CP8_VAEs&GANs_MNIST_datset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Checkpoint 8: Variational Autoencoder & Generative and adversarial models\n",
    "---\n",
    "*Responsible:* Guillermo Hamity (<ghamity@ed.ac.uk>)\n",
    "\n",
    "\n",
    "Quickdraw was an interesting dataset but requires quite a lot of CPU to get decent results. We discussed a relatively good solution to the problem in the Lecture, but it is quite CPU intensive.\n",
    "\n",
    "In order to make life a little easier in terms of training time, in this checkpoint exercise we will work with the MNIST dataset of hand-written digits. This time, though, we won't be classifying existing images, we will be generating new ones with generative models. \n",
    "\n",
    "- Exersice 1 and 2 we will build and use a Variational Autoencoder\n",
    "- Exersice 3 we will use a Generative Adeverserial Network\n",
    "\n",
    "\n",
    "These exercises build quite heavily on the ones on `AEs`. The network structure that worked on quickdraw should translate pretty well to MNIST, and hopefully perform better. You may be able to reuse some of your work, or you can look at my proposed solutions from last time for inspiration.\n",
    "\n",
    "\n",
    "### Notes on assessment\n",
    "* Try and calculate the answers to the exercises provided. If you are unable to complete the question, describe which approach you _would_ have taken to solve the problem\n",
    "* Code must be understandable and reproducible. Before grading the notebook kernel **may** be restarted and re-run, so make sure that your code can run from start to finish without any (unintentional) errors\n",
    "* If you are unsure on how to proceed please **ask one of the TAs** during the workshop\n",
    "- Notebooks should be submitted by **10AM Friday** \n",
    "- This CP exercise sheet is divided into **3 sections** giving a maximum of **10 marks** in total:\n",
    "\n",
    "| <p align='left'> Title                        | <p align='left'> Exercise nos. | <p align='center'> Number of marks |\n",
    "| ------------------------------------------------------ | --------------------- | ------------------ |\n",
    "| <p align='left'> 1. Build VAE                          | <p align='left'> 1–3  | <p align='left'> 4 |\n",
    "| <p align='left'> 2. Compile and train a VAE            | <p align='left'> 4–6  | <p align='left'> 4 | \n",
    "| <p align='left'> 3. Generative adversarial network     | <p align='left'> 7–9  | <p align='left'> 2 |\n",
    "| <p align='left'> **Total**                             |                       | <p align='left'> **10** |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preamble\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-19 21:52:57.219065: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Standard import(s)\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Keras import(s)\n",
    "from tensorflow.keras.utils import plot_model\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.layers import Input, Dense, Dropout, Flatten, Reshape, Conv2D, Conv2DTranspose, Concatenate, Lambda,BatchNormalization, MaxPooling2D, UpSampling2D\n",
    "from tensorflow.keras import backend as K\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from tensorflow.keras.backend import set_image_data_format\n",
    "from keras.optimizers import Adam\n",
    "from tensorflow.python.framework.ops import disable_eager_execution\n",
    "disable_eager_execution()\n",
    "set_image_data_format('channels_last')  # Images are formatted as (N, N, 1), with colour-channels last\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "---\n",
    "\n",
    "First, we'll load in the MNIST dataset from TensorFlow, which has already been split into training and testing parts for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training examples: 60000\n",
      "Number of testing  examples: 10000\n",
      "Number of target classes:    10\n",
      "Max. input before scaling: 255\n",
      "Image shape:  (28, 28, 1)\n"
     ]
    }
   ],
   "source": [
    "# Load in MNIST dataset \n",
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()\n",
    "\n",
    "# Get numbers\n",
    "nb_train   = X_train.shape[0]\n",
    "nb_test    = X_test .shape[0]\n",
    "nb_classes = y_train.max() + 1\n",
    "print(f\"Number of training examples: {nb_train}\")\n",
    "print(f\"Number of testing  examples: {nb_test}\")\n",
    "print(f\"Number of target classes:    {nb_classes}\")\n",
    "\n",
    "# Reshape inