO+Y6x1pI3b96OlwXf7RPw5s3b\n0Zv/4XvzdgzN//C9eTuG5n/43rwdQ/M/fG/ejqH5H743b8fQ/A/fm7djaG/ph2+M+XFjzCvGmNeM\nMb/0dp2UN2/e3lkz32kCjzEmJKLLRPRjRHSLiL5ERD9rrX3x7Ts9b968vRMWvYXvfpiIXrPWvk5E\nZIz5dSL6aSJ6wx/+QrNh11eWiYgoz1MiIrL5tPh8POHt0UTHcqPfN5b/J83yYizL+cUVGAUvYaDb\n5j5bYWDk31DHDB6I/8FXor1nA/9HBw0cx71UDezbhHxuYajHtvD5wiLPDwWwn4znqtfvFmMHPd12\n82EzPY9c5sXOnTDOwb1gL8vze8bm55W/X4r03MtxTERElXJSjMWlkn5Htk2oj1og847XHcC9cPM2\nm+lzMJ1O+Lp6Hf07ON/cZvydFJ4N656Ne58CnJUgwM+N7E8/NzJXEdyzMNR5KeYIHiH3P5mFc8zx\n/uRz/+I5BXPzosdJYF6dpZlct/y7d9Cj/nBs7vnDb7O38sM/RUQ34f9vEdFHHvSF9ZVl+id/55eJ\niGg03CUionR4q/j8pavXiYjoW69eL8bGsV5DnPKF73TGxVhnwA9HpVwpxpqJbseGb1YU6KXWE97P\nYq2mY/IAExEZucEpwUMkN9emWTHmXl6GdCyEuz9L+fM40RtWafIxa4sN3Y8pF9s/9cmf5/NN9HzH\n/U0iIvr/PvupYuxf/dEfFdt7gwEREU0O9IcyGE7lHPRhiyLdZ7NSkWvQz3sj+XGRWgUetgX5cZ9b\nXizGLp1aJSKi9z32aDF24sxp/c4j54mIqOReaERUqraIiMiGOuflSrXYnsq83bm7UYxdv3aZr/Gz\nv1uMjYeDYnsyHRIR0cZevxgbjGd8vER/sCV5HjJwHrWqzn8g5zSZ6udxmT9vt/W6Ww19dsoJz4uB\nF2IuP94BOLHhWJ/b/mDEYyMdc7eiFOi81OHczj3C82rgWd7d3ycios0OvxD/7q/+Dh3G3nFyzxjz\nC8aYLxtjvrzf7b3Th/Pmzdsh7K14/A0iOgP/f1rG5sxa+w+J6B8SEb338cdspclvTROxl7yx9Y3i\nbz/z/AtERHRlQ+HcmXMniu3E8ht1uw9vScNvx9NLq3oirXax3SgJFE3UoyQlgaUAiNLZRD+vsjes\nwncc3Msmeuyx80x7e8XY1a2tYntzd5uIiFaWloqxp5b5emqLeo7TTN/wleYCERGFsXqPnQ5P66/9\nm88UY9/4ysvFtoPW+US91HSWypbuJ4rUe3cSWWoBFHXgIAH0RCWF8KWQvc9OpJ9nffZogzt3irEn\nSvpYPfPoE0REVK3rHMSVmhwP0EiixwlLfJzW8loxVhfv3gevOejocmec8/3b7ysK6E34Gaukej6h\nHHIynRVjTUBxbo7GsGQoy4OSwFKqCkugRBBiHOl9DGQ/eaBzDiCCJn32+AczPXYmcL1a0n0v1vQZ\nrK/yfNy+rT+zr77Cz0EgczaDa3mQvRWP/yUiumiMOW+MKRHRJ4nod9/kO968efsesO/Y41trU2PM\nXyOif03sVn7FWvvCA79kLNmQPU17pU5ERMO7uga6dpO95S6sVR9/D6yZxdMnuuyhxx5h0PHcxSeL\nsdMLC8V2RTxnFKpHSYUxHI7TYqw7UO9B4t1bzWYxVJc16LSvy5WOEGz9iZ5QavWNuyefV2G9viTb\n60vq8QdTIPLkVWxJz+36jT8lIqLL3/x6MTYZKEIphTyXs6nOWyYeKwQvn5T0PBs19k7lEqx/Zd2/\ntKzoqVbWtWwS88kl4IWiSOYy0+ve2L1bbD825jlYNOruIrknIRKHQJw5KNaq67FXl/ieXp+NirGt\n3R39ijxX6VTPw92K0Vi9u7sT06nO7xCIuijh88R1dEnmaJbDd1Kd65LsNQIiLhREWwHkZkp6PVMS\npJQOi7H9Dm+bTPdNKXAJVZ6XSabP4EuvXyUioma9IdcF332AvRWoT9ba3yOi33sr+/DmzdvRm8/c\n8+btGNpb8vgPa2EQUrPJkKRcklBYAOGkIY9hOHmlrLDSwaYLz50rxj7y3EeJiGhxQUMt5bk4shAu\nsE8XysmBrLEQZ3Zxc4y/x7HEo4EQHA84dHT+jkLbU4v1Yvv1ZV4qJLA2WVngUFZzUZcj4RRi2HLx\n/YkuPX773/wBERHtbSssxPh6JNdRi5EkZNjebui8nF1bKbYvPbLO/55XfnaxzQRcta5LHHfdRESJ\nhPOiOILPBeZGej6zVKFoqcrj04kStvUmz0EM4UW8HhfRxs8bVX5uUiDlRhPdznPedrkeRERlWT5M\ncoX/k4nLH4FAPRBi5TJ/H/MSmrJEiuErOYTpxgLxAwgRuvM0wf3zSxaFNM3q+ryEjrgcw3JlqPd8\nIEuo8eCgGHNhwdmIv5MeAbnnzZu3d6kdqccPgoAqEirLU048ePXV14vPh/I2XqxruOgcJE2crvFb\n//0f+GAxtnb2ESKaf5vOeQ8ZxzevFfJoLhkHSDkyJfmqTk+R8ZUpwROGTLysGfWkcazHObPO4ZfR\nTD1BImRZtaXhrXyg3qM/YM9490DDY89/8QoREWVAXGFSikNCKwvqqc8sc8LMEzI/RESXHjlbbJ8+\nzR6/vaznUWmxJw4gLIUQx6GnGBCM257LvIv0O7nsawyIy30a3Cd7EP8C72NJkMfG3d1iLAPEZsSD\nYzgrKsu9h2sYjiSpBwi9MiCldq0u1wVhTEnMmcvAhHObumMCAgklnIqXGED8eDrjvw0hpLkgCWVZ\nCJmrQPLud5j8DiGprVXl8+x1+TuHTcH3Ht+bt2No/ofvzdsxtCOF+kSWrJAwe9ucj/9nz2uufhIy\nhHnv6VYxVgcIfkrIsqUTmtFVkiwnA+8wAzngrtgF47LkYCmSNQDhyWWzhQj1ZXmQKZwzOe+nFuly\nJALYuCBx3xkQTi7TsLaisfLMKHzd2eT5eP7yl4ux/btM5mBMeKGipNtimfd5cVXn7X0XThER0RPn\nzxVjK6uaBdmQJVSpChlzAtEDjD3DHEQO6lcQ6jM8DSFHwAAhSBHvvwTE1zyR98aG0NrF+TFrM4Ds\nukjYW7iL5FZdOSyR+mPOf1iBXPt2Qwk2V5tg71NEZaGYh2BpY9w2XFcuD1cO152nenazlJ8jLDSK\n5ZhunonmMyunI6lLgXtyos2/idmEnxEs8HmQeY/vzdsxNP/D9+btGNqRQv08z2g65hjvbYG01zc1\nBXOxwTDr8dPKku/vQEz4cWb1S2WFtMquwjsMoL6Di3NQ38EhgGYBfN+6uC8wpG4/1mBduavr112X\noLAnnwksNQoL+0OGmrOZLhmQib38Khctfe6zn9XvHPB3KpD6u9rSst7Tbc4JeOKssvbrqyeJiKha\n078jiIsX1bozhZ824PONgH2O5ubNQVrIeZAxAynRJgSoLyWmEOafY8QfZKgl4LavQdltE6D3cs2d\nG+6AvzOG2P9UIix1WK60oDzbBQoMQP0gdPoBD45C5MioC4Q3qBmAsF/m3UKqcyCRCdSGmEFq8bTL\nBUgzuG5XwluVwirUFniQeY/vzdsxtKMl9wxRJnWRWx325IORvgVPLLG3XGuq1xztakyzWmMiI4C3\nsRHyzxp42yJRJx7L4OfFG/Vej877Eu+OIVF5c8+9UMUDGBDaQAWezPC55al6nPGQ8xfCvnrIHN76\nnV0uuexuKeHnbtIiijK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac367a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3610... Discriminator Loss: 1.7396... Generator Loss: 0.7128\n",
      "Epoch 1/1-Step 3620... Discriminator Loss: 1.3929... Generator Loss: 0.9645\n",
      "Epoch 1/1-Step 3630... Discriminator Loss: 1.4905... Generator Loss: 0.6534\n",
      "Epoch 1/1-Step 3640... Discriminator Loss: 1.5167... Generator Loss: 0.7207\n",
      "Epoch 1/1-Step 3650... Discriminator Loss: 1.7349... Generator Loss: 0.6661\n",
      "Epoch 1/1-Step 3660... Discriminator Loss: 1.7091... Generator Loss: 0.8844\n",
      "Epoch 1/1-Step 3670... Discriminator Loss: 1.5579... Generator Loss: 0.8142\n",
      "Epoch 1/1-Step 3680... Discriminator Loss: 1.4968... Generator Loss: 0.6100\n",
      "Epoch 1/1-Step 3690... Discriminator Loss: 1.6704... Generator Loss: 0.6108\n",
      "Epoch 1/1-Step 3700... Discriminator Loss: 1.4823... Generator Loss: 0.8318\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac118e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3710... Discriminator Loss: 1.4687... Generator Loss: 0.8287\n",
      "Epoch 1/1-Step 3720... Discriminator Loss: 1.3611... Generator Loss: 0.7837\n",
      "Epoch 1/1-Step 3730... Discriminator Loss: 1.5581... Generator Loss: 0.7379\n",
      "Epoch 1/1-Step 3740... Discriminator Loss: 1.2695... Generator Loss: 0.7258\n",
      "Epoch 1/1-Step 3750... Discriminator Loss: 1.6435... Generator Loss: 0.6423\n",
      "Epoch 1/1-Step 3760... Discriminator Loss: 1.4146... Generator Loss: 0.7574\n",
      "Epoch 1/1-Step 3770... Discriminator Loss: 1.3162... Generator Loss: 0.9410\n",
      "Epoch 1/1-Step 3780... Discriminator Loss: 1.5345... Generator Loss: 0.7912\n",
      "Epoch 1/1-Step 3790... Discriminator Loss: 1.5952... Generator Loss: 0.8790\n",
      "Epoch 1/1-Step 3800... Discriminator Loss: 1.3599... Generator Loss: 0.9576\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nT30onX3hCj9Ht7/+tWL
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac358be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3810... Discriminator Loss: 1.2795... Generator Loss: 0.7788\n",
      "Epoch 1/1-Step 3820... Discriminator Loss: 1.6969... Generator Loss: 0.8338\n",
      "Epoch 1/1-Step 3830... Discriminator Loss: 1.4994... Generator Loss: 0.8638\n",
      "Epoch 1/1-Step 3840... Discriminator Loss: 1.6924... Generator Loss: 0.7830\n",
      "Epoch 1/1-Step 3850... Discriminator Loss: 1.5655... Generator Loss: 0.7393\n",
      "Epoch 1/1-Step 3860... Discriminator Loss: 1.3982... Generator Loss: 0.8017\n",
      "Epoch 1/1-Step 3870... Discriminator Loss: 1.6797... Generator Loss: 0.6754\n",
      "Epoch 1/1-Step 3880... Discriminator Loss: 1.6183... Generator Loss: 0.7672\n",
      "Epoch 1/1-Step 3890... Discriminator Loss: 1.4654... Generator Loss: 0.7202\n",
      "Epoch 1/1-Step 3900... Discriminator Loss: 1.5022... Generator Loss: 0.7759\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac349358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 3910... Discriminator Loss: 1.2944... Generator Loss: 0.8291\n",
      "Epoch 1/1-Step 3920... Discriminator Loss: 1.3820... Generator Loss: 0.8784\n",
      "Epoch 1/1-Step 3930... Discriminator Loss: 1.5773... Generator Loss: 0.7978\n",
      "Epoch 1/1-Step 3940... Discriminator Loss: 1.6380... Generator Loss: 0.6990\n",
      "Epoch 1/1-Step 3950... Discriminator Loss: 1.2866... Generator Loss: 0.8188\n",
      "Epoch 1/1-Step 3960... Discriminator Loss: 1.5176... Generator Loss: 0.8348\n",
      "Epoch 1/1-Step 3970... Discriminator Loss: 1.3513... Generator Loss: 0.7374\n",
      "Epoch 1/1-Step 3980... Discriminator Loss: 1.3328... Generator Loss: 0.7456\n",
      "Epoch 1/1-Step 3990... Discriminator Loss: 1.4493... Generator Loss: 0.7595\n",
      "Epoch 1/1-Step 4000... Discriminator Loss: 1.7063... Generator Loss: 0.5705\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl59AfL1dMR6nGQsRt8X
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac67d588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4010... Discriminator Loss: 1.4494... Generator Loss: 0.5850\n",
      "Epoch 1/1-Step 4020... Discriminator Loss: 1.1800... Generator Loss: 0.5784\n",
      "Epoch 1/1-Step 4030... Discriminator Loss: 1.5047... Generator Loss: 0.7578\n",
      "Epoch 1/1-Step 4040... Discriminator Loss: 1.4166... Generator Loss: 0.9235\n",
      "Epoch 1/1-Step 4050... Discriminator Loss: 1.3354... Generator Loss: 0.5722\n",
      "Epoch 1/1-Step 4060... Discriminator Loss: 1.3027... Generator Loss: 0.8640\n",
      "Epoch 1/1-Step 4070... Discriminator Loss: 1.4077... Generator Loss: 0.7505\n",
      "Epoch 1/1-Step 4080... Discriminator Loss: 1.6606... Generator Loss: 0.5598\n",
      "Epoch 1/1-Step 4090... Discriminator Loss: 1.6864... Generator Loss: 0.8527\n",
      "Epoch 1/1-Step 4100... Discriminator Loss: 1.4432... Generator Loss: 0.6975\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacac4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4110... Discriminator Loss: 1.4610... Generator Loss: 0.5814\n",
      "Epoch 1/1-Step 4120... Discriminator Loss: 1.3286... Generator Loss: 0.9355\n",
      "Epoch 1/1-Step 4130... Discriminator Loss: 1.3846... Generator Loss: 0.7204\n",
      "Epoch 1/1-Step 4140... Discriminator Loss: 1.6672... Generator Loss: 0.9865\n",
      "Epoch 1/1-Step 4150... Discriminator Loss: 1.1974... Generator Loss: 0.9359\n",
      "Epoch 1/1-Step 4160... Discriminator Loss: 1.4319... Generator Loss: 0.5810\n",
      "Epoch 1/1-Step 4170... Discriminator Loss: 1.4379... Generator Loss: 0.8376\n",
      "Epoch 1/1-Step 4180... Discriminator Loss: 1.6290... Generator Loss: 0.6633\n",
      "Epoch 1/1-Step 4190... Discriminator Loss: 1.4695... Generator Loss: 0.9870\n",
      "Epoch 1/1-Step 4200... Discriminator Loss: 1.3221... Generator Loss: 0.7868\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw71qe3DIsedaTa/ReQp
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dace31358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4210... Discriminator Loss: 1.5551... Generator Loss: 0.8544\n",
      "Epoch 1/1-Step 4220... Discriminator Loss: 1.7906... Generator Loss: 0.5292\n",
      "Epoch 1/1-Step 4230... Discriminator Loss: 1.6138... Generator Loss: 0.8525\n",
      "Epoch 1/1-Step 4240... Discriminator Loss: 1.3783... Generator Loss: 0.9272\n",
      "Epoch 1/1-Step 4250... Discriminator Loss: 1.3710... Generator Loss: 0.7193\n",
      "Epoch 1/1-Step 4260... Discriminator Loss: 1.4316... Generator Loss: 0.7570\n",
      "Epoch 1/1-Step 4270... Discriminator Loss: 1.4212... Generator Loss: 0.9779\n",
      "Epoch 1/1-Step 4280... Discriminator Loss: 1.5912... Generator Loss: 0.7956\n",
      "Epoch 1/1-Step 4290... Discriminator Loss: 1.5320... Generator Loss: 0.7611\n",
      "Epoch 1/1-Step 4300... Discriminator Loss: 1.5352... Generator Loss: 0.7894\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxDMCMYtDKfGczPU7PSg
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad058e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4310... Discriminator Loss: 1.2916... Generator Loss: 0.7880\n",
      "Epoch 1/1-Step 4320... Discriminator Loss: 1.4958... Generator Loss: 0.8778\n",
      "Epoch 1/1-Step 4330... Discriminator Loss: 1.4708... Generator Loss: 1.0361\n",
      "Epoch 1/1-Step 4340... Discriminator Loss: 1.5519... Generator Loss: 0.7345\n",
      "Epoch 1/1-Step 4350... Discriminator Loss: 1.4025... Generator Loss: 0.8023\n",
      "Epoch 1/1-Step 4360... Discriminator Loss: 1.5092... Generator Loss: 0.8394\n",
      "Epoch 1/1-Step 4370... Discriminator Loss: 1.4229... Generator Loss: 0.8124\n",
      "Epoch 1/1-Step 4380... Discriminator Loss: 1.5185... Generator Loss: 0.5909\n",
      "Epoch 1/1-Step 4390... Discriminator Loss: 1.3466... Generator Loss: 0.7556\n",
      "Epoch 1/1-Step 4400... Discriminator Loss: 1.4597... Generator Loss: 1.0690\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqQlsL2EsPZgV33NmThB
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad2cf400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4410... Discriminator Loss: 1.7616... Generator Loss: 0.9580\n",
      "Epoch 1/1-Step 4420... Discriminator Loss: 1.4738... Generator Loss: 0.6393\n",
      "Epoch 1/1-Step 4430... Discriminator Loss: 1.2484... Generator Loss: 0.5620\n",
      "Epoch 1/1-Step 4440... Discriminator Loss: 1.2880... Generator Loss: 0.7840\n",
      "Epoch 1/1-Step 4450... Discriminator Loss: 1.4303... Generator Loss: 0.9152\n",
      "Epoch 1/1-Step 4460... Discriminator Loss: 1.3044... Generator Loss: 1.0829\n",
      "Epoch 1/1-Step 4470... Discriminator Loss: 1.6106... Generator Loss: 0.5933\n",
      "Epoch 1/1-Step 4480... Discriminator Loss: 1.4947... Generator Loss: 0.8435\n",
      "Epoch 1/1-Step 4490... Discriminator Loss: 1.3264... Generator Loss: 0.8331\n",
      "Epoch 1/1-Step 4500... Discriminator Loss: 1.4577... Generator Loss: 0.7298\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac8b9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4510... Discriminator Loss: 1.4926... Generator Loss: 0.6693\n",
      "Epoch 1/1-Step 4520... Discriminator Loss: 1.5976... Generator Loss: 0.6750\n",
      "Epoch 1/1-Step 4530... Discriminator Loss: 1.6050... Generator Loss: 0.5765\n",
      "Epoch 1/1-Step 4540... Discriminator Loss: 1.5485... Generator Loss: 1.0729\n",
      "Epoch 1/1-Step 4550... Discriminator Loss: 1.5785... Generator Loss: 0.8145\n",
      "Epoch 1/1-Step 4560... Discriminator Loss: 1.3578... Generator Loss: 0.8214\n",
      "Epoch 1/1-Step 4570... Discriminator Loss: 1.4673... Generator Loss: 0.7480\n",
      "Epoch 1/1-Step 4580... Discriminator Loss: 1.5683... Generator Loss: 0.9356\n",
      "Epoch 1/1-Step 4590... Discriminator Loss: 1.5348... Generator Loss: 0.9295\n",
      "Epoch 1/1-Step 4600... Discriminator Loss: 1.4560... Generator Loss: 0.8204\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nPqUC4+OzM+tyS3JynYr
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad8ece48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4610... Discriminator Loss: 1.3437... Generator Loss: 0.8714\n",
      "Epoch 1/1-Step 4620... Discriminator Loss: 1.2796... Generator Loss: 0.6942\n",
      "Epoch 1/1-Step 4630... Discriminator Loss: 1.6742... Generator Loss: 0.8542\n",
      "Epoch 1/1-Step 4640... Discriminator Loss: 1.5383... Generator Loss: 0.8454\n",
      "Epoch 1/1-Step 4650... Discriminator Loss: 1.8187... Generator Loss: 0.6208\n",
      "Epoch 1/1-Step 4660... Discriminator Loss: 2.4415... Generator Loss: 0.7599\n",
      "Epoch 1/1-Step 4670... Discriminator Loss: 1.9543... Generator Loss: 0.7565\n",
      "Epoch 1/1-Step 4680... Discriminator Loss: 1.8191... Generator Loss: 0.5288\n",
      "Epoch 1/1-Step 4690... Discriminator Loss: 1.4574... Generator Loss: 0.6674\n",
      "Epoch 1/1-Step 4700... Discriminator Loss: 1.2855... Generator Loss: 0.9943\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad929240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4710... Discriminator Loss: 1.3984... Generator Loss: 0.7302\n",
      "Epoch 1/1-Step 4720... Discriminator Loss: 1.6908... Generator Loss: 0.6329\n",
      "Epoch 1/1-Step 4730... Discriminator Loss: 1.3791... Generator Loss: 1.0040\n",
      "Epoch 1/1-Step 4740... Discriminator Loss: 1.7028... Generator Loss: 0.7184\n",
      "Epoch 1/1-Step 4750... Discriminator Loss: 1.4108... Generator Loss: 0.7537\n",
      "Epoch 1/1-Step 4760... Discriminator Loss: 1.3353... Generator Loss: 0.7356\n",
      "Epoch 1/1-Step 4770... Discriminator Loss: 1.4164... Generator Loss: 1.1678\n",
      "Epoch 1/1-Step 4780... Discriminator Loss: 1.4975... Generator Loss: 0.8875\n",
      "Epoch 1/1-Step 4790... Discriminator Loss: 1.4364... Generator Loss: 0.7185\n",
      "Epoch 1/1-Step 4800... Discriminator Loss: 1.4725... Generator Loss: 0.8623\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCVtUKvPYklQ94MqCEl+
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dadda97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4810... Discriminator Loss: 1.5693... Generator Loss: 0.6796\n",
      "Epoch 1/1-Step 4820... Discriminator Loss: 1.4496... Generator Loss: 0.7525\n",
      "Epoch 1/1-Step 4830... Discriminator Loss: 1.4801... Generator Loss: 0.5545\n",
      "Epoch 1/1-Step 4840... Discriminator Loss: 1.4186... Generator Loss: 0.7224\n",
      "Epoch 1/1-Step 4850... Discriminator Loss: 1.4451... Generator Loss: 0.7199\n",
      "Epoch 1/1-Step 4860... Discriminator Loss: 1.3019... Generator Loss: 0.8858\n",
      "Epoch 1/1-Step 4870... Discriminator Loss: 1.4198... Generator Loss: 0.9096\n",
      "Epoch 1/1-Step 4880... Discriminator Loss: 1.2658... Generator Loss: 0.8868\n",
      "Epoch 1/1-Step 4890... Discriminator Loss: 1.6038... Generator Loss: 0.6470\n",
      "Epoch 1/1-Step 4900... Discriminator Loss: 1.4718... Generator Loss: 0.6067\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5f4b080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 4910... Discriminator Loss: 1.2905... Generator Loss: 0.9631\n",
      "Epoch 1/1-Step 4920... Discriminator Loss: 1.5757... Generator Loss: 0.8546\n",
      "Epoch 1/1-Step 4930... Discriminator Loss: 1.5008... Generator Loss: 0.8035\n",
      "Epoch 1/1-Step 4940... Discriminator Loss: 1.5255... Generator Loss: 0.7468\n",
      "Epoch 1/1-Step 4950... Discriminator Loss: 1.6217... Generator Loss: 0.5873\n",
      "Epoch 1/1-Step 4960... Discriminator Loss: 1.3852... Generator Loss: 0.6521\n",
      "Epoch 1/1-Step 4970... Discriminator Loss: 1.6474... Generator Loss: 0.6915\n",
      "Epoch 1/1-Step 4980... Discriminator Loss: 1.3931... Generator Loss: 0.7564\n",
      "Epoch 1/1-Step 4990... Discriminator Loss: 1.1192... Generator Loss: 0.7424\n",
      "Epoch 1/1-Step 5000... Discriminator Loss: 1.4956... Generator Loss: 1.0168\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nK6/eBKTz4vNPERHRZ37
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dada0b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5010... Discriminator Loss: 1.5876... Generator Loss: 0.8955\n",
      "Epoch 1/1-Step 5020... Discriminator Loss: 1.5652... Generator Loss: 0.6562\n",
      "Epoch 1/1-Step 5030... Discriminator Loss: 1.3265... Generator Loss: 0.8291\n",
      "Epoch 1/1-Step 5040... Discriminator Loss: 1.6025... Generator Loss: 0.6956\n",
      "Epoch 1/1-Step 5050... Discriminator Loss: 1.5548... Generator Loss: 0.7266\n",
      "Epoch 1/1-Step 5060... Discriminator Loss: 1.2764... Generator Loss: 0.7391\n",
      "Epoch 1/1-Step 5070... Discriminator Loss: 1.3527... Generator Loss: 0.8363\n",
      "Epoch 1/1-Step 5080... Discriminator Loss: 1.5493... Generator Loss: 0.6342\n",
      "Epoch 1/1-Step 5090... Discriminator Loss: 1.4803... Generator Loss: 0.7554\n",
      "Epoch 1/1-Step 5100... Discriminator Loss: 1.6153... Generator Loss: 0.7942\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db66a1710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5110... Discriminator Loss: 1.6088... Generator Loss: 0.8132\n",
      "Epoch 1/1-Step 5120... Discriminator Loss: 1.5672... Generator Loss: 0.9925\n",
      "Epoch 1/1-Step 5130... Discriminator Loss: 1.3305... Generator Loss: 0.8971\n",
      "Epoch 1/1-Step 5140... Discriminator Loss: 1.4021... Generator Loss: 0.7788\n",
      "Epoch 1/1-Step 5150... Discriminator Loss: 1.3778... Generator Loss: 0.8807\n",
      "Epoch 1/1-Step 5160... Discriminator Loss: 1.3363... Generator Loss: 0.9228\n",
      "Epoch 1/1-Step 5170... Discriminator Loss: 1.6637... Generator Loss: 0.7810\n",
      "Epoch 1/1-Step 5180... Discriminator Loss: 1.3825... Generator Loss: 0.7041\n",
      "Epoch 1/1-Step 5190... Discriminator Loss: 1.2808... Generator Loss: 0.9957\n",
      "Epoch 1/1-Step 5200... Discriminator Loss: 1.5101... Generator Loss: 0.7931\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nm3pPh7v8rG0s6PNUqfL
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6b89518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5210... Discriminator Loss: 1.5796... Generator Loss: 0.6868\n",
      "Epoch 1/1-Step 5220... Discriminator Loss: 1.4450... Generator Loss: 0.5678\n",
      "Epoch 1/1-Step 5230... Discriminator Loss: 1.7324... Generator Loss: 0.7932\n",
      "Epoch 1/1-Step 5240... Discriminator Loss: 1.6043... Generator Loss: 0.9452\n",
      "Epoch 1/1-Step 5250... Discriminator Loss: 1.5204... Generator Loss: 0.7910\n",
      "Epoch 1/1-Step 5260... Discriminator Loss: 1.4736... Generator Loss: 0.7656\n",
      "Epoch 1/1-Step 5270... Discriminator Loss: 1.3685... Generator Loss: 0.6641\n",
      "Epoch 1/1-Step 5280... Discriminator Loss: 1.3507... Generator Loss: 0.6822\n",
      "Epoch 1/1-Step 5290... Discriminator Loss: 1.4663... Generator Loss: 0.8238\n",
      "Epoch 1/1-Step 5300... Discriminator Loss: 1.2868... Generator Loss: 0.8053\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbce5b5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5310... Discriminator Loss: 1.4377... Generator Loss: 0.6657\n",
      "Epoch 1/1-Step 5320... Discriminator Loss: 1.2514... Generator Loss: 0.8370\n",
      "Epoch 1/1-Step 5330... Discriminator Loss: 1.3734... Generator Loss: 0.7236\n",
      "Epoch 1/1-Step 5340... Discriminator Loss: 1.5389... Generator Loss: 0.6186\n",
      "Epoch 1/1-Step 5350... Discriminator Loss: 1.4716... Generator Loss: 0.7952\n",
      "Epoch 1/1-Step 5360... Discriminator Loss: 1.4496... Generator Loss: 0.8589\n",
      "Epoch 1/1-Step 5370... Discriminator Loss: 1.2425... Generator Loss: 0.9591\n",
      "Epoch 1/1-Step 5380... Discriminator Loss: 1.4800... Generator Loss: 1.0139\n",
      "Epoch 1/1-Step 5390... Discriminator Loss: 1.3756... Generator Loss: 0.7858\n",
      "Epoch 1/1-Step 5400... Discriminator Loss: 1.5945... Generator Loss: 1.2073\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMscSyB2IHGMFmWGdFhN
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5bfd8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5410... Discriminator Loss: 1.5085... Generator Loss: 1.1527\n",
      "Epoch 1/1-Step 5420... Discriminator Loss: 1.3853... Generator Loss: 0.6506\n",
      "Epoch 1/1-Step 5430... Discriminator Loss: 1.2997... Generator Loss: 0.6286\n",
      "Epoch 1/1-Step 5440... Discriminator Loss: 1.4468... Generator Loss: 0.8861\n",
      "Epoch 1/1-Step 5450... Discriminator Loss: 1.3339... Generator Loss: 0.6909\n",
      "Epoch 1/1-Step 5460... Discriminator Loss: 1.3680... Generator Loss: 0.8333\n",
      "Epoch 1/1-Step 5470... Discriminator Loss: 1.6801... Generator Loss: 0.7344\n",
      "Epoch 1/1-Step 5480... Discriminator Loss: 1.4616... Generator Loss: 0.6459\n",
      "Epoch 1/1-Step 5490... Discriminator Loss: 1.4658... Generator Loss: 0.6166\n",
      "Epoch 1/1-Step 5500... Discriminator Loss: 1.3914... Generator Loss: 0.6666\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7b96048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5510... Discriminator Loss: 1.5045... Generator Loss: 0.7602\n",
      "Epoch 1/1-Step 5520... Discriminator Loss: 1.5775... Generator Loss: 0.7188\n",
      "Epoch 1/1-Step 5530... Discriminator Loss: 1.4987... Generator Loss: 0.6459\n",
      "Epoch 1/1-Step 5540... Discriminator Loss: 1.5860... Generator Loss: 0.6320\n",
      "Epoch 1/1-Step 5550... Discriminator Loss: 1.3267... Generator Loss: 0.7290\n",
      "Epoch 1/1-Step 5560... Discriminator Loss: 1.4017... Generator Loss: 0.8656\n",
      "Epoch 1/1-Step 5570... Discriminator Loss: 1.4532... Generator Loss: 0.9305\n",
      "Epoch 1/1-Step 5580... Discriminator Loss: 1.3911... Generator Loss: 0.8586\n",
      "Epoch 1/1-Step 5590... Discriminator Loss: 1.4204... Generator Loss: 0.8865\n",
      "Epoch 1/1-Step 5600... Discriminator Loss: 1.5059... Generator Loss: 0.8704\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMJFlyJmbP3cPjjsjIyDuzrq6uvnu6h5yeGXI45HB5\niEtRJKEVKFKLxUpLaCDoAAUJ2KUkgKvFaoHVDx2rP4QW2oMCKC4piZSoBfcYUTyx5Mz03D3TV3VX\n152VWZkZGXeEH08/zJ7bl13V3VnTPTlTk8+ARnl7ZLg/f+7h9r3PzD4z1lry5s3b6bLg2z0Ab968\nnbz5H743b6fQ/A/fm7dTaP6H783bKTT/w/fm7RSa/+F783YKzf/wvXk7hfa+fvjGmJ8wxrxqjLls\njPnlD2pQ3rx5+9aa+WYTeIwxIRG9RkQ/RkQ3iOjzRPQL1tpvfHDD8+bN27fCovfx3Y8S0WVr7ZtE\nRMaYf0JEP0NE7/jDr1QrttFsEhHRPM2JiChN0uLzLE2IiCjPc/0SbJuAAUoQKFDRbX2BWfyOMfd8\n7o6P7zxD727v9no08G0LB7K5lfPA2Gx+z/niSG9DpRITEdE802vI5TjuXzwOHl+vVbePXJfBTXPP\nTjeXcRwX++r1qo6zVJJxZMW+NON7htAxMDgJPLYMxpvJdUxmSbFvOpvrV+RzHHtZzr2xtQknuhew\nppk+T7PZhIiIxqNRsS9P3b3XucxSvZ6keAbxebn3PhJcY2CCt++6/3fA3P4jz8b9njLcZdw/eJ/5\n31IYEhHRLEkozbL3epzf1w9/k4iuw//fIKKPvdsXGs0m/Rt/6S8REdG1vSEREe3duVt83t/ZISKi\nyXhQ7Evns2I7LleIiKhWrRf7ajXeZ4w+WMl0XGyXSzwhFh6IyYQfiGSu+wJ4dN0LJseXiftF440M\nzJG/JyKyRrfn8jAniT7U8zmfuwTf2VpaKrafeOIsERHdOBgW+4ZjHud0onMxm02L7VQeVvzBlkK+\ntVGIw9Wxl+RlY+ARqMq8bm5tFfs+9rHni+0z6yt87qnen7t72/zdUOe/Lj9SIqIs5bEfTnS8fZmX\nb7xxu9j36mV9lCYj/ts41LE9ur5KRES/8nf/drHPiBMhIrLE5z/o7Rb73nydfdAXP/v5Yt+wx2NP\nJjrew8ODYnt7+w4REY3HOt7JlOd3Di+IMNS5rlT45RjCvZ/KSycBx4bPk5sXfDYydHhuX3avEwvh\nppZk31a3Q0REX3/r6j3HuJ99y8k9Y8ynjTEvGmNenMLN9+bN27fP3o/Hv0lEZ+D/t2TfEbPW/n0i\n+vtERJ3FZXvY4zdYZvktWW13i7+d9NnLWcBMWVnfiGGlTEREOXjLacjbdq6wMU/1LVutMiJog3fI\naw0iIprP9diR0bdoU/42y/QNP5owihhP1etGMp4wLuuxAz3O/gF7kgQ8RSbXP5sp/OxNAYpO2StE\nVr1mTc5DuV53FKnHyXO+jgCWDLk7J7zaS/C586Y7d9RD7mX7RERUrS0W+6rlBf1+WCMiIhvB/EZ8\nH7Nhr9jXTxVxueVDCeZlMeLjdEq1Yl+jrCgusHxtUUmfgzzm4zSa+rxQXb+f5Hxf8r6O49bdPhER\nvXlVkcVMUFM20Xsyn05hm/fHgc6VCfmelEivoVLR8bZaLSIiCnEJanjeQniWEdaPJnzP54BE3TIl\nhb87GCjyS93yAaB+MuO5HqRy/e+6KFV7Px7/80R0yRhzwRgTE9HPE9Hvvo/jefPm7YTsm/b41trU\nGPMfE9G/IKKQiP6htfbr7/odIprLWiwL+E3VWkEvxd55PNB1zXisb0Qb8tvYBPrGS1NeS80nuu6s\nlvXNvLLOb+ONdqvYV87kDQ5v6G5LP1/u8Nva4BtaeIM+ePwD4Qpu7u8X+7JQz11v8bprMFI0Mpvz\nm3421Tf5NFGPMxUOIE11X7nBni2uqJefA8KZz2VezH3Wg7DuXGwo6rm4sk5ERLeFIyEiyiwf52Pf\n92Sx77mnNvR6GlUZux6zXWFU07upHnTYhyWdeMv2giKHSG5ff6D3fq+vqOnaLZ6bAFBCWObnoFzW\nvxtZIAczvv+jZK/Yd/X6y3zsvVt6jXOZo7nOVRWOubnSJiKi1YZ69EXZDuDeThO93ihmdFbH7yww\nqgwA7SEvMxKUMU70edrrHRIR0STR69qf6j273ePv3Ly5U+yby99OnMe39/IE97P3A/XJWvt7RPR7\n7+cY3rx5O3nzmXvevJ1Ce18e/0HNkqUkZeg3FWh+JDQnUDJNITR3hPzgf3OAWfMRQ2ML++Kaxp5b\nEgJcKOuldmsMmZcBfq4sKKRqCKQuQ1jKCDE2Arh29TaHfkKaFPsOpgrTYoGQZSDVBg2GgJPDQz02\n8DGDIS8FxnM9T73C4yhX9bpsgKFaiU0DqRnJKz2AY1dg6VKT7zx9dq3Y9/jTTxMR0ZMf/Uixr7ux\nWmwHctDRAEKjhzy2EiyvkJQbDHluKmUlMGuSG9CpqN8pZTpv4z7PDRKlK0u8FMvgGm8cKDE5TBni\n39nZLvbdvcXLkPFQSdzpkOe1BCTYYl2XO5sdHtuFFX02Vpu81CrH+ncTgPBGiNI2PEP1qizLUr2u\nZKZLtalsI3nXMjy2yVznr5sCyZjxfG1DjkEqUD+Z8bwcE+l7j+/N22m0E/X4hohiOWMjlrc5hLVq\nVfaQE/CapUy93LTHBJvz8kREqZBt9RjCXyUla6qSeNOq6Nt6bYEJnCUgY7oNDQ1VSjzIGDy+I3Ya\nZd0XSwZbDbzH69c1dHRjzG9zo1+h1Q57rv07d4p9Bt7gsZBhi20Yj2wbCBsGc/U4LqlodmSfvPoz\nyEAbwryVGXE9/cJzxb7v+eQniYiovaYo4AhkCHkubVU922KHScLsUD1XNlKvvDviBK09SFQxLb6e\nEqA5h0CIiNyd2h8qYTsVz5gBEhocqsc/mDOBd3dP5/XQoacpEo9CKgMC7EBYcKPJz0Q3Up/Ykke1\nFOpctAFVBhLmw2ewIFUhCSmL9BprkphTgntakkSgQwgz1wIguqs8/5eB5O31+NpcjsxxyT3v8b15\nO4Xmf/jevJ1CO1GoT5QTSVZXWXK762V490gcuQwwaw4ZVInA+tlQYWUkectYTNKo6HeCjGHTksRV\niYhWBG7XgCArl/Q7JYFsUajQzRWmYGy/JTHweFWzyeJclynhbd5+s6fLmVYllPMBGUYKRes1Pufq\nGSXVWivLRES0P9CMuL1D3U5GvJ0ADCZZPsRYOQJk2flHuNjlIz/yQ8W+zhbDdouZkbAUK4qFAE5W\nKwyTu4s6B6VU52ggefB39xWWbycM4edAyIaQDbjcYbg9zaBwR+Ld46GSorORLgWyjD/fuaNx/MEh\nL22GsAyZzfjvyg1d+m0tt4vt1ZZklJaweElyIuBRrUC+fCSwvgQsrZG8+yOFU0dqfHh/A4jfUJ6n\nEOYcl7WNkiwDm7o0uXGHj5O7LNNjVtt6j+/N2yk0/8P35u0U2olC/cAYqscMTcZzx8YrRJ9IAUUI\ncBlZ3Exi6DmU6pYqDOER3jdihVd1iR4sL2rhSUMgVQSQ1YRYlCEQH5YZroYcC25yiRhgGunWip6n\nJHDRWmX6Bz0phAGoj2/fC2scP15aVua8usDQN4dUzsMDKCCSVM/ZSOG/K8ipw9hW2hrF+P4f+gE+\nz7lH9OQybRMoax4ONB152Odtm0OhkmWIbgDHtpp6nq1VXqZMDqH8+oCLZ3b7CmPHUDRTluhBC5Zv\noUDYXl+PM5ro2CK5zN6eLgUOdnmZMRoodI5knCstYPLbugx0j1Ec6723skTKIf0ZC2VcWfaR2nvr\n0qhhyYBl3vL9GNKAA1liBhBtGsOzfij6BYsQUXBaDua4AfxiLN68eTt1dsJxfFsQIKG88dKJerFA\nCBEkpBJUTxGPj0lr1TLHNFtVJeJW2+otz26wyMUCkDlRQdZA3BVELEJxHzm8wXN5R1oo382EfEIB\nhXJZz7PeZe89nSlx9dY
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5cd0fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5610... Discriminator Loss: 1.3622... Generator Loss: 0.8729\n",
      "Epoch 1/1-Step 5620... Discriminator Loss: 1.6957... Generator Loss: 0.6604\n",
      "Epoch 1/1-Step 5630... Discriminator Loss: 1.6844... Generator Loss: 0.7867\n",
      "Epoch 1/1-Step 5640... Discriminator Loss: 1.5988... Generator Loss: 0.8483\n",
      "Epoch 1/1-Step 5650... Discriminator Loss: 1.6001... Generator Loss: 0.8210\n",
      "Epoch 1/1-Step 5660... Discriminator Loss: 1.3692... Generator Loss: 0.6221\n",
      "Epoch 1/1-Step 5670... Discriminator Loss: 1.2333... Generator Loss: 0.7121\n",
      "Epoch 1/1-Step 5680... Discriminator Loss: 1.4341... Generator Loss: 0.8165\n",
      "Epoch 1/1-Step 5690... Discriminator Loss: 1.4222... Generator Loss: 1.0732\n",
      "Epoch 1/1-Step 5700... Discriminator Loss: 1.5038... Generator Loss: 0.6557\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLnc11bot49uo6ozdaCj
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da74a0cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5710... Discriminator Loss: 1.5134... Generator Loss: 0.5332\n",
      "Epoch 1/1-Step 5720... Discriminator Loss: 1.6874... Generator Loss: 0.5323\n",
      "Epoch 1/1-Step 5730... Discriminator Loss: 1.6629... Generator Loss: 0.6543\n",
      "Epoch 1/1-Step 5740... Discriminator Loss: 1.1474... Generator Loss: 0.9340\n",
      "Epoch 1/1-Step 5750... Discriminator Loss: 1.3726... Generator Loss: 0.8105\n",
      "Epoch 1/1-Step 5760... Discriminator Loss: 1.6248... Generator Loss: 0.5390\n",
      "Epoch 1/1-Step 5770... Discriminator Loss: 1.3388... Generator Loss: 1.0406\n",
      "Epoch 1/1-Step 5780... Discriminator Loss: 1.4832... Generator Loss: 0.8520\n",
      "Epoch 1/1-Step 5790... Discriminator Loss: 1.4594... Generator Loss: 0.6703\n",
      "Epoch 1/1-Step 5800... Discriminator Loss: 1.5462... Generator Loss: 0.5732\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nGc+WUQpKM4gi7JSHr37
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da736b978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5810... Discriminator Loss: 1.4532... Generator Loss: 0.7959\n",
      "Epoch 1/1-Step 5820... Discriminator Loss: 1.3183... Generator Loss: 0.7068\n",
      "Epoch 1/1-Step 5830... Discriminator Loss: 1.5936... Generator Loss: 0.6918\n",
      "Epoch 1/1-Step 5840... Discriminator Loss: 1.5260... Generator Loss: 0.8292\n",
      "Epoch 1/1-Step 5850... Discriminator Loss: 1.3679... Generator Loss: 0.8010\n",
      "Epoch 1/1-Step 5860... Discriminator Loss: 1.3761... Generator Loss: 0.8645\n",
      "Epoch 1/1-Step 5870... Discriminator Loss: 1.2885... Generator Loss: 0.8227\n",
      "Epoch 1/1-Step 5880... Discriminator Loss: 1.5387... Generator Loss: 0.7631\n",
      "Epoch 1/1-Step 5890... Discriminator Loss: 1.5974... Generator Loss: 0.8087\n",
      "Epoch 1/1-Step 5900... Discriminator Loss: 1.6992... Generator Loss: 0.7804\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da72154a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 5910... Discriminator Loss: 1.7040... Generator Loss: 0.6521\n",
      "Epoch 1/1-Step 5920... Discriminator Loss: 1.5462... Generator Loss: 0.7267\n",
      "Epoch 1/1-Step 5930... Discriminator Loss: 1.5583... Generator Loss: 0.9569\n",
      "Epoch 1/1-Step 5940... Discriminator Loss: 1.4782... Generator Loss: 0.6528\n",
      "Epoch 1/1-Step 5950... Discriminator Loss: 1.3296... Generator Loss: 0.8029\n",
      "Epoch 1/1-Step 5960... Discriminator Loss: 1.5989... Generator Loss: 1.2280\n",
      "Epoch 1/1-Step 5970... Discriminator Loss: 1.2882... Generator Loss: 0.9308\n",
      "Epoch 1/1-Step 5980... Discriminator Loss: 1.3564... Generator Loss: 0.7141\n",
      "Epoch 1/1-Step 5990... Discriminator Loss: 1.6733... Generator Loss: 0.9728\n",
      "Epoch 1/1-Step 6000... Discriminator Loss: 1.4325... Generator Loss: 0.7974\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCkuKU5j3+N68nUM7U48
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da7177d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6010... Discriminator Loss: 1.3203... Generator Loss: 0.7839\n",
      "Epoch 1/1-Step 6020... Discriminator Loss: 1.4128... Generator Loss: 0.7078\n",
      "Epoch 1/1-Step 6030... Discriminator Loss: 1.5910... Generator Loss: 0.6148\n",
      "Epoch 1/1-Step 6040... Discriminator Loss: 1.4653... Generator Loss: 0.8717\n",
      "Epoch 1/1-Step 6050... Discriminator Loss: 1.7522... Generator Loss: 0.9054\n",
      "Epoch 1/1-Step 6060... Discriminator Loss: 1.3762... Generator Loss: 1.0431\n",
      "Epoch 1/1-Step 6070... Discriminator Loss: 1.5753... Generator Loss: 0.7253\n",
      "Epoch 1/1-Step 6080... Discriminator Loss: 1.4033... Generator Loss: 0.9338\n",
      "Epoch 1/1-Step 6090... Discriminator Loss: 1.2361... Generator Loss: 0.8381\n",
      "Epoch 1/1-Step 6100... Discriminator Loss: 1.6563... Generator Loss: 0.6171\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da75392b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6110... Discriminator Loss: 1.3800... Generator Loss: 0.9543\n",
      "Epoch 1/1-Step 6120... Discriminator Loss: 1.4016... Generator Loss: 0.6586\n",
      "Epoch 1/1-Step 6130... Discriminator Loss: 1.4016... Generator Loss: 0.7899\n",
      "Epoch 1/1-Step 6140... Discriminator Loss: 1.4648... Generator Loss: 0.7876\n",
      "Epoch 1/1-Step 6150... Discriminator Loss: 1.2822... Generator Loss: 0.7874\n",
      "Epoch 1/1-Step 6160... Discriminator Loss: 1.4370... Generator Loss: 0.8804\n",
      "Epoch 1/1-Step 6170... Discriminator Loss: 1.5832... Generator Loss: 0.7170\n",
      "Epoch 1/1-Step 6180... Discriminator Loss: 1.6901... Generator Loss: 0.7309\n",
      "Epoch 1/1-Step 6190... Discriminator Loss: 1.4601... Generator Loss: 0.7070\n",
      "Epoch 1/1-Step 6200... Discriminator Loss: 1.5494... Generator Loss: 0.8740\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw7wN00nrnPAzl2zFbBs
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db7d11940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6210... Discriminator Loss: 1.5020... Generator Loss: 0.7783\n",
      "Epoch 1/1-Step 6220... Discriminator Loss: 1.3836... Generator Loss: 0.8767\n",
      "Epoch 1/1-Step 6230... Discriminator Loss: 1.2711... Generator Loss: 0.7823\n",
      "Epoch 1/1-Step 6240... Discriminator Loss: 1.3631... Generator Loss: 0.7754\n",
      "Epoch 1/1-Step 6250... Discriminator Loss: 1.2764... Generator Loss: 0.7984\n",
      "Epoch 1/1-Step 6260... Discriminator Loss: 1.4023... Generator Loss: 0.8042\n",
      "Epoch 1/1-Step 6270... Discriminator Loss: 1.4534... Generator Loss: 0.7786\n",
      "Epoch 1/1-Step 6280... Discriminator Loss: 1.4395... Generator Loss: 0.9211\n",
      "Epoch 1/1-Step 6290... Discriminator Loss: 1.7293... Generator Loss: 0.7617\n",
      "Epoch 1/1-Step 6300... Discriminator Loss: 1.4211... Generator Loss: 0.7980\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da7242390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6310... Discriminator Loss: 1.3222... Generator Loss: 0.7418\n",
      "Epoch 1/1-Step 6320... Discriminator Loss: 1.5442... Generator Loss: 0.7134\n",
      "Epoch 1/1-Step 6330... Discriminator Loss: 1.3139... Generator Loss: 0.9803\n",
      "Epoch 1/1-Step 6340... Discriminator Loss: 1.6821... Generator Loss: 0.5913\n",
      "Epoch 1/1-Step 6350... Discriminator Loss: 1.4928... Generator Loss: 0.7019\n",
      "Epoch 1/1-Step 6360... Discriminator Loss: 1.7137... Generator Loss: 0.7491\n",
      "Epoch 1/1-Step 6370... Discriminator Loss: 1.2787... Generator Loss: 0.9492\n",
      "Epoch 1/1-Step 6380... Discriminator Loss: 1.3087... Generator Loss: 0.7035\n",
      "Epoch 1/1-Step 6390... Discriminator Loss: 1.5177... Generator Loss: 0.6452\n",
      "Epoch 1/1-Step 6400... Discriminator Loss: 1.5534... Generator Loss: 0.7097\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMbVd2Hrb2me481Fz1Xr2RZJNNsge12hosx5GtKFAG\nSEEQC1KCwEgE6E8GGQkQK0GQAUgAZ0ASI0CEGLYjOfAgJZIiRVZkK5LbiRQN3a1Ws5tkk3yP5Bur\nXs13vvdMOz/W2md9xcdHFpvsUtO1F/BQ5517z7TPuWd9+1trfctYa8mbN28Xy4I/6RPw5s3b+Zv/\n4XvzdgHN//C9ebuA5n/43rxdQPM/fG/eLqD5H743bxfQ/A/fm7cLaB/qh2+M+SFjzGvGmFvGmJ/+\nqE7Kmzdv31oz32wCjzEmJKLXiegHieg+EX2RiH7cWvvKR3d63rx5+1ZY9CG2/S4iumWtfZOIyBjz\n94joR4joiT/8ViOxy50GERFVLxx48dSShIiI4jiu1oUhnGIQyja6yu3HwEpbFLpJwKDGBAFuRERE\nZQnfC8Nq2chxgijRTeRvWZbVunQ+lw1Mta7Qj2mRZkRENJlNdaXb3uoXk3qtWl5e6hMRUQjHni9m\nREQ0nkyqdeOx7rOU6y1xXOSMLazEV/y7vfDdNoYMrDv9jXeuDAL+bgBjEISPj3UA4++uvCh0RyXc\nMyP7igLdZ6PG49Fv1+F7jx2GIriPbiWOAd6/x64Llk4Njwnks3cfy3cZlup/Bk/yfT6PovCxdTk8\no2nGz5OBA4VyvUHIv5n94xGNJrMnHVSP9X5feA+7TET34P/3iei732uD5U6D/tJf+NNERFRmKRER\n5UVefX7z8jYREW1tblfrekur1XJYaxERkS3hh5bz9gHsJ50Mq+Vmg39UtUazWmcXPICz2aBa1+ou\nV8tJo0dERI11PY+FPAmz2bxa9/arr8p56Y90ONW78vrdB0RE9OWvf6VaR1P+wbrrJyK6/omb1fKP\n/YUfJiKi3vrlat0rr3+diIh+94tfrtb9zu98qVqejcf8N9MxKHI+j3mqxyngx5XKuJXwhLvl8NSP\nFB52+QHhS6OR8CPUaOgYdFv6Istz/qG1Wzr+c7l/g/GiWjcajqrlRI6/2tIf+Qs3+V78y//Uc9U6\nfDHkcj3LvX61juTFW8AYTKb8EsWXWw7jksml4UuUYr62DF4aOYxBIW97iz83+RydmIUfsZXXXxTp\nRmurS7yuodscTfVZvrezQ0REQabn0Wvxs9pc2iQiov/4f/xFOot9y8k9Y8xPGmO+ZIz50mSWvv8G\n3rx5+5bbh/H4D4joCvx/W9adMmvtXyOiv0ZEtN5v2DfvMEjIxeP14a2+8txTRES0tapv7XZfl1N5\nHR/u7Vfr9h485M9G42pdkKknubrNiKHRalTrZvuHvM1Yt2lf1ksJO7w+6a/odcjf8fFhte6V3/89\nPsZTT+kFN7p6HsToYJoqLN/f3yMiosVCvXNt+6puHvF5ZlM9t1cEMdy6datad+feQ91eoHWzqdfY\narHXiEL1TPNUlwUQnPJ8RtxcacEDIhwv380d8nIG2xRWr60Uj99tKiLoNPiel6TrJhP1QQ5RIII5\nkKlNPlaUNgVPPp3xPY8LcC7i8SkHTyveudHQsbIwOwhluhkmMKWoMYJJYR43h306xHBqWur2F+B1\nwbjmMg2cKNJJpzyVc7CdiMjm+hzcf3CfF2Z6Hi885RAX/w3O6Mo/jMf/IhE9Y4y5YYxJiOjHiOhX\nP8T+vHnzdk72TXt8a21ujPm3iegfEFFIRH/TWvvye20zSwt6+e4JERFlc34zf9+nblSfd7s8j4sT\nnW9nU3277e6yt/zqS69W6159m9+Cg2OdCzUCfYV/6o1dIiK60oTX+ojfrN2eHic+2K2Wyy57j1Zd\nPXm8skFEROOXXqvW3X6FvX/Lblbr6hvtavnwDp/T6ES96ts7cp7wVt9Idf47FjDz0qu/X637whd4\njv/g0V61bjZTb9iTee1WX3mBWiyEVFe/N1so8ljIfDMv9fPhjOe/w4l+D71cId67gLluKZ8XuV5j\nOgdEIF/NlRqhzVVGUp2ahW30URycyDNSqF8aL3j/uw/VAx4N9Z4HMW+fLU6qdQ0hy9xfIqKOoKIk\nblXr6k317lHC3j2MlacoI963ifSelYGerxX/acHdOrI5B9SyEJKWiGg+n7iNdd2Un7sUuIBZqtvM\nRvybsVPkGnhcwrqgJ3M2X/5hoD5Za3+diH79w+zDmzdv528+c8+btwtoH8rjf1ALjaG+QJLlJYbE\nLzytcDoUGDaeKYQb7CoE/6NvvE5ERC/fe1Ste3gkoayJEnoNC5BLINWirpe6KSGURqzk0gJip7Ua\nn0cxUuIlXOFQS72t++5L2KXdVdhY6yhEvHGJIe3JQMlIe7xORERDUviZxDo9GBwwP/rGm9/Qa5Qw\nznCscfxeorDze2/ydOnGtoYfu0LudZp6PlEAEFEg/mCiZNn9wwMiInpwcFyt2x/oMUcyPSuBEBxP\nGMOPF0qqxTCrqkm471JXpzPPbvIUqwkE2/aKEqm37/H1TicK6xsSry6AuMVQmJFjTmE602p0+G8L\niEXJA2i3dV2ro/cvrvHnUUPXGQf/a3q+BLCfDF8jToFcmHkx1znOfKrbxBMJnUa6zWDA28zmeg1T\nyAGRoaQCfrVlmcrp8Fg8MW3gHeY9vjdvF9DO1eO3ajF95w0mwp66skVERFeeVnLPGn777R2ql39w\n961q+f4BeyRIeqMrffYeTz+zUa1br6mXoxP2tuGJhuE2a+we+h29/BZ46naXkyJoosQKzTn8srSp\nBNp333yRiIiuvvid1boxZK11OuLFFuoCrzc5bPi1oXraoq0k42LABN7oUFHNQEisEoi2Zzc0senz\nV/naV1eVpOr32MN2INMthrttJeQ2nur5XmNQQ/vLer67++qlnMePYXwHEkZ76S0lHo+n6pXbsvl2\nTw++3ma31ILxjyG0VyM+kaOBHvtYkn0SyvR7TT33RcHrm3BuXdllr6nH6bV4uQ1JRg1YjluMvqIW\nhGWdp0cvH8Ez5jL7MBlKwtWLRI+dQGg1DsRTB3o9tuB9lieKNE2hnzcSHrfpDEOs/HkU8rNhMK3v\nPcx7fG/eLqD5H743bxfQzhXqN2o1+tRT14mIaPvGNT6BvkKqyZQhzp37mpX24NFRtVwPGbZ+34tK\nCL74yc8REdGl7evVugiyt/KDO0RENL2tZFmxx1MJAxlmLqefiCiiXLbV6UEqOdfTuW6zvMZQvrcO\nGX5TJcPIcHx9bXVdj2N4GnEUK1G0C9lxhztM7j3YUaifSgYaEnqf2tLcgdUuj8sSkFhdyeSqIzoF\n0s2xQEkE2X6y+TLs51JfSa65XDvyRzMpHKGFklhfeeugWg5lXhbDWEflQv7qNKQeaOy6mQiRB/H1\n4YinXXmm0JcgZ72QrLhuV4nSloxXPdLHvFHn663XlWxMYDkWUi+sA5GXyDJm5oWPQ300WxULwWeQ\nM0HyjJa5TosW8owZrAOADM92jT+3kP/gvhvKXTF0NnbPe3xv3i6g+R++N28X0M4V6kdRSMsSr+2t\nMqt/BGmMdx8yvH14qKxmWSjce/6ZF4iI6MVPa/XvuhS41AAGm7luH5UcB40yheCZRA8WRzqNKCYK\nVdMRx5GLQPMJcomXvvayFsq8KsU+nwc8Ha5pdGE2N3I9OvUYDRmqZpFC0ulUj313yEVMuwM9tovN\ndoAh3lzRKVIiMdwYaPuK3IWSUywyd1nNCaSeJlK63G5AdKCrRVILqa4s5gpP55JS+l3PaA7Bzp6O\n/96Qr214ojH5TEqTC4DOiwmUVUtOAGokuPLeIRSopFDaHMsY4PUmsn0U4Lg4Bp50HcJjJ5cARTgU\n8rKBVPBTWgBaxf/Y5yHkGsQw1yok2aEGZbtOcwA1BeZTvcamjFeto5+Xcp6u5Pqswjre43vzdgHt\nXD1+EITUEvKkXLAHnkB
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6bd5898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6410... Discriminator Loss: 1.4230... Generator Loss: 0.8357\n",
      "Epoch 1/1-Step 6420... Discriminator Loss: 1.5231... Generator Loss: 0.8468\n",
      "Epoch 1/1-Step 6430... Discriminator Loss: 1.2886... Generator Loss: 0.9715\n",
      "Epoch 1/1-Step 6440... Discriminator Loss: 1.5898... Generator Loss: 0.7024\n",
      "Epoch 1/1-Step 6450... Discriminator Loss: 1.4828... Generator Loss: 0.6253\n",
      "Epoch 1/1-Step 6460... Discriminator Loss: 1.4022... Generator Loss: 0.7826\n",
      "Epoch 1/1-Step 6470... Discriminator Loss: 1.4482... Generator Loss: 0.8871\n",
      "Epoch 1/1-Step 6480... Discriminator Loss: 1.2896... Generator Loss: 0.8432\n",
      "Epoch 1/1-Step 6490... Discriminator Loss: 1.3794... Generator Loss: 0.6385\n",
      "Epoch 1/1-Step 6500... Discriminator Loss: 1.3448... Generator Loss: 0.8719\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da704cb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6510... Discriminator Loss: 1.5633... Generator Loss: 0.7200\n",
      "Epoch 1/1-Step 6520... Discriminator Loss: 1.2998... Generator Loss: 0.7419\n",
      "Epoch 1/1-Step 6530... Discriminator Loss: 1.2107... Generator Loss: 0.8752\n",
      "Epoch 1/1-Step 6540... Discriminator Loss: 1.6673... Generator Loss: 0.9346\n",
      "Epoch 1/1-Step 6550... Discriminator Loss: 1.4481... Generator Loss: 0.8061\n",
      "Epoch 1/1-Step 6560... Discriminator Loss: 1.1742... Generator Loss: 0.7871\n",
      "Epoch 1/1-Step 6570... Discriminator Loss: 1.4755... Generator Loss: 0.9317\n",
      "Epoch 1/1-Step 6580... Discriminator Loss: 1.4640... Generator Loss: 0.9636\n",
      "Epoch 1/1-Step 6590... Discriminator Loss: 1.3566... Generator Loss: 0.7615\n",
      "Epoch 1/1-Step 6600... Discriminator Loss: 1.2830... Generator Loss: 0.7147\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQutQGME+QQB9q3DMRXt
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da69125f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6610... Discriminator Loss: 1.6832... Generator Loss: 0.6913\n",
      "Epoch 1/1-Step 6620... Discriminator Loss: 1.5239... Generator Loss: 0.8743\n",
      "Epoch 1/1-Step 6630... Discriminator Loss: 1.4028... Generator Loss: 0.6859\n",
      "Epoch 1/1-Step 6640... Discriminator Loss: 1.4776... Generator Loss: 0.8069\n",
      "Epoch 1/1-Step 6650... Discriminator Loss: 1.6406... Generator Loss: 0.7486\n",
      "Epoch 1/1-Step 6660... Discriminator Loss: 1.3582... Generator Loss: 0.9390\n",
      "Epoch 1/1-Step 6670... Discriminator Loss: 1.6321... Generator Loss: 0.6592\n",
      "Epoch 1/1-Step 6680... Discriminator Loss: 1.5398... Generator Loss: 0.9347\n",
      "Epoch 1/1-Step 6690... Discriminator Loss: 1.6167... Generator Loss: 1.0672\n",
      "Epoch 1/1-Step 6700... Discriminator Loss: 1.4168... Generator Loss: 0.7212\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6b39f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6710... Discriminator Loss: 1.5981... Generator Loss: 0.4664\n",
      "Epoch 1/1-Step 6720... Discriminator Loss: 1.5008... Generator Loss: 0.7696\n",
      "Epoch 1/1-Step 6730... Discriminator Loss: 1.8466... Generator Loss: 0.6465\n",
      "Epoch 1/1-Step 6740... Discriminator Loss: 1.4396... Generator Loss: 0.7880\n",
      "Epoch 1/1-Step 6750... Discriminator Loss: 1.5017... Generator Loss: 0.7424\n",
      "Epoch 1/1-Step 6760... Discriminator Loss: 1.5507... Generator Loss: 0.6303\n",
      "Epoch 1/1-Step 6770... Discriminator Loss: 1.3082... Generator Loss: 0.8959\n",
      "Epoch 1/1-Step 6780... Discriminator Loss: 1.7568... Generator Loss: 0.5531\n",
      "Epoch 1/1-Step 6790... Discriminator Loss: 1.2920... Generator Loss: 0.6762\n",
      "Epoch 1/1-Step 6800... Discriminator Loss: 1.5200... Generator Loss: 0.7450\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nN7zx0gpUdus9miVyr6K
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6b18c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6810... Discriminator Loss: 1.3104... Generator Loss: 0.9882\n",
      "Epoch 1/1-Step 6820... Discriminator Loss: 1.3435... Generator Loss: 1.2353\n",
      "Epoch 1/1-Step 6830... Discriminator Loss: 1.1359... Generator Loss: 0.7902\n",
      "Epoch 1/1-Step 6840... Discriminator Loss: 1.4087... Generator Loss: 0.6343\n",
      "Epoch 1/1-Step 6850... Discriminator Loss: 1.3144... Generator Loss: 0.7056\n",
      "Epoch 1/1-Step 6860... Discriminator Loss: 1.4977... Generator Loss: 0.8563\n",
      "Epoch 1/1-Step 6870... Discriminator Loss: 1.4116... Generator Loss: 1.0645\n",
      "Epoch 1/1-Step 6880... Discriminator Loss: 1.4191... Generator Loss: 1.0519\n",
      "Epoch 1/1-Step 6890... Discriminator Loss: 1.5543... Generator Loss: 0.5972\n",
      "Epoch 1/1-Step 6900... Discriminator Loss: 1.3013... Generator Loss: 0.9223\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6ac5128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 6910... Discriminator Loss: 1.4314... Generator Loss: 0.9101\n",
      "Epoch 1/1-Step 6920... Discriminator Loss: 1.3602... Generator Loss: 0.7910\n",
      "Epoch 1/1-Step 6930... Discriminator Loss: 1.4245... Generator Loss: 0.8057\n",
      "Epoch 1/1-Step 6940... Discriminator Loss: 1.3132... Generator Loss: 0.8169\n",
      "Epoch 1/1-Step 6950... Discriminator Loss: 1.4812... Generator Loss: 0.5608\n",
      "Epoch 1/1-Step 6960... Discriminator Loss: 1.3330... Generator Loss: 0.7081\n",
      "Epoch 1/1-Step 6970... Discriminator Loss: 1.7487... Generator Loss: 0.7917\n",
      "Epoch 1/1-Step 6980... Discriminator Loss: 1.3499... Generator Loss: 0.7759\n",
      "Epoch 1/1-Step 6990... Discriminator Loss: 1.3482... Generator Loss: 0.9711\n",
      "Epoch 1/1-Step 7000... Discriminator Loss: 1.2888... Generator Loss: 0.9719\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nXLxeh6y0mp57TcgpLEl
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6c07940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7010... Discriminator Loss: 1.5234... Generator Loss: 0.5683\n",
      "Epoch 1/1-Step 7020... Discriminator Loss: 1.4594... Generator Loss: 0.8179\n",
      "Epoch 1/1-Step 7030... Discriminator Loss: 1.2845... Generator Loss: 0.7871\n",
      "Epoch 1/1-Step 7040... Discriminator Loss: 1.7051... Generator Loss: 0.4298\n",
      "Epoch 1/1-Step 7050... Discriminator Loss: 1.8542... Generator Loss: 0.4715\n",
      "Epoch 1/1-Step 7060... Discriminator Loss: 1.3809... Generator Loss: 0.8507\n",
      "Epoch 1/1-Step 7070... Discriminator Loss: 1.3072... Generator Loss: 0.7898\n",
      "Epoch 1/1-Step 7080... Discriminator Loss: 1.4766... Generator Loss: 0.8204\n",
      "Epoch 1/1-Step 7090... Discriminator Loss: 1.3707... Generator Loss: 0.6629\n",
      "Epoch 1/1-Step 7100... Discriminator Loss: 1.4636... Generator Loss: 0.8876\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da62d46a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7110... Discriminator Loss: 1.3131... Generator Loss: 0.7485\n",
      "Epoch 1/1-Step 7120... Discriminator Loss: 1.2582... Generator Loss: 0.9262\n",
      "Epoch 1/1-Step 7130... Discriminator Loss: 1.3768... Generator Loss: 0.9119\n",
      "Epoch 1/1-Step 7140... Discriminator Loss: 1.7029... Generator Loss: 0.7280\n",
      "Epoch 1/1-Step 7150... Discriminator Loss: 1.2092... Generator Loss: 0.7597\n",
      "Epoch 1/1-Step 7160... Discriminator Loss: 1.3700... Generator Loss: 0.7609\n",
      "Epoch 1/1-Step 7170... Discriminator Loss: 1.4676... Generator Loss: 0.7798\n",
      "Epoch 1/1-Step 7180... Discriminator Loss: 1.3671... Generator Loss: 0.9741\n",
      "Epoch 1/1-Step 7190... Discriminator Loss: 1.2731... Generator Loss: 0.6481\n",
      "Epoch 1/1-Step 7200... Discriminator Loss: 1.3359... Generator Loss: 0.7306\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbwhSdV1RBXISAtNCKLK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6245438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7210... Discriminator Loss: 1.5297... Generator Loss: 0.8224\n",
      "Epoch 1/1-Step 7220... Discriminator Loss: 1.3420... Generator Loss: 0.7128\n",
      "Epoch 1/1-Step 7230... Discriminator Loss: 1.4625... Generator Loss: 0.6799\n",
      "Epoch 1/1-Step 7240... Discriminator Loss: 1.1714... Generator Loss: 0.8914\n",
      "Epoch 1/1-Step 7250... Discriminator Loss: 1.5818... Generator Loss: 0.7320\n",
      "Epoch 1/1-Step 7260... Discriminator Loss: 1.3664... Generator Loss: 0.7976\n",
      "Epoch 1/1-Step 7270... Discriminator Loss: 1.5032... Generator Loss: 0.4953\n",
      "Epoch 1/1-Step 7280... Discriminator Loss: 1.5580... Generator Loss: 0.9459\n",
      "Epoch 1/1-Step 7290... Discriminator Loss: 1.3796... Generator Loss: 0.7077\n",
      "Epoch 1/1-Step 7300... Discriminator Loss: 1.4201... Generator Loss: 0.8195\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6514d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7310... Discriminator Loss: 1.4778... Generator Loss: 1.0325\n",
      "Epoch 1/1-Step 7320... Discriminator Loss: 1.4020... Generator Loss: 0.8997\n",
      "Epoch 1/1-Step 7330... Discriminator Loss: 1.3293... Generator Loss: 0.7710\n",
      "Epoch 1/1-Step 7340... Discriminator Loss: 1.4421... Generator Loss: 0.7427\n",
      "Epoch 1/1-Step 7350... Discriminator Loss: 1.3939... Generator Loss: 0.7963\n",
      "Epoch 1/1-Step 7360... Discriminator Loss: 1.3132... Generator Loss: 1.0574\n",
      "Epoch 1/1-Step 7370... Discriminator Loss: 1.4085... Generator Loss: 0.7144\n",
      "Epoch 1/1-Step 7380... Discriminator Loss: 1.6673... Generator Loss: 0.7668\n",
      "Epoch 1/1-Step 7390... Discriminator Loss: 1.7228... Generator Loss: 0.6862\n",
      "Epoch 1/1-Step 7400... Discriminator Loss: 1.3159... Generator Loss: 0.8038\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLVBkUZJ6NhPzG94keh5
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da602cfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7410... Discriminator Loss: 1.2452... Generator Loss: 0.9329\n",
      "Epoch 1/1-Step 7420... Discriminator Loss: 1.3113... Generator Loss: 1.0131\n",
      "Epoch 1/1-Step 7430... Discriminator Loss: 1.3944... Generator Loss: 0.8405\n",
      "Epoch 1/1-Step 7440... Discriminator Loss: 1.5701... Generator Loss: 0.6464\n",
      "Epoch 1/1-Step 7450... Discriminator Loss: 1.5497... Generator Loss: 0.6989\n",
      "Epoch 1/1-Step 7460... Discriminator Loss: 1.3282... Generator Loss: 0.8715\n",
      "Epoch 1/1-Step 7470... Discriminator Loss: 1.3463... Generator Loss: 0.9407\n",
      "Epoch 1/1-Step 7480... Discriminator Loss: 1.3872... Generator Loss: 0.8756\n",
      "Epoch 1/1-Step 7490... Discriminator Loss: 1.4650... Generator Loss: 0.7903\n",
      "Epoch 1/1-Step 7500... Discriminator Loss: 1.6060... Generator Loss: 0.7171\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6492438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7510... Discriminator Loss: 1.5941... Generator Loss: 0.9093\n",
      "Epoch 1/1-Step 7520... Discriminator Loss: 1.4365... Generator Loss: 0.8351\n",
      "Epoch 1/1-Step 7530... Discriminator Loss: 1.2449... Generator Loss: 0.9179\n",
      "Epoch 1/1-Step 7540... Discriminator Loss: 1.2767... Generator Loss: 0.7730\n",
      "Epoch 1/1-Step 7550... Discriminator Loss: 1.3225... Generator Loss: 1.0675\n",
      "Epoch 1/1-Step 7560... Discriminator Loss: 1.4835... Generator Loss: 0.6993\n",
      "Epoch 1/1-Step 7570... Discriminator Loss: 1.5442... Generator Loss: 0.7474\n",
      "Epoch 1/1-Step 7580... Discriminator Loss: 1.2541... Generator Loss: 0.8181\n",
      "Epoch 1/1-Step 7590... Discriminator Loss: 1.5195... Generator Loss: 0.8365\n",
      "Epoch 1/1-Step 7600... Discriminator Loss: 1.3852... Generator Loss: 0.8290\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ng0JyQaxTrmehoxD77Dw
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da5e43978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7610... Discriminator Loss: 1.4413... Generator Loss: 0.6986\n",
      "Epoch 1/1-Step 7620... Discriminator Loss: 1.1399... Generator Loss: 1.0401\n",
      "Epoch 1/1-Step 7630... Discriminator Loss: 1.6043... Generator Loss: 0.8657\n",
      "Epoch 1/1-Step 7640... Discriminator Loss: 1.2415... Generator Loss: 0.7136\n",
      "Epoch 1/1-Step 7650... Discriminator Loss: 1.4320... Generator Loss: 0.8120\n",
      "Epoch 1/1-Step 7660... Discriminator Loss: 1.4469... Generator Loss: 0.7746\n",
      "Epoch 1/1-Step 7670... Discriminator Loss: 1.5370... Generator Loss: 0.4515\n",
      "Epoch 1/1-Step 7680... Discriminator Loss: 1.4170... Generator Loss: 0.7300\n",
      "Epoch 1/1-Step 7690... Discriminator Loss: 1.3316... Generator Loss: 0.7466\n",
      "Epoch 1/1-Step 7700... Discriminator Loss: 1.3313... Generator Loss: 0.7142\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da5c6c550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7710... Discriminator Loss: 1.3184... Generator Loss: 0.9367\n",
      "Epoch 1/1-Step 7720... Discriminator Loss: 1.3341... Generator Loss: 0.7573\n",
      "Epoch 1/1-Step 7730... Discriminator Loss: 1.1874... Generator Loss: 0.8370\n",
      "Epoch 1/1-Step 7740... Discriminator Loss: 1.4749... Generator Loss: 0.7949\n",
      "Epoch 1/1-Step 7750... Discriminator Loss: 1.4547... Generator Loss: 0.8975\n",
      "Epoch 1/1-Step 7760... Discriminator Loss: 1.3196... Generator Loss: 0.8681\n",
      "Epoch 1/1-Step 7770... Discriminator Loss: 1.1391... Generator Loss: 0.8448\n",
      "Epoch 1/1-Step 7780... Discriminator Loss: 1.5548... Generator Loss: 0.8652\n",
      "Epoch 1/1-Step 7790... Discriminator Loss: 1.3386... Generator Loss: 0.5819\n",
      "Epoch 1/1-Step 7800... Discriminator Loss: 1.3970... Generator Loss: 0.8801\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nd0cIzowLelDQQ3NRi4L
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da5af5470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7810... Discriminator Loss: 1.2564... Generator Loss: 0.8303\n",
      "Epoch 1/1-Step 7820... Discriminator Loss: 1.5232... Generator Loss: 0.7860\n",
      "Epoch 1/1-Step 7830... Discriminator Loss: 1.4922... Generator Loss: 0.8832\n",
      "Epoch 1/1-Step 7840... Discriminator Loss: 1.1502... Generator Loss: 0.7599\n",
      "Epoch 1/1-Step 7850... Discriminator Loss: 1.3228... Generator Loss: 0.7369\n",
      "Epoch 1/1-Step 7860... Discriminator Loss: 1.4072... Generator Loss: 0.9205\n",
      "Epoch 1/1-Step 7870... Discriminator Loss: 1.5092... Generator Loss: 0.7181\n",
      "Epoch 1/1-Step 7880... Discriminator Loss: 1.2182... Generator Loss: 0.8111\n",
      "Epoch 1/1-Step 7890... Discriminator Loss: 1.4104... Generator Loss: 0.8939\n",
      "Epoch 1/1-Step 7900... Discriminator Loss: 1.2429... Generator Loss: 0.7198\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da58dc7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 7910... Discriminator Loss: 1.4885... Generator Loss: 0.7308\n",
      "Epoch 1/1-Step 7920... Discriminator Loss: 1.5290... Generator Loss: 1.0555\n",
      "Epoch 1/1-Step 7930... Discriminator Loss: 1.2758... Generator Loss: 0.9313\n",
      "Epoch 1/1-Step 7940... Discriminator Loss: 1.3991... Generator Loss: 0.7532\n",
      "Epoch 1/1-Step 7950... Discriminator Loss: 1.3558... Generator Loss: 0.7273\n",
      "Epoch 1/1-Step 7960... Discriminator Loss: 1.2681... Generator Loss: 0.8287\n",
      "Epoch 1/1-Step 7970... Discriminator Loss: 1.4279... Generator Loss: 0.8256\n",
      "Epoch 1/1-Step 7980... Discriminator Loss: 1.4797... Generator Loss: 0.9346\n",
      "Epoch 1/1-Step 7990... Discriminator Loss: 1.4607... Generator Loss: 0.7711\n",
      "Epoch 1/1-Step 8000... Discriminator Loss: 1.4272... Generator Loss: 0.7907\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nX20aF7NHUlQ+a2wNvgL
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da5d799b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8010... Discriminator Loss: 1.2898... Generator Loss: 0.7857\n",
      "Epoch 1/1-Step 8020... Discriminator Loss: 1.4224... Generator Loss: 0.6170\n",
      "Epoch 1/1-Step 8030... Discriminator Loss: 1.3454... Generator Loss: 0.7413\n",
      "Epoch 1/1-Step 8040... Discriminator Loss: 1.3122... Generator Loss: 0.7526\n",
      "Epoch 1/1-Step 8050... Discriminator Loss: 1.4496... Generator Loss: 0.8310\n",
      "Epoch 1/1-Step 8060... Discriminator Loss: 1.2493... Generator Loss: 0.7688\n",
      "Epoch 1/1-Step 8070... Discriminator Loss: 1.3621... Generator Loss: 0.6786\n",
      "Epoch 1/1-Step 8080... Discriminator Loss: 1.5462... Generator Loss: 0.7908\n",
      "Epoch 1/1-Step 8090... Discriminator Loss: 1.4604... Generator Loss: 0.7219\n",
      "Epoch 1/1-Step 8100... Discriminator Loss: 1.4296... Generator Loss: 1.0000\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da58f4550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8110... Discriminator Loss: 1.4757... Generator Loss: 0.8027\n",
      "Epoch 1/1-Step 8120... Discriminator Loss: 1.4487... Generator Loss: 0.6392\n",
      "Epoch 1/1-Step 8130... Discriminator Loss: 1.4130... Generator Loss: 0.7898\n",
      "Epoch 1/1-Step 8140... Discriminator Loss: 1.4286... Generator Loss: 1.0276\n",
      "Epoch 1/1-Step 8150... Discriminator Loss: 1.5710... Generator Loss: 0.7437\n",
      "Epoch 1/1-Step 8160... Discriminator Loss: 1.3216... Generator Loss: 0.9824\n",
      "Epoch 1/1-Step 8170... Discriminator Loss: 1.4902... Generator Loss: 0.7336\n",
      "Epoch 1/1-Step 8180... Discriminator Loss: 1.3396... Generator Loss: 0.7715\n",
      "Epoch 1/1-Step 8190... Discriminator Loss: 1.4304... Generator Loss: 0.9224\n",
      "Epoch 1/1-Step 8200... Discriminator Loss: 1.4550... Generator Loss: 0.7752\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMJVl2HnZubG/fcq2sfevu6up9dpEiNeKIIilapH5I\nBEnDkG0CA8OWQcMCLFo/vAAyIP/wQviHYMGSTAKyRNoiIYKWZFFcJK7DmeHM9N5d3bVXZlWuL9++\nRMT1j3NunC+nqrqzp3typpn3AI2Kvi9fLDfixfnud875jrHWkjdv3o6XBd/pE/DmzdvRm//he/N2\nDM3/8L15O4bmf/jevB1D8z98b96Oofkfvjdvx9D8D9+bt2NoH+qHb4z5YWPMW8aYd4wxP/dRnZQ3\nb96+vWa+1QQeY0xIRG8T0Q8S0V0i+jIR/ZS19vWP7vS8efP27bDoQ3z3M0T0jrX2OhGRMeafEtGP\nE9Fjf/iVStk2mnUiIsqynIiIZpNp8XmWZgc+IyLK3+/FJJ/bA0MH/u+hrxhj+F8yj/wr6/7PPmLs\nEeb29/CxHx4rjg3fiUO9DavLbfmOfj/PeT6iSP+uVEoePk6u8xYGgRxHP0/ns2J7MuZ5z+apHqe4\nbD14nut2Jtsp3J9ZJvcMjo0WyAmEoYJLt3+cqSDQz43h7SgMi7FyqURERIPR8JHnlss54ZjuD+Y6\n5nmrlnX+kiTWz6PgofPVudSxPNd5m87m7sL0O3JPA7gGC+cRRnzMKCk99Hkqc0pElKZ6nHTG9y+D\n+2hy/ls3z3v7fRqMx3DXH20f5od/iojuwP/fJaLPvtcXGs06/bWf/lEiIup3J0REdOvtm8Xnew/2\n+LPhpBgbTnXbXY2Fm2vlBuANn2U6WdbmB75LRBRFfDMi+MFl8HmWuxdQ9tAYPeJHjDcXfwDuBebO\nAb9TgYdtrbVSbP/sf/Jjsh89zmA0JiKilcXVYuz8uTO6Tzn8bDwqxlrlMhERxXpqtPPgbrH99qtv\nExHR7oP9Ymw65WtM4Xx7E33I+iPe3u7rj+/uXpf/bjguxvBFV5Lr7LRqxVhGvP85/FCq1WqxHcqP\nfLHTKsauXrxARET/9qtfKcZmI70/vf0BX8NkXoy5KYxi/ZGfOXGKiIiee+JCMXbx7HKxvbrCjqkl\nDoqIqFHhcyvDy7Y36Bbbt26vExGRTfUpa3UWiIio0mgUY2mi328unSQiooVT54sxK9e93esXY9ub\nm7q9fo+IiHbXbxdj8YjvXzPmY/9Pv/jP6DD2bSf3jDFfNMZ8xRjzlfF48v5f8ObN27fdPozHv0dE\nZ+D/T8vYAbPW/n0i+vtEREsrC3YgHrw34DdVv7db/O1oxG/t0VTf2hOAOg6mWdKxMGDvUU70UlpJ\npdh2nno00ZdOKWE3uHZiqRgzsXrg7oj/ttvVN+94yN40PQCNH4a3B2Cy5WMfgP/ihsYz/e7mSL3u\neCZeDGCl227X1XvUY4WIwxGfZzbSa5wJLOzu7xRjr7z8SrH9tVfeJSKiHqCrXHARwuAAlhc7A/bq\nuwP17gNZMswBZSHqmeWCGAL9nASFpDAttcVysb24xtfZbOn1mjrfn919vSfzie5zNJL7M1MU4GB0\nq637efr5J4iI6Oxauxgr6a2nwPBJJYAG8zk/j8ORXvewD+chSKnbHRRjG1uMCEp1QA6A2PKIEUGp\nBc+35fMNM723kdVnuZo0+fMF3U9Skd/EpC/n/74on//uUH/1aPsyET1hjLlgjEmI6CeJ6Nc+xP68\nefN2RPYte3xrbWqM+RtE9P8Rv8P/obX2tff6Tpal1BuwB+rev89jXV0rkawns1Q9RgBvXse34Nq7\nU2NP8cwpfQv+wAtXiu32Ar/t31lXOuLaPT72+dMnirGLF2DNHPFb8x35OyKil9/m77/2tq6Tu/u8\n1p2nej4GvHso3j+lh8nGHLzidKYE51CWQ0Gub+5yIJ4Y0URf5y0V9BQCH7K9w+f+1utvF2Nfeu2G\nft7jv42BBKiJ9yiRji1X1RNfaLCXHE91vX5vnz3tel+94dZAuYbBlO/pEFBCHD1McA63FfmtLfGa\n+kRtsRhbELc8Ah5j1tfrzeZ8D5AoXVpkD/nJTz5TjD1/cY2IiOohIAO4Z9GMPfBoe6sYmwwEiQ6U\n25jDM1qt8Xw0FxVFjIS0ngIqDMf6/b48/6FR776wzOdWMnoN8UCfjWTEyCOA560iaNAKMjusx/8w\nUJ+stf+CiP7Fh9mHN2/ejt585p43b8fQPpTH/6CWZxlNuj0iIpr2GPYEAIUiwfIVYFtaywrhG2WG\nRQjTfvR7niciou8BeH/y1Mliu1RiGJZHCl8HAw4b7u8rvKw2msV2s83ESxQpDBuNGaq++frXi7Ff\n+Ze/RUREf/jKrWJsY0sJnqnARgtLExdvNoFCMiTDpkJSlSOF2HHZkT5AoE0VNtoJb6cjJZw2bnPI\n5876A/07OM6FFQ6VnTnRKcbOnGay89QJDS8uL+t2Vc6jVILYszxCPYCkr736ZrH9O1/n1d+1B0oy\ndmU5g8uddKbXtr3J92dlSc9tZYFhcD6H0CiSgxJqO7Wm5/uJz7xIREQvXH1K9yPnHsyB1EyVTLYy\nx3gckvtXb+gSp1bT7WaL5xIJQWv5/mawZJtY9bMDOWSc6tIlGPNvw4RKrlYM3HPZ/QSWfG6PRp7v\nQyJ97/G9eTuOduQefyBkXioJHxG89cslPp3lk/rWfvpZfVsvVxkJvHD2VDH2/NXLRETUaauHLAFh\nFUqySFDRz5tLF3l/6VoxNpv0iu2ozOcRJ5pUUmtLYkfn+4qxC0IU/e7v/34x9ou/8kfF9pu32cth\nool7I+ObGT3fqM+IYWlFQ1DLLUYjSaTv6Qw8Viak3ghCXftdRgEREHVXTit6uvrkJSIiOnfxbDG2\ncpI/by9juEg9G2XspsIQM9DYg54K9FG6eFXJtM997+eIiOiN198oxn7zqy8TEdGXrt0sxrozvZ7+\nHp97twthzjUmCS0SW4DiTkto9tOfulqMPXvlHBERLdXUg1bF6wYWYniwn0SSbOJYnxd5hCiI9Dvl\nqobZXEZlDiStQww5pI5V4F6UZ+y1ZxZSxyZ873Pw+BFkCMZy++fgrotn5wOm3nuP783bMTT/w/fm\n7RjakUL9NM1oe0uyzAYM7VoAGxcl7vrCJ5Soe+7K5WL7TIc/PwOEU03QUzCF/PxM95mHroBCybBA\nYp9xSaF8UFEYl2a8DEkn8B2BspD6TgsdhvovPqPQ9rXXNV+gL3HXwT0ltoocBURmAPtrQqB1WhoT\nbrqMPcyIg+KmXLLVgPujeo2XJk9cUKh+/tK5YvvCk0/zvjt6nIpkmSUVyJsPdV6sQH3KoUhELsQA\nZC1XFKquXeCc+MaSZkkunOD8idJv/3Yx9m9efqvY7gtRuAV1BKfPC1Gql0gtyIp76gm+tnNrepyq\nsH/hTM83jhiix5B3f6D4qcyfJyXdt94gvVFB+LDPzAOdA2vcc/dwXgcRUUkepHwO93EkxUuhkqd4\nwZFcTwxLDpvwkiQr0iC//Zl73rx5+5ia/+F783YM7UihvrVEqUDduaRYRhATvnjpNBERffal54qx\nMyuattmp8N9WoHa7iMcaBeEmehTDCemu6US+ot8JIoB+ArVSiLGmqaScZhBDDXi72dby0bWOQusz\nSwzR7+1pbH939ojCHTi3JdlXDEyzg/g5lH2aHOrXA6ntBiZ6ZZXhdKuj+QmnzivU7yxxKaqDtvx9\nnoMACoSCAM7DpYfmGJKYyxisMw7oAvDf1pp6nHOXzxMR0fcPXyzG7uxrCvIr73Kt1wBKgkfC5sdQ\nYruypM/GYlsiH3DuUe4YfD3fSKjxBKE+sOih5E+EEVbu8M8EdSJSTNOWf3OIFLi/TDPIEYDlUC4+\nF9I5aCZ19jPIIcgMlny7FHDQBfimR/2w3L73+N68HUM7Uo/Pxq84pyCzsKze8ukXOGZ/HjLv6kC8\nxO49CvFS68QuQsyEQ7E
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da5af0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8210... Discriminator Loss: 1.4445... Generator Loss: 0.7623\n",
      "Epoch 1/1-Step 8220... Discriminator Loss: 1.4200... Generator Loss: 0.7617\n",
      "Epoch 1/1-Step 8230... Discriminator Loss: 1.1172... Generator Loss: 0.8498\n",
      "Epoch 1/1-Step 8240... Discriminator Loss: 1.5483... Generator Loss: 0.7358\n",
      "Epoch 1/1-Step 8250... Discriminator Loss: 1.6734... Generator Loss: 0.7975\n",
      "Epoch 1/1-Step 8260... Discriminator Loss: 1.4886... Generator Loss: 0.6588\n",
      "Epoch 1/1-Step 8270... Discriminator Loss: 1.2841... Generator Loss: 0.8001\n",
      "Epoch 1/1-Step 8280... Discriminator Loss: 1.4488... Generator Loss: 0.8065\n",
      "Epoch 1/1-Step 8290... Discriminator Loss: 1.5152... Generator Loss: 0.7439\n",
      "Epoch 1/1-Step 8300... Discriminator Loss: 1.4619... Generator Loss: 0.7942\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da5c38f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8310... Discriminator Loss: 1.3789... Generator Loss: 0.8775\n",
      "Epoch 1/1-Step 8320... Discriminator Loss: 1.3144... Generator Loss: 0.9968\n",
      "Epoch 1/1-Step 8330... Discriminator Loss: 1.6090... Generator Loss: 0.6775\n",
      "Epoch 1/1-Step 8340... Discriminator Loss: 1.2479... Generator Loss: 0.8178\n",
      "Epoch 1/1-Step 8350... Discriminator Loss: 1.5151... Generator Loss: 0.8977\n",
      "Epoch 1/1-Step 8360... Discriminator Loss: 1.3956... Generator Loss: 0.8187\n",
      "Epoch 1/1-Step 8370... Discriminator Loss: 1.4939... Generator Loss: 0.7903\n",
      "Epoch 1/1-Step 8380... Discriminator Loss: 1.3979... Generator Loss: 0.6723\n",
      "Epoch 1/1-Step 8390... Discriminator Loss: 1.3989... Generator Loss: 0.7754\n",
      "Epoch 1/1-Step 8400... Discriminator Loss: 1.3765... Generator Loss: 0.7052\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDiNg9Rsl83dZQc53eUX
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6023b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8410... Discriminator Loss: 1.7072... Generator Loss: 0.7592\n",
      "Epoch 1/1-Step 8420... Discriminator Loss: 1.4405... Generator Loss: 0.7637\n",
      "Epoch 1/1-Step 8430... Discriminator Loss: 1.3000... Generator Loss: 1.0078\n",
      "Epoch 1/1-Step 8440... Discriminator Loss: 1.4366... Generator Loss: 0.8232\n",
      "Epoch 1/1-Step 8450... Discriminator Loss: 1.6770... Generator Loss: 0.6622\n",
      "Epoch 1/1-Step 8460... Discriminator Loss: 1.3857... Generator Loss: 0.7809\n",
      "Epoch 1/1-Step 8470... Discriminator Loss: 1.3647... Generator Loss: 0.6839\n",
      "Epoch 1/1-Step 8480... Discriminator Loss: 1.2338... Generator Loss: 0.8962\n",
      "Epoch 1/1-Step 8490... Discriminator Loss: 1.4571... Generator Loss: 0.7776\n",
      "Epoch 1/1-Step 8500... Discriminator Loss: 1.3789... Generator Loss: 1.0501\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6344208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8510... Discriminator Loss: 1.4961... Generator Loss: 0.7628\n",
      "Epoch 1/1-Step 8520... Discriminator Loss: 1.4442... Generator Loss: 0.7187\n",
      "Epoch 1/1-Step 8530... Discriminator Loss: 1.4512... Generator Loss: 0.9546\n",
      "Epoch 1/1-Step 8540... Discriminator Loss: 1.4886... Generator Loss: 0.8847\n",
      "Epoch 1/1-Step 8550... Discriminator Loss: 1.2711... Generator Loss: 0.9819\n",
      "Epoch 1/1-Step 8560... Discriminator Loss: 1.4398... Generator Loss: 0.7477\n",
      "Epoch 1/1-Step 8570... Discriminator Loss: 1.3999... Generator Loss: 0.7235\n",
      "Epoch 1/1-Step 8580... Discriminator Loss: 1.2959... Generator Loss: 0.8604\n",
      "Epoch 1/1-Step 8590... Discriminator Loss: 1.4778... Generator Loss: 0.6832\n",
      "Epoch 1/1-Step 8600... Discriminator Loss: 1.3924... Generator Loss: 0.9010\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmsLVl2JrR2jGce7rnzvW/KearKSle5PDXQlDHYgNr8\nAGODUKtxq/4AMgKJNvygQQKp+QP0rxatdoNbarptoC2spmXacrvALWG3q1xZrqzMyvG9l2+4871n\nHmLa/Nhrx/pOviHvq6y8rqy7l5TKeHHPidixI06sb39rrW8prTU5c+bscpn3Zz0AZ86cXby5H74z\nZ5fQ3A/fmbNLaO6H78zZJTT3w3fm7BKa++E7c3YJzf3wnTm7hPaJfvhKqZ9VSr2tlHpPKfWr369B\nOXPm7NM19b0m8CilfCJ6h4h+hojuEtEfE9Evaa3f/P4Nz5kzZ5+GBZ/gu18move01h8QESml/j4R\n/TwRPfKHH4WhrlRiIiLSShER0WKxKP9e5LnZWHoXyT++l5eUImU3YN/HfKf8inxSP2RLPq9g+2HH\nk52eZ7arkUx9rVYrtwM/JCKizM4FEaVpSkREOewriuKB7aLAuSoeHC6MzU4lzqkdpx2j+YpsF/xZ\njQd9yHHQfD5WBNcbReYawygq98WVipzTM0A0z+R67eHvHw/KfXkB85EXdsBy7sA31+PLucOKmesK\nzLnn+eV2kiRERJQmKRw7e/Aatcy/Z+cNzp1nfM9SOY6G79h5xefF3r8glnmptdvldqvRNH/3ffgS\n35PCjHH//h71z/of94h/oh/+DhHdgX/fJaIfe9wXKpWYvvzFLxARUcI34+bND8q/j8/OzAY81MST\nTkSU8c192EPmPewXB/vxzwE/WLgPf5w+b0YwwQU/4XYMRPKMVWJ4sEJZPWV8U3Bfq2lefC/srJX7\nvvTFV8vtXmuTiIhOhqNy3/29AyIiGg7loZ/PZ+X2dDwx/5/O5dz8AOMLwvPlGtOs4P/Lj8fjeWnU\nKg/sIyJaZNnSd4nkx5lmcp/gNNSsmR/51Z2Vct/VG9tERLR9Zbfc9/SzL5TbQb1ORERnp/Aj59/P\nX/21/6vcNxzJHI1HY3PuAOZ6o0tERJVmp9y38+JrRET0wqtfLPfFdflx3b5zl4iIju/syXlODomI\nqEiTcp/KZLvKL7I4kHmZnJrvnN6/W+4rEnFyAb8lPBjvfGGO2btxrdz3pZ/7uXL7Kz/5FfP3tlxP\nMDf3fDE7JiKiv/xv/yU6j33q5J5S6qtKqa8rpb6eptnHf8GZM2efun0Sj3+PiK7Av3d535Jprf8m\nEf1NIqJqpaLv75s3U+obONM/HZafzRfmtQ5AhhR495C9TxyCJ87N3wuAnwhPLdT0wXNFDAHrCKn4\nrU1ElLOXROjm8ThS8KB99rDzuXjN2UJebrl+0ONnDE/36+LNFp54j3xmtvsHJ+W+swODhOawLBqN\np/L3gfF8PsxBlT1JBWCur2Qcmi839+U7Fs2EhXyntQSJzf7pXMYxY48TCUigAFx+f2LG+d6tY9nH\naGTqybxdfVq8XKNiPFpckftDvL1/76jclSwE9aTsgeNIng2tjCf3Y9nX3e2Z8+1slvuaLUEjPvvC\nSMt31Nw8l6PRWblvMZbnVlXN2OrVpgyXnXJy0pcxwvx7FqIrmf/FxHz27Ph+ue/w9P1ye6Z+nIiI\nWrWNcl+tWjXHWzH3KYLl0+Psk3j8PyaiZ5VSN5RSERH9IhH99ic4njNnzi7IvmePr7XOlFL/IRH9\n32Sc9N/WWn/ncd8pSNOMSY/J1HiCLBfyw64nffAYa616uf3iFbMu3m3Lm7XBpJAlxYiIKrG4H7ue\nj6qxfKdu3pLNWqPcF8FaS7Grn/K6kYhoyp5rOBZPfe/slIiIvnVb1oP7A/nOmNdsCmY5bvB4anKN\nUU3OPeszipiJN4ss/xDINU4DOWi3Za6j1xTvvNYy17ix2i33tRsyb/W62Y4A6fih8RbIY8wBwcwW\nZmzWy5txmnnxtXxuOpM52Oc5unUoHn/Ec3k0Oi33ncwOy+3d9g0ztlDu43hi5nI4kvkneHaCqvHQ\nq8glvGi2X3z1c+W+p5+5TkRE291qua/XlvNcrRve4V5Xnpc99qZ7J/vlvmIBiIE5kQagqyA1vMvi\nhqzHjw7kORnwc5KRzOVw08z/NJTrSsfCgfUnhjffrD1b7mv45t5n2sxP4CFefrR9EqhPWut/RET/\n6JMcw5kzZxdvLnPPmbNLaJ/I4z+p6UKX8dEFQ30NsDJg0q3bFnj/lR97qdz+F155joiItjoC51Z6\nhqyp1SQkE8UC4xRDYgXQ2LeQDOLAOoV8gsKMMQNIu5ga6DYeCsFzxDB2bV0ImH/yJ5LGcPPQfHYB\nhOA8NbA9yQTq5xmGEs12B5Yzioc5nkMIKRM4uLJirn13TWD97pZZFu1sCyTtdnryfYb6fiSQVvFy\nKc+EcJpPZclhicmlfAC+tmwhc3V2elBuf3D3QzP25Nuyb9/8/c5NIb5u3pOlwAvPmWPVq3JPlW/g\ndDIVUhPzDQoy4zg9lb+r98zxAyUQfTa8SURE0+da5T7/qkD9dmiejY2qQPTWppmj3dZquc8ScURE\nSpt5gdUi1XwznqQBoTcIaumUr9eTeWvxsmui5FmcnMj2u6//KRERHV/9CflOz0D9yDPLBPWIsPZH\nzXl8Z84uoV2sx9eaFuwZUk5AKSDxQzGpd3VT3sY/9eJOuf3MhtnfaQgp12ga7x7XxHMFFfT4/CYE\n8k8xAaIhOUgn8g7kJCgqINSVB+at3vCEQKtxGE7n2+W+4xNBBPt9E/KZzgRZTGfGU/eH4klHEwkN\nrQXGa8eh3Bqb2FEAQmlW5Hp2mcC7vrMu+zbM9sqKoIB6Q5BUhYlNL5Z5o4CzKjVkLHbl+9a7o0/R\nPKZiIWhkFQjZTst4bUwUundmvOXdu+I1v/Htt8vt137kZSIiutKRexJWzDgSID3VUj6l2V4AIdvf\nM4Th0bsfyrlvvGeOA+G4hv5CuX3K95nm8my0IzNX13qCNBd1mbfRwBCOOSIyvn9ZLghkMREyc3Jq\nUEiSyd9nufltJAFk+zUgNHpg8uW+efOb5b6tliEjq/H5wnjWnMd35uwSmvvhO3N2Ce1CoX5RFLRg\nwizPbOGDEF8hk3vPbwshstUQSNvgjL0YiTrOfMI89KUiE9+zO2WfJfyACCkgDu1x7qApQOTvFOb7\nGrLAalUmgjoCbV+5sVVuf+tDk8h4NoWML87yG40EFg4GknOuVszfF0CWZbwkqcB1Y6bhxqqZr9WO\nLJEaNV4CQXFMDDn4Nq/BAyKUeFlEvsDGpUIlXgNpWHLYugodyucCX+bD981nv/T5l8t9tzmefe+P\nXi/3vfUtyWl/453vEhHR+udkWdWsNPjcUMfxEKiPdRyaebHkTOY3aZpz53uytBitAJxODPROoS7i\nqXUDp+O6XENYIFNnlizzqZyHpkz4TeU4HS1/358Ycm84kL8PuRaggFsSk/wj4efkcF9yHgZPm6VN\nGBiy9ryFbM7jO3N2Cc398J05u4R24ay+rS2XclGBWQ0udri6CqmlwG5HzMwHAMFt4YiCd9gSRLfb\nXvDgPqyj9wQ6E8dEsTzYRgIULBlsXXQlkONs9wRub/J1vHMocC6dmOufLjDF9cGilzMoy7WxfSwq\n6jYFtq9wzL9ZFWhcYbYe69xDiNkHIUP9EFh9u41Qf6kEnaMhCPU5n0AHMOcBFgOZ/St1GcePvPwi\nERH94duS//D+vjDeX/uDN4iI6JUtKdttdGw+AgwIi154oFiMZZeOMZRNa15i3nlPUmEnQ8khsNnT\n1QWkkt81DHx6fKvc1wOGv0jMMffel5Tc4/4J/02Oo0juecqRjeGZRBf6tna/JnNZBbLe4/LrAO5Z\nyudO7NL5IXoRDzPn8Z05u4R2oR6f6EHyAYm4OnuFlZZ4rgg8iV86byTvHhTaWNpmcg89dekp4Nwe\nFMBYEquAIhDr8T0Q5/B
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da59b9978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8610... Discriminator Loss: 1.3040... Generator Loss: 0.7070\n",
      "Epoch 1/1-Step 8620... Discriminator Loss: 1.2790... Generator Loss: 0.8604\n",
      "Epoch 1/1-Step 8630... Discriminator Loss: 1.2457... Generator Loss: 0.8689\n",
      "Epoch 1/1-Step 8640... Discriminator Loss: 1.4526... Generator Loss: 0.7422\n",
      "Epoch 1/1-Step 8650... Discriminator Loss: 1.3948... Generator Loss: 0.8504\n",
      "Epoch 1/1-Step 8660... Discriminator Loss: 1.3248... Generator Loss: 0.9155\n",
      "Epoch 1/1-Step 8670... Discriminator Loss: 1.5329... Generator Loss: 0.8403\n",
      "Epoch 1/1-Step 8680... Discriminator Loss: 1.3771... Generator Loss: 0.7440\n",
      "Epoch 1/1-Step 8690... Discriminator Loss: 1.3710... Generator Loss: 0.7733\n",
      "Epoch 1/1-Step 8700... Discriminator Loss: 1.2870... Generator Loss: 1.0761\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da68b26d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8710... Discriminator Loss: 1.2801... Generator Loss: 0.8817\n",
      "Epoch 1/1-Step 8720... Discriminator Loss: 1.4866... Generator Loss: 0.4617\n",
      "Epoch 1/1-Step 8730... Discriminator Loss: 1.4093... Generator Loss: 0.7667\n",
      "Epoch 1/1-Step 8740... Discriminator Loss: 1.4472... Generator Loss: 0.7394\n",
      "Epoch 1/1-Step 8750... Discriminator Loss: 1.3565... Generator Loss: 1.0057\n",
      "Epoch 1/1-Step 8760... Discriminator Loss: 1.5551... Generator Loss: 0.8387\n",
      "Epoch 1/1-Step 8770... Discriminator Loss: 1.4306... Generator Loss: 0.9035\n",
      "Epoch 1/1-Step 8780... Discriminator Loss: 1.3470... Generator Loss: 0.7870\n",
      "Epoch 1/1-Step 8790... Discriminator Loss: 1.1758... Generator Loss: 0.9317\n",
      "Epoch 1/1-Step 8800... Discriminator Loss: 1.7832... Generator Loss: 0.5622\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlw1hO3CkKHp0N7RiDjK
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da69fe7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8810... Discriminator Loss: 1.4859... Generator Loss: 0.6879\n",
      "Epoch 1/1-Step 8820... Discriminator Loss: 1.3202... Generator Loss: 0.9934\n",
      "Epoch 1/1-Step 8830... Discriminator Loss: 1.3614... Generator Loss: 0.7921\n",
      "Epoch 1/1-Step 8840... Discriminator Loss: 1.4707... Generator Loss: 0.8216\n",
      "Epoch 1/1-Step 8850... Discriminator Loss: 1.3681... Generator Loss: 0.8966\n",
      "Epoch 1/1-Step 8860... Discriminator Loss: 1.4595... Generator Loss: 0.7677\n",
      "Epoch 1/1-Step 8870... Discriminator Loss: 1.2765... Generator Loss: 0.7567\n",
      "Epoch 1/1-Step 8880... Discriminator Loss: 1.3029... Generator Loss: 1.0909\n",
      "Epoch 1/1-Step 8890... Discriminator Loss: 1.4201... Generator Loss: 0.6570\n",
      "Epoch 1/1-Step 8900... Discriminator Loss: 1.5210... Generator Loss: 0.8050\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da59b9ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 8910... Discriminator Loss: 1.6994... Generator Loss: 0.6114\n",
      "Epoch 1/1-Step 8920... Discriminator Loss: 1.5626... Generator Loss: 0.6903\n",
      "Epoch 1/1-Step 8930... Discriminator Loss: 1.6196... Generator Loss: 0.7636\n",
      "Epoch 1/1-Step 8940... Discriminator Loss: 1.3288... Generator Loss: 1.0120\n",
      "Epoch 1/1-Step 8950... Discriminator Loss: 1.4728... Generator Loss: 0.7880\n",
      "Epoch 1/1-Step 8960... Discriminator Loss: 1.5478... Generator Loss: 0.7824\n",
      "Epoch 1/1-Step 8970... Discriminator Loss: 1.5614... Generator Loss: 0.6430\n",
      "Epoch 1/1-Step 8980... Discriminator Loss: 1.5400... Generator Loss: 0.8293\n",
      "Epoch 1/1-Step 8990... Discriminator Loss: 1.4078... Generator Loss: 0.6796\n",
      "Epoch 1/1-Step 9000... Discriminator Loss: 1.4296... Generator Loss: 0.8616\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlKPJxU2T3k6+biLZX+g
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da7047da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9010... Discriminator Loss: 1.3790... Generator Loss: 0.8356\n",
      "Epoch 1/1-Step 9020... Discriminator Loss: 1.4117... Generator Loss: 0.7046\n",
      "Epoch 1/1-Step 9030... Discriminator Loss: 1.2722... Generator Loss: 0.8328\n",
      "Epoch 1/1-Step 9040... Discriminator Loss: 1.4026... Generator Loss: 0.7923\n",
      "Epoch 1/1-Step 9050... Discriminator Loss: 1.4994... Generator Loss: 0.7725\n",
      "Epoch 1/1-Step 9060... Discriminator Loss: 1.3455... Generator Loss: 0.7827\n",
      "Epoch 1/1-Step 9070... Discriminator Loss: 1.4406... Generator Loss: 0.8783\n",
      "Epoch 1/1-Step 9080... Discriminator Loss: 1.5039... Generator Loss: 0.5461\n",
      "Epoch 1/1-Step 9090... Discriminator Loss: 1.4151... Generator Loss: 0.8387\n",
      "Epoch 1/1-Step 9100... Discriminator Loss: 1.2660... Generator Loss: 0.7873\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da701b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9110... Discriminator Loss: 1.6979... Generator Loss: 0.7636\n",
      "Epoch 1/1-Step 9120... Discriminator Loss: 1.3256... Generator Loss: 0.8592\n",
      "Epoch 1/1-Step 9130... Discriminator Loss: 1.3613... Generator Loss: 0.6400\n",
      "Epoch 1/1-Step 9140... Discriminator Loss: 1.3697... Generator Loss: 0.8042\n",
      "Epoch 1/1-Step 9150... Discriminator Loss: 1.3631... Generator Loss: 0.7997\n",
      "Epoch 1/1-Step 9160... Discriminator Loss: 1.3487... Generator Loss: 0.7175\n",
      "Epoch 1/1-Step 9170... Discriminator Loss: 1.5507... Generator Loss: 0.6901\n",
      "Epoch 1/1-Step 9180... Discriminator Loss: 1.4207... Generator Loss: 0.8358\n",
      "Epoch 1/1-Step 9190... Discriminator Loss: 1.3789... Generator Loss: 0.7480\n",
      "Epoch 1/1-Step 9200... Discriminator Loss: 1.2807... Generator Loss: 0.6159\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWuMJFl2HnZuPDIj3/Wuru6enu6ensfOLPfF0Yo0Jb4p\nUJIh+odMkDYEwabAH36AtmxYlH7INmAD8h/b+iWYsGTTgCyRtkWIECRZBC2aFk2Tu9xd7mt2Znp6\nunu6612Vle/MyIi4/nHOjfPlVPVM9c5scZt1D9Co6MiM143ION/9zjnfMdZa8ubN2+Wy4I/6BLx5\n83bx5n/43rxdQvM/fG/eLqH5H743b5fQ/A/fm7dLaP6H783bJTT/w/fm7RLaR/rhG2N+0hjzpjHm\nrjHmFz+uk/Lmzdt31sy3m8BjjAmJ6C0i+gkiekREXyCin7XWfvPjOz1v3rx9Jyz6CNt+nojuWmvv\nEREZY/4hEf0UET3xhx/FFVtNEiIiyrKM/87n5ee2yPnvE15Gxhj5C+uI/xMEujIwuPzkC3jSO89t\nbmA/7pyKM7a3C+vsqc8X9u3OC04sDsNyuVKNT22b5TwuRaFHx2VnuM8wDBYvhoiCIIRl/jwI9REI\no9h9Wq7L4Txms/TUuVk5jzxLzzw39/niuJweGPYjcvSYzykK9dwTWRfAWGXzrFyeTqdyHvo8nTVG\nZ91bs/C5PE8Lz025UbkmhC8U1v09fY1PfsbkOHFcrourNSIiimq1cl211iiXG0mVjw3nUcizYec8\n/vv7u9Tv9T7gqWf7KD/8a0T0Hvz/ERH9yQ/aoJok9MrnPk9ERMf7R/L3cfn5bDwkosWbh7+qQB7m\nSqw3P5Z19aoOYLOql5VE/F0TnL7Rs1QfHLQ4ik4dZyovqhn8EmY5P1iF1X2nWV4uzzN56Avdxv0e\n2/Vque5Ku10uX799TbbVfR50eaxmk0m5biwPOhFRIefRbCXlunaLH56gUinX1eAhSpp8zEZrpVzX\nWtnibaJ6ua430R/Pvft8r6YzHbdsOubvHe6U6ybDfrk8T/mBnM/0fHN5WHMYyyhp6blvbBIR0XJH\n7+PLV9b4Gjr6vaP9brn85hvfICKik8O9ct1squPlLI6M/NWXG/7Iq3Lv3XNDpC9RE8Iz1tAf50Re\nQJOZ3vtZ5q4RXtZwHlHM97+1fqVct/Xiq0REtPbap8t1tz75+XL59ZdvExHRck3PY3LEY5DuPiQi\nov/0P/736Dz2UX745zJjzM8T0c8TEVWqyYd825s3bxdhH+WH/5iInoP/X5d1C2at/SUi+iUioqTR\nsP0hv6EGoxMiIpqMB+V3ixL2qycwRt/MkbyF8W0dlov6Pk0z9Uih4X3VIoBUAhfHs1m5bj4HT53z\n9tNMjzMWmAvOm4zsx8I5FkUOy3bhLxFRJp6gmyuqyVJdnkb8eb3e1GPP2auGpPuuwJ1rtthDX9ns\nlOsSgYV5gLdYvf9UjnncPSnXpYZfzEWg3jktdJvhQBCZgX3K8hzc2RxQjw143AurU4Es4/07NEBE\nlMEYBUNGJilA+aTKY13t7ZfrDsHjHxzt8n4m6OV5n4gGW3VebiR6DTWA26FMc4YTfTamDhkGepFV\nuAFTeY4mgITmcj1zGJgs18+DgLdJYfo1De8SEdE2XPe4ea1cvn3rJl9PQ7c5lp9wUOXnpTDn4+s/\nCqv/BSJ60RhzyxhTIaKfIaJf/wj78+bN2wXZt+3xrbWZMeY/IKL/k4hCIvp71tpvfNA2eZZR/4Q9\nzOiE39YWCBjjCCcgLyoVnQu3mzyvWmrqG3qlwZ9X1fVTHAAicPuCiVwk3+00dZ42mqiXcyROZuHc\nBvwGx/ltIAhkbk9tystuARievCTq9HtjeEt3R+zdc6vrltfZA7ZhPyFQUs9dWyciomubq+W6WOaQ\noxS4gp5e4/6QPU5vot652xsREVEGns0AyjCWr7dIwaPLeUSBjiUViuKcb2Hf4D6fuY1Ly2fjcnks\nfEHW0Klht8EeLZsc6rq9o3LZoQckBJtVPuatq8vlulevM1dwfVXXNSt67ql46O1jRROPD3j5cKDn\naOEZq8WMuKZVfTaGsp/BGJDDDEhPebbm41G5bnTEaCYFf/zw618rl39nlb3/66/d1PMVgBPJ3+w0\nn3mmfaQ5vrX2nxLRP/0o+/DmzdvFm8/c8+btEtp3nNVHs0VRhuwcGRdWgFgRKGsAAtZrChGf2+QQ\n1Ks3NAT1yeevEhFRC8JBGRB1k6nEngGakSzPgNzrnfTK5VggfBDrNu/uMHn05oPdct1YcFWAjB++\nSy2fB8J/QfoL63C605Ew0dbyUrlu5YoQN12F0O2WhuY+8fJNIiLaWNNxKXI+QG+kxGEYKqw0MWPD\nyAB8FcJrDuRdtabjGgmsx2mRC8lFpOczH+v2RcHbFBmQdyEvBxnmJQApOuPrzCMIj0343GdDHQMM\n+8YVvmctIOpe2eLx+PHPvVyue+kqT4uQ0DMW7xmv/8QNJdX2j3l6+u6eEou7J3oePZmeTYGk3Yj5\nuR3AtOgAthmOheAknR7YCYdB513d5uSN/6dc/qbl/U+731uuW1vha7waOiJZt/0g8x7fm7dLaBfr\n8a2lLGUv65IiLGbcSRguAm9447qGtf78D90hIqLXX7pZrrvS2eBtSJNObK7vs9GQEUYG3qEMw+X6\ndhyCJ3EJKCEk8Fxb4je4gf28e8ieYAqkW3+E4Tw5HhBxeRmKtPA99Xwu+ajV0FszmY1kP+odbty5\nWi7fvHOTiIhqiZJUkshF9Zbuu93U8NnqiO/DYVdRQCYJKtNC97O0osfpj3n7g2Ml1QYDHrc9AnLu\nWL1pIb6lKHRdJoTjHFNaDGa9STbgXBHZZMT3MR3p+WLiTSJhulubilB+5LO3iIjoUy9tlOtqQv4Z\nCJmhx3f3KqlA1mCFw6TtlqLP6/1hubx9cEBERDsHx+W6uTwTm20dyyTQY+4JgToCdJoZIfxyDUnm\nfUUZ6d5bREQ0O1gr1w0Cfh678qxmuff43rx5e4L5H743b5fQLhTqE1kyAmtdQUgBMeNQct63lhS2\n/5s/9D3l8o+9/gkiIlpuaW57ZAWCW4VhptD3WSfh9RlkiVEJ9RV6FUsKEUd9hmfzXKGmCykPBzol\nSGXaMrAKr0IDUEvIqznCWEd2AcrFzL6qzHMKzEQUgvKFDSXvvudlJaw21jm33cXuiYgC4vE1Vm9x\nBgkHk5SXR5CLP5ThGIz1e53ldT0PyWHonuh57BxwzD2wmgE47ur0jAyfRxTiBfO4FZmShFhwUwjU\nN5BHUaRT2Ua/127ocdY3mVz8/Ms6NXn5OkP8GkwJQsmdD8HnBUD8uhySfKEgR4qGAiUwm4nmGDQF\nZjdhjnogZHGcwDQCsiCtXM/RWK/RjcYMniFT6DNIkrU43rkLn/O0oL3EeQkWMkI/yLzH9+btEpr/\n4XvzdgntgqG+xq+1VFVhZU1i+t9zc7Nc970SoyYiWu4wxI+QhXXMOWDnEAt7pI67AmWW7tC20Mu3\nFlJxiY8zGWl5Kcnn19c11fPBDqePpkNleLHcNp0yZBuOsMzY1WlDIZJ+SlUpKBmPFQaHNb6em9ev\nl+s6DZ3uVAM+Zq2i0NdNgcjqdc+x/FTyI+otHYOKTAWCE4WXzVinXTNh2eek11gTqNqESEojUkgb\nVnh7ZOjzhCMAGUQhZlavdy5wFbMjCommhFCPXwe4vbHGeQ9bq1qoZKwrlIGpmMB6A+OPKeLubhgo\nnnGfYip4AZu02y05X30GA5neDaA0uFPXsV6VCMEcntv5JD218ypME9MeRw1231bJixUpbFuRiE+R\nn11q/n7zHt+bt0toF+/x3UJJ8unbbUXEIz55S8UJlpf0DR44T1/A21g8WmAgAzCArCx5c6PiSunx\nIebpMt2IiKx4rCwC7y1CEo2aepnVNnuzIygFxfLShvPeE8jOKg+OHgcQipzvGIpnVkRgY7Wj2Xwx\nCpS4wp8RxNInTC71err
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da72d7128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9210... Discriminator Loss: 1.5555... Generator Loss: 0.9003\n",
      "Epoch 1/1-Step 9220... Discriminator Loss: 1.5975... Generator Loss: 0.8965\n",
      "Epoch 1/1-Step 9230... Discriminator Loss: 1.3636... Generator Loss: 0.6376\n",
      "Epoch 1/1-Step 9240... Discriminator Loss: 1.2529... Generator Loss: 0.9186\n",
      "Epoch 1/1-Step 9250... Discriminator Loss: 1.4379... Generator Loss: 0.8917\n",
      "Epoch 1/1-Step 9260... Discriminator Loss: 1.6029... Generator Loss: 0.8227\n",
      "Epoch 1/1-Step 9270... Discriminator Loss: 1.5843... Generator Loss: 0.7934\n",
      "Epoch 1/1-Step 9280... Discriminator Loss: 1.5361... Generator Loss: 0.5071\n",
      "Epoch 1/1-Step 9290... Discriminator Loss: 1.3000... Generator Loss: 0.8819\n",
      "Epoch 1/1-Step 9300... Discriminator Loss: 1.4055... Generator Loss: 0.8635\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dbc6df3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9310... Discriminator Loss: 1.4435... Generator Loss: 0.8696\n",
      "Epoch 1/1-Step 9320... Discriminator Loss: 1.4161... Generator Loss: 0.8086\n",
      "Epoch 1/1-Step 9330... Discriminator Loss: 1.2543... Generator Loss: 1.0072\n",
      "Epoch 1/1-Step 9340... Discriminator Loss: 1.3282... Generator Loss: 0.8115\n",
      "Epoch 1/1-Step 9350... Discriminator Loss: 1.5271... Generator Loss: 0.6300\n",
      "Epoch 1/1-Step 9360... Discriminator Loss: 1.4608... Generator Loss: 0.8856\n",
      "Epoch 1/1-Step 9370... Discriminator Loss: 1.4150... Generator Loss: 0.7733\n",
      "Epoch 1/1-Step 9380... Discriminator Loss: 1.3764... Generator Loss: 0.6170\n",
      "Epoch 1/1-Step 9390... Discriminator Loss: 1.4807... Generator Loss: 0.8092\n",
      "Epoch 1/1-Step 9400... Discriminator Loss: 1.3529... Generator Loss: 0.9089\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n29Gow2DyKPG4CjPwvV0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da70affd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9410... Discriminator Loss: 1.2223... Generator Loss: 0.8422\n",
      "Epoch 1/1-Step 9420... Discriminator Loss: 1.3734... Generator Loss: 0.9582\n",
      "Epoch 1/1-Step 9430... Discriminator Loss: 1.3146... Generator Loss: 0.7735\n",
      "Epoch 1/1-Step 9440... Discriminator Loss: 1.3670... Generator Loss: 0.7866\n",
      "Epoch 1/1-Step 9450... Discriminator Loss: 1.4191... Generator Loss: 0.8354\n",
      "Epoch 1/1-Step 9460... Discriminator Loss: 1.3997... Generator Loss: 0.6151\n",
      "Epoch 1/1-Step 9470... Discriminator Loss: 1.4929... Generator Loss: 0.6021\n",
      "Epoch 1/1-Step 9480... Discriminator Loss: 1.5720... Generator Loss: 0.8717\n",
      "Epoch 1/1-Step 9490... Discriminator Loss: 1.4754... Generator Loss: 0.8908\n",
      "Epoch 1/1-Step 9500... Discriminator Loss: 1.4898... Generator Loss: 0.8116\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6909b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9510... Discriminator Loss: 1.2624... Generator Loss: 0.8489\n",
      "Epoch 1/1-Step 9520... Discriminator Loss: 1.3049... Generator Loss: 0.6696\n",
      "Epoch 1/1-Step 9530... Discriminator Loss: 1.3965... Generator Loss: 0.8644\n",
      "Epoch 1/1-Step 9540... Discriminator Loss: 1.4915... Generator Loss: 0.9424\n",
      "Epoch 1/1-Step 9550... Discriminator Loss: 1.4157... Generator Loss: 0.7676\n",
      "Epoch 1/1-Step 9560... Discriminator Loss: 1.4209... Generator Loss: 1.0694\n",
      "Epoch 1/1-Step 9570... Discriminator Loss: 1.4742... Generator Loss: 0.6127\n",
      "Epoch 1/1-Step 9580... Discriminator Loss: 1.4572... Generator Loss: 0.7362\n",
      "Epoch 1/1-Step 9590... Discriminator Loss: 1.4579... Generator Loss: 0.8288\n",
      "Epoch 1/1-Step 9600... Discriminator Loss: 1.5529... Generator Loss: 0.6160\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNg32ug0Q1WiGQhbPMwn
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db61e2f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9610... Discriminator Loss: 1.2765... Generator Loss: 0.9334\n",
      "Epoch 1/1-Step 9620... Discriminator Loss: 1.4019... Generator Loss: 0.8936\n",
      "Epoch 1/1-Step 9630... Discriminator Loss: 1.3948... Generator Loss: 0.9540\n",
      "Epoch 1/1-Step 9640... Discriminator Loss: 1.4181... Generator Loss: 0.6897\n",
      "Epoch 1/1-Step 9650... Discriminator Loss: 1.7385... Generator Loss: 0.6183\n",
      "Epoch 1/1-Step 9660... Discriminator Loss: 1.3200... Generator Loss: 1.0546\n",
      "Epoch 1/1-Step 9670... Discriminator Loss: 1.3607... Generator Loss: 0.7846\n",
      "Epoch 1/1-Step 9680... Discriminator Loss: 1.2170... Generator Loss: 0.8661\n",
      "Epoch 1/1-Step 9690... Discriminator Loss: 1.3859... Generator Loss: 0.7578\n",
      "Epoch 1/1-Step 9700... Discriminator Loss: 1.3041... Generator Loss: 0.9085\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db5a09cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9710... Discriminator Loss: 1.3318... Generator Loss: 0.7355\n",
      "Epoch 1/1-Step 9720... Discriminator Loss: 1.4124... Generator Loss: 0.7767\n",
      "Epoch 1/1-Step 9730... Discriminator Loss: 1.5481... Generator Loss: 0.8282\n",
      "Epoch 1/1-Step 9740... Discriminator Loss: 1.2969... Generator Loss: 0.7407\n",
      "Epoch 1/1-Step 9750... Discriminator Loss: 1.4520... Generator Loss: 0.8938\n",
      "Epoch 1/1-Step 9760... Discriminator Loss: 1.8065... Generator Loss: 0.5857\n",
      "Epoch 1/1-Step 9770... Discriminator Loss: 1.3169... Generator Loss: 0.9921\n",
      "Epoch 1/1-Step 9780... Discriminator Loss: 1.3492... Generator Loss: 0.7576\n",
      "Epoch 1/1-Step 9790... Discriminator Loss: 1.4472... Generator Loss: 0.6129\n",
      "Epoch 1/1-Step 9800... Discriminator Loss: 1.3531... Generator Loss: 0.9124\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsvWmMZNlyHhbn3pv7Uln70tXd1dv0TM/yZt5KijTfk56f\nTdImKRgCTcowBIvA+2FboGEDJm0YXgAbkH94kf8IlkXJtCFLpG3SogSBNMHFgkjxkcO3zr703l1d\ne1bumXc5/hFxbnw5VT1TPUvxjeoEMJjbJyvvcu7NG9/5IuILY60lb968nS0L/qxPwJs3b6dv/ofv\nzdsZNP/D9+btDJr/4XvzdgbN//C9eTuD5n/43rydQfM/fG/ezqB9pB++MeZHjTFvGmPeMcb84sd1\nUt68eftkzXzYBB5jTEhEbxHR14joPhH9CRH9rLX2tY/v9Lx58/ZJWPQRvvtFInrHWnuTiMgY8w+I\n6KeI6LE//GK5bMu1BhERJZOYiIiyNNU/kHdQYHQINqlUCImIqBCGMJrJdzPdzXEvM9hRGDLQSeE7\naaznkWb8/Qx2k1o3pjsygZxPqaTnWCzk27VykYiIGvXqke/YTI+XxJN8++bdh0evwW3DUAiTVJDr\nqRT0dhZlOzA4g7oDIxMShMGRMTQD37fy/TjRcx9NJvL/JB9LM53XLP+u7ieRa89SvGdTB+VzC/Q+\nu+sZw1zZ7Jh7/v633u2aokCvG7fdXBbhGYtk28LOE3g43PXiWJKPwVzAReani8/t0VN/z9m7a9Cx\nIOB5cc/deDyiJImPfuk99lF++OeI6B78+z4Rfen9vlCuNegHfuzfICKiR/ceERHRsNPJPw/k4SnB\nw1gJdTo2lmf5wLO1fMwmQ/5/qg9EOrXNE2v190gzc3UiIuoP+/nY3q6eR3cw4nOL9TvdCZ9HfwKT\nXp8hIqK1jSv52LWNlXz7i09vEBHRj/zA5/Kxco2/Mxq087HdRzqNf/mv/WdERBTjDynm7SjRuagX\n9datzvB8PLu+mI9dWJwjIqIq/J2BRyuSH1WjXtexKMr/Mr9GuBexPMQ77YN87LVbfO5vPdjOx9rD\ncb49kic8NvpD2u3wvHe7g3wsAQcQRHyzqjJXREQXV5eIiOjNe7f0fOQ+ERFlMd8sfBm4qwjhh1KO\n+DzmK5V8bKFWzrfXmuyY1mb12IszPDaBl/V+f5hvHwz5eTsY6XO31eNra8PfDWJ9oMZyTyeJfieT\nF4ed+rHDaty9ECN1NLU6/yauXeDn7tXXvk0nsU+c3DPGfN0Y87Ix5uV4NPrgL3jz5u0Tt4/i8R8Q\n0Xn497qMTZm19m8R0d8iImrMzNrRwR4REcUHW3wCiXoHI2/ECD2+wGUionM19mIbDfUezrnPzc3n\nY9WKfqdz2CUiop2OeqmVFfZyidG/e1jQ87hzn7+zO9GxgwGfW7utL6/R1iYREXUPdN9h/HS+fbnC\n1xG99KxeT5PPs9yc1e+AJx4d8r4QdrslTqOs1319tZlvf+kpvg3XN1bzsVnxUo1GIx8rV3Q7KvC1\nRyX1ds67T60ywMtZy9sJ3LMvbvGxX33l9Xzs1ZsP8+13d9vu4DpWYq/87lA9fpwownGHHI308y2Z\n4xTGskQ9qIPhiGqK4t0XqnqNNxb43n/lqfV87KKgIyKipSVGTbWGIqGqoCKE+qOxzkFXvHp/omjj\n7g5fNyKh2/uH+fajLn/n/gEgTUEJuJTKppYC7pnQsSTlOdhp78u/dR7fzz6Kx/8TIrpmjLlkjCkS\n0c8Q0W98hP158+btlOxDe3xrbWKM+feJ6LeIKCSiv2OtffX9vpNlGY37vL4z8rZvRnoKtRK/WefA\nY6/OqGdbTnntV2jrW3+xwW/z83P6Bse3da/Kb94I18x7/LZtLeq+66u6Np+Tt+yjnZ5e75hRwF6q\nxx7Lm34yup+P3Qp0/fV2wKRe77P7+Vhzls8zqijhV63oGz4Wssyk6l1mC3ye61W9rhdmdT1/Y5H3\nuVhp5WPlAs9Lo6hr1RJ4/FA+N6HOf1Dg+TVAqmXgQZIR3zsbKmFSPXeViIjmanrsK8u6Dn/tjXeI\niGh/pNd4eZY97KSvYzc3d/U4cunJRD/vd4UMBgSCxHBOVsJ6viXX88Pz6tH/0jOXiYjo+nXlZWYW\nl/LtQkvmC64xRxMBkHMGiDxZr8exztWNNT63Lz+jyGB/by/fviXX+zKgo5fv7xAR0bt7+rz0x8oB\n5EQh8OGp8Hj9Q/67LD2eInyvfRSoT9baf0JE/+Sj7MObN2+nbz5zz5u3M2gfyeM/qRljKBBSqdpk\nSFWHsEpdYH+loHBtmCnUeShEXWVFibzVKwzdZs8pzxiVFKYVF5lE65HCsD/9zvd4f12F8qurSrbV\nF3h7BWLlk4jPuw0h0s5dPrdhrITfzs6jfPuNuwwx79/aycdWbsg51hROhwVdHriYcQAMW7Uo+QBV\nnavGrMJ+I6RfEkDMWHafwHUHY4iqCCQGHpWs5ChYINos5C0Ecn8y2Gc64X0WgCRcunAh347lPP7k\nO2/lYxMJf620dBny8EDvRX/kYL0eJ44FMiOShW2H8EOIyc/KfH32qi7jrj1/kYiIZpb0fherurQ0\n8uiYUK87c6SaBYwNYcMw4s9xiVQs8VjZ6li9qc/lzAIv3+aWdA7qb/DY6FU99s1tfXZSyX2xcB4u\ndO3Cg0gGvp95j+/N2xm0U/f4LjQVSCZWBLkKLoFkPNYQRxxrAsS5Zfagzzx3Ix9bv87hsyKQZfhG\nnIg3Xjm/kY8tPWCv/O031QuNIPni4nn2BtWa7rPR47ftpQUlyB51JCSzq+G8wUg9191tDuV8920N\n6TxzKMk4TfUEBhIyGlXeDiFZpyBJTEWYrCoknZB4pwl4oSjh7SJEdzDhMZBImIFBlzgzlS02nVLH\nf4fZfkJyZZCIQql+f3FpjYiInr2uH3deeZv3A/epCCTvUK43TTVcl8hz8EEp5gF4/KeWmHD8zNOK\nBitNvqe4lyyDOcj4PCwkHFnD+zQGCT8IJTpkclzWIJCjEWT2VSPe5/lF9fh/To65B4lJj9qa6DWR\nrMUpglMS2GKS7MITpuB7j+/N2xk0/8P35u0M2qlCfWsziiXzKhUIFEJmXpAJqQNk2cUFjQ9/7jPP\nERHRuY2L+VipwpA3gAIVa6BYRRBb3Sqkevr6M0REtLOtEHx7V2Osh2WGdEstPbeK7KgKBNqSFN9s\ntxXeI9zuDXn8jfsao97dZpKqsnqUNCMiIqktqBcV/tclA61aAPIIioFcVRMWgcQC9TEjLor08yDk\nzwMolHG54giXMV5tHVSFWHmeBwBLhgyWCkbIwdW1c/nYSwKtH3T1Pr++qfMfyj7j4GjWIFZOYbGK\n2y7DXL5wkZcZzRldnqVybgmQbgaWJpEj9YDYtbJvPF4GS4HMaCmSfsntHOYSiFK3beCezTeYjPzh\nGxv52Ms3tY6jK7kv08U+jgh1xWoe6nvz5u0x5n/43rydQTtVqE9kKZUCg0igeViAd4/A/4WGsunP\nXbucb5+7wNCtXD1aWEIGYRaUnwoENVCeurTC6a4vvqDRge9861v59rDPkKpfUjhXEOgdhlrK6z4u\nFRR2YzmtK6DoA2vc6TPEi1OoAYc5mEg81kJMuFZkCFivQIktpo8KrkR9gVgKWMZQTBIGep6OwUdm\n3cj52qkAuW5ad5pYB+/mHRMCYEmSujwM+HhljVNkv/isps3+87fv5tv7PV4iGXs0aP841tqh8Dos\nHdfmm3Jo/U4mf5fBWArLIRPytpla7shnGcb2Yf7dn06FCiTaAfm1CWynsjzAUt/M8Pa5FV2aXF3V\ndOObsjRNpnQMeFtviYf63rx5e4ydqsfPMktjyR6rShFEA9Rrqhm/hy7MaYba6ooWUFTrLDgRFjEg\nLZ4AYr7TXoFfhQbetiXJDDy/vpaPTboai7+/+RC+yVYsM8qoQ/y8VJCstUjfn0ECXsGJUJQVwdAM\nX9sQdm6BXCoJkYdeqi6
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1daddb8f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9810... Discriminator Loss: 1.4782... Generator Loss: 0.6006\n",
      "Epoch 1/1-Step 9820... Discriminator Loss: 1.2930... Generator Loss: 0.8293\n",
      "Epoch 1/1-Step 9830... Discriminator Loss: 1.6044... Generator Loss: 0.6507\n",
      "Epoch 1/1-Step 9840... Discriminator Loss: 1.3829... Generator Loss: 0.7335\n",
      "Epoch 1/1-Step 9850... Discriminator Loss: 1.3934... Generator Loss: 0.6768\n",
      "Epoch 1/1-Step 9860... Discriminator Loss: 1.3801... Generator Loss: 0.7877\n",
      "Epoch 1/1-Step 9870... Discriminator Loss: 1.3987... Generator Loss: 0.7471\n",
      "Epoch 1/1-Step 9880... Discriminator Loss: 1.2617... Generator Loss: 1.0574\n",
      "Epoch 1/1-Step 9890... Discriminator Loss: 1.5471... Generator Loss: 0.8093\n",
      "Epoch 1/1-Step 9900... Discriminator Loss: 1.6048... Generator Loss: 0.9721\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad7f3400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 9910... Discriminator Loss: 1.3679... Generator Loss: 0.8096\n",
      "Epoch 1/1-Step 9920... Discriminator Loss: 1.4799... Generator Loss: 0.8523\n",
      "Epoch 1/1-Step 9930... Discriminator Loss: 1.5154... Generator Loss: 0.7592\n",
      "Epoch 1/1-Step 9940... Discriminator Loss: 1.2642... Generator Loss: 0.8838\n",
      "Epoch 1/1-Step 9950... Discriminator Loss: 1.4755... Generator Loss: 0.8207\n",
      "Epoch 1/1-Step 9960... Discriminator Loss: 1.3311... Generator Loss: 1.1212\n",
      "Epoch 1/1-Step 9970... Discriminator Loss: 1.2920... Generator Loss: 0.8989\n",
      "Epoch 1/1-Step 9980... Discriminator Loss: 1.4537... Generator Loss: 0.8655\n",
      "Epoch 1/1-Step 9990... Discriminator Loss: 1.5272... Generator Loss: 0.7564\n",
      "Epoch 1/1-Step 10000... Discriminator Loss: 1.4175... Generator Loss: 0.6625\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNRCYVlWpRoRov0Pvz5z
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dad3eaa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10010... Discriminator Loss: 1.4935... Generator Loss: 0.8439\n",
      "Epoch 1/1-Step 10020... Discriminator Loss: 1.2484... Generator Loss: 0.8695\n",
      "Epoch 1/1-Step 10030... Discriminator Loss: 1.3282... Generator Loss: 0.8563\n",
      "Epoch 1/1-Step 10040... Discriminator Loss: 1.2869... Generator Loss: 0.9696\n",
      "Epoch 1/1-Step 10050... Discriminator Loss: 1.3332... Generator Loss: 0.9056\n",
      "Epoch 1/1-Step 10060... Discriminator Loss: 1.3607... Generator Loss: 0.8038\n",
      "Epoch 1/1-Step 10070... Discriminator Loss: 1.2698... Generator Loss: 0.8784\n",
      "Epoch 1/1-Step 10080... Discriminator Loss: 1.5117... Generator Loss: 0.8056\n",
      "Epoch 1/1-Step 10090... Discriminator Loss: 1.4906... Generator Loss: 0.5707\n",
      "Epoch 1/1-Step 10100... Discriminator Loss: 1.2585... Generator Loss: 0.9396\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1db6a8dbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10110... Discriminator Loss: 1.4391... Generator Loss: 0.9607\n",
      "Epoch 1/1-Step 10120... Discriminator Loss: 1.4613... Generator Loss: 0.8471\n",
      "Epoch 1/1-Step 10130... Discriminator Loss: 1.4135... Generator Loss: 0.8948\n",
      "Epoch 1/1-Step 10140... Discriminator Loss: 1.3844... Generator Loss: 0.8816\n",
      "Epoch 1/1-Step 10150... Discriminator Loss: 1.3477... Generator Loss: 0.7196\n",
      "Epoch 1/1-Step 10160... Discriminator Loss: 1.2777... Generator Loss: 0.9514\n",
      "Epoch 1/1-Step 10170... Discriminator Loss: 1.3293... Generator Loss: 0.8516\n",
      "Epoch 1/1-Step 10180... Discriminator Loss: 1.4020... Generator Loss: 0.5783\n",
      "Epoch 1/1-Step 10190... Discriminator Loss: 1.2682... Generator Loss: 0.8802\n",
      "Epoch 1/1-Step 10200... Discriminator Loss: 1.4533... Generator Loss: 0.8726\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNIIssOqDXVlg8goj0HI
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacda5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10210... Discriminator Loss: 1.4546... Generator Loss: 1.1139\n",
      "Epoch 1/1-Step 10220... Discriminator Loss: 1.6298... Generator Loss: 0.7489\n",
      "Epoch 1/1-Step 10230... Discriminator Loss: 1.2910... Generator Loss: 0.8509\n",
      "Epoch 1/1-Step 10240... Discriminator Loss: 1.4758... Generator Loss: 0.6658\n",
      "Epoch 1/1-Step 10250... Discriminator Loss: 1.2535... Generator Loss: 0.8536\n",
      "Epoch 1/1-Step 10260... Discriminator Loss: 1.4520... Generator Loss: 0.8541\n",
      "Epoch 1/1-Step 10270... Discriminator Loss: 1.5810... Generator Loss: 0.7749\n",
      "Epoch 1/1-Step 10280... Discriminator Loss: 1.2750... Generator Loss: 0.9727\n",
      "Epoch 1/1-Step 10290... Discriminator Loss: 1.2823... Generator Loss: 0.9044\n",
      "Epoch 1/1-Step 10300... Discriminator Loss: 1.2948... Generator Loss: 0.8789\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac902390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10310... Discriminator Loss: 1.3948... Generator Loss: 0.7093\n",
      "Epoch 1/1-Step 10320... Discriminator Loss: 1.2347... Generator Loss: 0.6795\n",
      "Epoch 1/1-Step 10330... Discriminator Loss: 1.3978... Generator Loss: 0.8835\n",
      "Epoch 1/1-Step 10340... Discriminator Loss: 1.3607... Generator Loss: 0.7265\n",
      "Epoch 1/1-Step 10350... Discriminator Loss: 1.3404... Generator Loss: 0.7620\n",
      "Epoch 1/1-Step 10360... Discriminator Loss: 1.3495... Generator Loss: 0.9613\n",
      "Epoch 1/1-Step 10370... Discriminator Loss: 1.2985... Generator Loss: 0.8333\n",
      "Epoch 1/1-Step 10380... Discriminator Loss: 1.3280... Generator Loss: 0.9454\n",
      "Epoch 1/1-Step 10390... Discriminator Loss: 1.4848... Generator Loss: 0.8489\n",
      "Epoch 1/1-Step 10400... Discriminator Loss: 1.3480... Generator Loss: 1.1267\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n6VH0Iad7gMBEUmi1yTP
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac76d8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10410... Discriminator Loss: 1.2695... Generator Loss: 0.8037\n",
      "Epoch 1/1-Step 10420... Discriminator Loss: 1.2881... Generator Loss: 0.8146\n",
      "Epoch 1/1-Step 10430... Discriminator Loss: 1.4841... Generator Loss: 0.7443\n",
      "Epoch 1/1-Step 10440... Discriminator Loss: 1.1279... Generator Loss: 0.7864\n",
      "Epoch 1/1-Step 10450... Discriminator Loss: 1.4649... Generator Loss: 0.9297\n",
      "Epoch 1/1-Step 10460... Discriminator Loss: 1.3983... Generator Loss: 0.7493\n",
      "Epoch 1/1-Step 10470... Discriminator Loss: 1.3025... Generator Loss: 0.8726\n",
      "Epoch 1/1-Step 10480... Discriminator Loss: 1.3413... Generator Loss: 0.6361\n",
      "Epoch 1/1-Step 10490... Discriminator Loss: 1.4095... Generator Loss: 0.6189\n",
      "Epoch 1/1-Step 10500... Discriminator Loss: 1.3131... Generator Loss: 0.6618\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac17b8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10510... Discriminator Loss: 1.3210... Generator Loss: 0.8413\n",
      "Epoch 1/1-Step 10520... Discriminator Loss: 1.2047... Generator Loss: 0.5878\n",
      "Epoch 1/1-Step 10530... Discriminator Loss: 1.4400... Generator Loss: 1.0525\n",
      "Epoch 1/1-Step 10540... Discriminator Loss: 1.4856... Generator Loss: 0.7272\n",
      "Epoch 1/1-Step 10550... Discriminator Loss: 1.4373... Generator Loss: 0.6747\n",
      "Epoch 1/1-Step 10560... Discriminator Loss: 1.4687... Generator Loss: 0.7741\n",
      "Epoch 1/1-Step 10570... Discriminator Loss: 1.2590... Generator Loss: 0.7342\n",
      "Epoch 1/1-Step 10580... Discriminator Loss: 1.3178... Generator Loss: 0.8569\n",
      "Epoch 1/1-Step 10590... Discriminator Loss: 1.2052... Generator Loss: 0.7949\n",
      "Epoch 1/1-Step 10600... Discriminator Loss: 1.1968... Generator Loss: 0.9305\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na7cl2+z7PvNdREQ0TWQ
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da6b588d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10610... Discriminator Loss: 1.5676... Generator Loss: 0.6585\n",
      "Epoch 1/1-Step 10620... Discriminator Loss: 1.4860... Generator Loss: 0.9136\n",
      "Epoch 1/1-Step 10630... Discriminator Loss: 1.2400... Generator Loss: 1.0616\n",
      "Epoch 1/1-Step 10640... Discriminator Loss: 1.3501... Generator Loss: 0.6219\n",
      "Epoch 1/1-Step 10650... Discriminator Loss: 1.5369... Generator Loss: 0.8147\n",
      "Epoch 1/1-Step 10660... Discriminator Loss: 1.4280... Generator Loss: 0.6745\n",
      "Epoch 1/1-Step 10670... Discriminator Loss: 1.3291... Generator Loss: 0.8705\n",
      "Epoch 1/1-Step 10680... Discriminator Loss: 1.6188... Generator Loss: 0.8199\n",
      "Epoch 1/1-Step 10690... Discriminator Loss: 1.4133... Generator Loss: 0.6562\n",
      "Epoch 1/1-Step 10700... Discriminator Loss: 1.3344... Generator Loss: 0.7071\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da493fe80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10710... Discriminator Loss: 1.3191... Generator Loss: 0.9704\n",
      "Epoch 1/1-Step 10720... Discriminator Loss: 1.4205... Generator Loss: 0.7190\n",
      "Epoch 1/1-Step 10730... Discriminator Loss: 1.3806... Generator Loss: 0.8748\n",
      "Epoch 1/1-Step 10740... Discriminator Loss: 1.2956... Generator Loss: 0.8417\n",
      "Epoch 1/1-Step 10750... Discriminator Loss: 1.2877... Generator Loss: 0.9098\n",
      "Epoch 1/1-Step 10760... Discriminator Loss: 1.4572... Generator Loss: 0.7460\n",
      "Epoch 1/1-Step 10770... Discriminator Loss: 1.3599... Generator Loss: 0.7880\n",
      "Epoch 1/1-Step 10780... Discriminator Loss: 1.4582... Generator Loss: 0.8497\n",
      "Epoch 1/1-Step 10790... Discriminator Loss: 1.5790... Generator Loss: 0.5692\n",
      "Epoch 1/1-Step 10800... Discriminator Loss: 1.2783... Generator Loss: 0.7312\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\na1QCkkgv/JzbILUT/cI
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dacdbd5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10810... Discriminator Loss: 1.4244... Generator Loss: 0.7810\n",
      "Epoch 1/1-Step 10820... Discriminator Loss: 1.3106... Generator Loss: 1.0429\n",
      "Epoch 1/1-Step 10830... Discriminator Loss: 1.3927... Generator Loss: 0.8113\n",
      "Epoch 1/1-Step 10840... Discriminator Loss: 1.3475... Generator Loss: 0.7938\n",
      "Epoch 1/1-Step 10850... Discriminator Loss: 1.4264... Generator Loss: 0.6428\n",
      "Epoch 1/1-Step 10860... Discriminator Loss: 1.5916... Generator Loss: 0.4819\n",
      "Epoch 1/1-Step 10870... Discriminator Loss: 1.3360... Generator Loss: 0.7638\n",
      "Epoch 1/1-Step 10880... Discriminator Loss: 1.4117... Generator Loss: 1.1296\n",
      "Epoch 1/1-Step 10890... Discriminator Loss: 1.3386... Generator Loss: 0.8863\n",
      "Epoch 1/1-Step 10900... Discriminator Loss: 1.2954... Generator Loss: 0.6318\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSul5j+/N2zk0/8P35u0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da472b630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 10910... Discriminator Loss: 1.3890... Generator Loss: 0.6359\n",
      "Epoch 1/1-Step 10920... Discriminator Loss: 1.3991... Generator Loss: 0.7678\n",
      "Epoch 1/1-Step 10930... Discriminator Loss: 1.2430... Generator Loss: 1.0111\n",
      "Epoch 1/1-Step 10940... Discriminator Loss: 1.4026... Generator Loss: 0.8312\n",
      "Epoch 1/1-Step 10950... Discriminator Loss: 1.4150... Generator Loss: 0.7696\n",
      "Epoch 1/1-Step 10960... Discriminator Loss: 1.4728... Generator Loss: 0.5662\n",
      "Epoch 1/1-Step 10970... Discriminator Loss: 1.4131... Generator Loss: 0.8603\n",
      "Epoch 1/1-Step 10980... Discriminator Loss: 1.3735... Generator Loss: 0.7989\n",
      "Epoch 1/1-Step 10990... Discriminator Loss: 1.4215... Generator Loss: 0.7640\n",
      "Epoch 1/1-Step 11000... Discriminator Loss: 1.3377... Generator Loss: 0.8408\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLKXJmIvgIAqWgkK5pw0
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1dac5296d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11010... Discriminator Loss: 1.4131... Generator Loss: 0.8705\n",
      "Epoch 1/1-Step 11020... Discriminator Loss: 1.6240... Generator Loss: 0.8889\n",
      "Epoch 1/1-Step 11030... Discriminator Loss: 1.2515... Generator Loss: 0.8264\n",
      "Epoch 1/1-Step 11040... Discriminator Loss: 1.3354... Generator Loss: 0.6601\n",
      "Epoch 1/1-Step 11050... Discriminator Loss: 1.3464... Generator Loss: 0.8474\n",
      "Epoch 1/1-Step 11060... Discriminator Loss: 1.2523... Generator Loss: 0.9518\n",
      "Epoch 1/1-Step 11070... Discriminator Loss: 1.2608... Generator Loss: 0.6950\n",
      "Epoch 1/1-Step 11080... Discriminator Loss: 1.2128... Generator Loss: 0.8864\n",
      "Epoch 1/1-Step 11090... Discriminator Loss: 1.3931... Generator Loss: 0.9828\n",
      "Epoch 1/1-Step 11100... Discriminator Loss: 1.3821... Generator Loss: 0.8715\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da465ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11110... Discriminator Loss: 1.4582... Generator Loss: 1.0099\n",
      "Epoch 1/1-Step 11120... Discriminator Loss: 1.4083... Generator Loss: 0.7510\n",
      "Epoch 1/1-Step 11130... Discriminator Loss: 1.4243... Generator Loss: 0.9778\n",
      "Epoch 1/1-Step 11140... Discriminator Loss: 1.3653... Generator Loss: 0.8567\n",
      "Epoch 1/1-Step 11150... Discriminator Loss: 1.2807... Generator Loss: 0.9424\n",
      "Epoch 1/1-Step 11160... Discriminator Loss: 1.5374... Generator Loss: 0.9249\n",
      "Epoch 1/1-Step 11170... Discriminator Loss: 1.2315... Generator Loss: 0.7423\n",
      "Epoch 1/1-Step 11180... Discriminator Loss: 1.5830... Generator Loss: 0.7148\n",
      "Epoch 1/1-Step 11190... Discriminator Loss: 1.3675... Generator Loss: 0.9009\n",
      "Epoch 1/1-Step 11200... Discriminator Loss: 1.3814... Generator Loss: 0.6984\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvtYrPM6VBfXoJSHqyhH
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da447cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11210... Discriminator Loss: 1.3557... Generator Loss: 0.8923\n",
      "Epoch 1/1-Step 11220... Discriminator Loss: 1.5899... Generator Loss: 0.7487\n",
      "Epoch 1/1-Step 11230... Discriminator Loss: 1.2507... Generator Loss: 0.9168\n",
      "Epoch 1/1-Step 11240... Discriminator Loss: 1.3095... Generator Loss: 0.8921\n",
      "Epoch 1/1-Step 11250... Discriminator Loss: 1.5450... Generator Loss: 0.6646\n",
      "Epoch 1/1-Step 11260... Discriminator Loss: 1.4563... Generator Loss: 0.9997\n",
      "Epoch 1/1-Step 11270... Discriminator Loss: 1.3845... Generator Loss: 0.8956\n",
      "Epoch 1/1-Step 11280... Discriminator Loss: 1.4630... Generator Loss: 0.8149\n",
      "Epoch 1/1-Step 11290... Discriminator Loss: 1.2505... Generator Loss: 0.7907\n",
      "Epoch 1/1-Step 11300... Discriminator Loss: 1.3488... Generator Loss: 1.0223\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da48fb2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11310... Discriminator Loss: 1.4269... Generator Loss: 0.7955\n",
      "Epoch 1/1-Step 11320... Discriminator Loss: 1.5527... Generator Loss: 0.6192\n",
      "Epoch 1/1-Step 11330... Discriminator Loss: 1.4390... Generator Loss: 0.9324\n",
      "Epoch 1/1-Step 11340... Discriminator Loss: 1.4394... Generator Loss: 0.6186\n",
      "Epoch 1/1-Step 11350... Discriminator Loss: 1.2852... Generator Loss: 0.7538\n",
      "Epoch 1/1-Step 11360... Discriminator Loss: 1.2607... Generator Loss: 1.0253\n",
      "Epoch 1/1-Step 11370... Discriminator Loss: 1.3005... Generator Loss: 0.8196\n",
      "Epoch 1/1-Step 11380... Discriminator Loss: 1.5029... Generator Loss: 0.8377\n",
      "Epoch 1/1-Step 11390... Discriminator Loss: 1.4446... Generator Loss: 0.8927\n",
      "Epoch 1/1-Step 11400... Discriminator Loss: 1.2528... Generator Loss: 0.7855\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEGuWGX/gcbx23uaBRa
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da46bd198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11410... Discriminator Loss: 1.5148... Generator Loss: 0.8904\n",
      "Epoch 1/1-Step 11420... Discriminator Loss: 1.3747... Generator Loss: 0.7251\n",
      "Epoch 1/1-Step 11430... Discriminator Loss: 1.2497... Generator Loss: 1.0771\n",
      "Epoch 1/1-Step 11440... Discriminator Loss: 1.5128... Generator Loss: 0.7344\n",
      "Epoch 1/1-Step 11450... Discriminator Loss: 1.2396... Generator Loss: 0.7375\n",
      "Epoch 1/1-Step 11460... Discriminator Loss: 1.4684... Generator Loss: 0.9293\n",
      "Epoch 1/1-Step 11470... Discriminator Loss: 1.4544... Generator Loss: 0.8491\n",
      "Epoch 1/1-Step 11480... Discriminator Loss: 1.4127... Generator Loss: 0.7527\n",
      "Epoch 1/1-Step 11490... Discriminator Loss: 1.2788... Generator Loss: 1.1250\n",
      "Epoch 1/1-Step 11500... Discriminator Loss: 1.3754... Generator Loss: 0.7059\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da40c3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11510... Discriminator Loss: 1.2868... Generator Loss: 0.8759\n",
      "Epoch 1/1-Step 11520... Discriminator Loss: 1.5955... Generator Loss: 0.7895\n",
      "Epoch 1/1-Step 11530... Discriminator Loss: 1.4522... Generator Loss: 0.9598\n",
      "Epoch 1/1-Step 11540... Discriminator Loss: 1.1838... Generator Loss: 0.8858\n",
      "Epoch 1/1-Step 11550... Discriminator Loss: 1.2494... Generator Loss: 0.9102\n",
      "Epoch 1/1-Step 11560... Discriminator Loss: 1.3684... Generator Loss: 1.0006\n",
      "Epoch 1/1-Step 11570... Discriminator Loss: 1.2447... Generator Loss: 0.8008\n",
      "Epoch 1/1-Step 11580... Discriminator Loss: 1.6734... Generator Loss: 0.6938\n",
      "Epoch 1/1-Step 11590... Discriminator Loss: 1.2360... Generator Loss: 0.7650\n",
      "Epoch 1/1-Step 11600... Discriminator Loss: 1.2692... Generator Loss: 0.8925\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIrlCj+4zkey8bKbYh8c
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da43dd940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11610... Discriminator Loss: 1.3321... Generator Loss: 0.7206\n",
      "Epoch 1/1-Step 11620... Discriminator Loss: 1.3030... Generator Loss: 0.9384\n",
      "Epoch 1/1-Step 11630... Discriminator Loss: 1.4545... Generator Loss: 0.7635\n",
      "Epoch 1/1-Step 11640... Discriminator Loss: 1.3959... Generator Loss: 0.9490\n",
      "Epoch 1/1-Step 11650... Discriminator Loss: 1.4048... Generator Loss: 0.7957\n",
      "Epoch 1/1-Step 11660... Discriminator Loss: 1.5312... Generator Loss: 0.8739\n",
      "Epoch 1/1-Step 11670... Discriminator Loss: 1.4612... Generator Loss: 0.6763\n",
      "Epoch 1/1-Step 11680... Discriminator Loss: 1.1386... Generator Loss: 0.8367\n",
      "Epoch 1/1-Step 11690... Discriminator Loss: 1.6632... Generator Loss: 0.7885\n",
      "Epoch 1/1-Step 11700... Discriminator Loss: 1.2312... Generator Loss: 0.8279\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da3d307f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11710... Discriminator Loss: 1.4111... Generator Loss: 0.8266\n",
      "Epoch 1/1-Step 11720... Discriminator Loss: 1.3457... Generator Loss: 0.8164\n",
      "Epoch 1/1-Step 11730... Discriminator Loss: 1.3250... Generator Loss: 0.7632\n",
      "Epoch 1/1-Step 11740... Discriminator Loss: 1.4381... Generator Loss: 0.8089\n",
      "Epoch 1/1-Step 11750... Discriminator Loss: 1.4645... Generator Loss: 0.8010\n",
      "Epoch 1/1-Step 11760... Discriminator Loss: 1.3394... Generator Loss: 0.8888\n",
      "Epoch 1/1-Step 11770... Discriminator Loss: 1.2586... Generator Loss: 0.8806\n",
      "Epoch 1/1-Step 11780... Discriminator Loss: 1.2871... Generator Loss: 0.8592\n",
      "Epoch 1/1-Step 11790... Discriminator Loss: 1.4190... Generator Loss: 0.7568\n",
      "Epoch 1/1-Step 11800... Discriminator Loss: 1.3011... Generator Loss: 0.8448\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nqCYVoA5kXT5IhWC8x/f
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da3bfc4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11810... Discriminator Loss: 1.5680... Generator Loss: 0.8663\n",
      "Epoch 1/1-Step 11820... Discriminator Loss: 1.4439... Generator Loss: 0.7407\n",
      "Epoch 1/1-Step 11830... Discriminator Loss: 1.2312... Generator Loss: 0.9643\n",
      "Epoch 1/1-Step 11840... Discriminator Loss: 1.5059... Generator Loss: 0.8405\n",
      "Epoch 1/1-Step 11850... Discriminator Loss: 1.2742... Generator Loss: 0.7685\n",
      "Epoch 1/1-Step 11860... Discriminator Loss: 1.3000... Generator Loss: 0.8343\n",
      "Epoch 1/1-Step 11870... Discriminator Loss: 1.5035... Generator Loss: 0.8467\n",
      "Epoch 1/1-Step 11880... Discriminator Loss: 1.3962... Generator Loss: 0.8547\n",
      "Epoch 1/1-Step 11890... Discriminator Loss: 1.3165... Generator Loss: 0.8841\n",
      "Epoch 1/1-Step 11900... Discriminator Loss: 1.2998... Generator Loss: 0.7774\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da4524198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 11910... Discriminator Loss: 1.3885... Generator Loss: 0.7595\n",
      "Epoch 1/1-Step 11920... Discriminator Loss: 1.3611... Generator Loss: 0.8702\n",
      "Epoch 1/1-Step 11930... Discriminator Loss: 1.2886... Generator Loss: 0.7828\n",
      "Epoch 1/1-Step 11940... Discriminator Loss: 1.2814... Generator Loss: 0.8035\n",
      "Epoch 1/1-Step 11950... Discriminator Loss: 1.3590... Generator Loss: 0.8102\n",
      "Epoch 1/1-Step 11960... Discriminator Loss: 1.3776... Generator Loss: 0.6348\n",
      "Epoch 1/1-Step 11970... Discriminator Loss: 1.2823... Generator Loss: 0.9209\n",
      "Epoch 1/1-Step 11980... Discriminator Loss: 1.5918... Generator Loss: 0.7585\n",
      "Epoch 1/1-Step 11990... Discriminator Loss: 1.2999... Generator Loss: 0.9440\n",
      "Epoch 1/1-Step 12000... Discriminator Loss: 1.2886... Generator Loss: 0.9465\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nbil4u1miHsmIxy9Ctlk
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da3d80160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12010... Discriminator Loss: 1.3893... Generator Loss: 1.0843\n",
      "Epoch 1/1-Step 12020... Discriminator Loss: 1.3826... Generator Loss: 0.8926\n",
      "Epoch 1/1-Step 12030... Discriminator Loss: 1.5257... Generator Loss: 0.7666\n",
      "Epoch 1/1-Step 12040... Discriminator Loss: 1.3441... Generator Loss: 0.8539\n",
      "Epoch 1/1-Step 12050... Discriminator Loss: 1.3652... Generator Loss: 0.9032\n",
      "Epoch 1/1-Step 12060... Discriminator Loss: 1.4243... Generator Loss: 1.0420\n",
      "Epoch 1/1-Step 12070... Discriminator Loss: 1.1094... Generator Loss: 1.1120\n",
      "Epoch 1/1-Step 12080... Discriminator Loss: 1.3395... Generator Loss: 0.9102\n",
      "Epoch 1/1-Step 12090... Discriminator Loss: 1.3288... Generator Loss: 0.8957\n",
      "Epoch 1/1-Step 12100... Discriminator Loss: 1.3541... Generator Loss: 0.7103\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da3ec7f28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12110... Discriminator Loss: 1.3892... Generator Loss: 0.8427\n",
      "Epoch 1/1-Step 12120... Discriminator Loss: 1.3067... Generator Loss: 0.9743\n",
      "Epoch 1/1-Step 12130... Discriminator Loss: 1.2540... Generator Loss: 0.8069\n",
      "Epoch 1/1-Step 12140... Discriminator Loss: 1.1483... Generator Loss: 0.9029\n",
      "Epoch 1/1-Step 12150... Discriminator Loss: 1.4093... Generator Loss: 0.9016\n",
      "Epoch 1/1-Step 12160... Discriminator Loss: 1.4080... Generator Loss: 0.7713\n",
      "Epoch 1/1-Step 12170... Discriminator Loss: 1.5690... Generator Loss: 0.7068\n",
      "Epoch 1/1-Step 12180... Discriminator Loss: 1.3836... Generator Loss: 0.7119\n",
      "Epoch 1/1-Step 12190... Discriminator Loss: 1.4461... Generator Loss: 0.5718\n",
      "Epoch 1/1-Step 12200... Discriminator Loss: 1.3887... Generator Loss: 1.0066\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nc9KXHtUlgQhSTjsDfgN
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da39d64a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12210... Discriminator Loss: 1.4476... Generator Loss: 1.0213\n",
      "Epoch 1/1-Step 12220... Discriminator Loss: 1.1862... Generator Loss: 0.8651\n",
      "Epoch 1/1-Step 12230... Discriminator Loss: 1.4644... Generator Loss: 0.7877\n",
      "Epoch 1/1-Step 12240... Discriminator Loss: 1.3565... Generator Loss: 0.9463\n",
      "Epoch 1/1-Step 12250... Discriminator Loss: 1.2269... Generator Loss: 0.8404\n",
      "Epoch 1/1-Step 12260... Discriminator Loss: 1.2739... Generator Loss: 0.8159\n",
      "Epoch 1/1-Step 12270... Discriminator Loss: 1.4365... Generator Loss: 0.6559\n",
      "Epoch 1/1-Step 12280... Discriminator Loss: 1.4702... Generator Loss: 1.1371\n",
      "Epoch 1/1-Step 12290... Discriminator Loss: 1.3055... Generator Loss: 1.1993\n",
      "Epoch 1/1-Step 12300... Discriminator Loss: 1.2517... Generator Loss: 0.7576\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da415fb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12310... Discriminator Loss: 1.6214... Generator Loss: 0.8081\n",
      "Epoch 1/1-Step 12320... Discriminator Loss: 1.2923... Generator Loss: 1.3314\n",
      "Epoch 1/1-Step 12330... Discriminator Loss: 1.1905... Generator Loss: 0.8463\n",
      "Epoch 1/1-Step 12340... Discriminator Loss: 1.6069... Generator Loss: 0.6199\n",
      "Epoch 1/1-Step 12350... Discriminator Loss: 1.4185... Generator Loss: 0.7690\n",
      "Epoch 1/1-Step 12360... Discriminator Loss: 1.2614... Generator Loss: 0.6309\n",
      "Epoch 1/1-Step 12370... Discriminator Loss: 1.2238... Generator Loss: 0.7949\n",
      "Epoch 1/1-Step 12380... Discriminator Loss: 1.3518... Generator Loss: 0.9461\n",
      "Epoch 1/1-Step 12390... Discriminator Loss: 1.3218... Generator Loss: 0.7197\n",
      "Epoch 1/1-Step 12400... Discriminator Loss: 1.3029... Generator Loss: 0.8115\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nawlofO7SOZ8bci9CmLe
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da344b908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12410... Discriminator Loss: 1.3318... Generator Loss: 0.6683\n",
      "Epoch 1/1-Step 12420... Discriminator Loss: 1.4617... Generator Loss: 0.9376\n",
      "Epoch 1/1-Step 12430... Discriminator Loss: 1.4149... Generator Loss: 0.7557\n",
      "Epoch 1/1-Step 12440... Discriminator Loss: 1.3193... Generator Loss: 0.6324\n",
      "Epoch 1/1-Step 12450... Discriminator Loss: 1.2309... Generator Loss: 1.0568\n",
      "Epoch 1/1-Step 12460... Discriminator Loss: 1.4777... Generator Loss: 0.8648\n",
      "Epoch 1/1-Step 12470... Discriminator Loss: 1.3108... Generator Loss: 1.0013\n",
      "Epoch 1/1-Step 12480... Discriminator Loss: 1.2289... Generator Loss: 0.7481\n",
      "Epoch 1/1-Step 12490... Discriminator Loss: 1.5573... Generator Loss: 0.8947\n",
      "Epoch 1/1-Step 12500... Discriminator Loss: 1.5264... Generator Loss: 0.9437\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da38b0860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12510... Discriminator Loss: 1.2938... Generator Loss: 0.7327\n",
      "Epoch 1/1-Step 12520... Discriminator Loss: 1.4593... Generator Loss: 0.9380\n",
      "Epoch 1/1-Step 12530... Discriminator Loss: 1.4106... Generator Loss: 0.7292\n",
      "Epoch 1/1-Step 12540... Discriminator Loss: 1.3427... Generator Loss: 0.8795\n",
      "Epoch 1/1-Step 12550... Discriminator Loss: 1.2490... Generator Loss: 0.7984\n",
      "Epoch 1/1-Step 12560... Discriminator Loss: 1.3427... Generator Loss: 0.7317\n",
      "Epoch 1/1-Step 12570... Discriminator Loss: 1.5050... Generator Loss: 0.9468\n",
      "Epoch 1/1-Step 12580... Discriminator Loss: 1.2578... Generator Loss: 0.9158\n",
      "Epoch 1/1-Step 12590... Discriminator Loss: 1.2107... Generator Loss: 0.7373\n",
      "Epoch 1/1-Step 12600... Discriminator Loss: 1.2843... Generator Loss: 0.8304\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAP4AAAD8CAYAAABXXhlaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\novRjoO8uUalS0tluuq/
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f1da3187668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1-Step 12610... Discriminator Loss: 1.5333... Generator Loss: 0.8049\n",
      "Epoch 1/1-Step 12620... Discriminator Loss: 1.6096... Generator Loss: 0.7886\n",
      "Epoch 1/1-Step 12630... Discriminator Loss: 1.4343... Generator Loss: 0.9134\n",
      "Epoch 1/1-Step 12640... Discriminator Loss: 1.6045... Generator Loss: 0.5781\n",
      "Epoch 1/1-Step 12650... Discriminator Loss: 1.3478... Generator Loss: 0.8706\n",
      "Epoch 1/1-Step 12660... Discriminator Loss: 1.5313... Generator Loss: 0.9332\n"
     ]
    }
   ],
   "source": [
    "batch_size = 16\n",
    "z_dim = 100\n",
    "learning_rate = 0.0003\n",
    "beta1 = 0.5\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE\n",
    "\"\"\"\n",
    "epochs = 1\n",
    "\n",
    "celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))\n",
    "with tf.Graph().as_default():\n",
    "    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,\n",
    "          celeba_dataset.shape, celeba_dataset.image_mode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Submitting This Project\n",
    "When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as \"dlnd_face_generation.ipynb\" and save it as a HTML file under \"File\" -> \"Download as\". Include the \"helper.py\" and \"problem_unittests.py\" files in your submission."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}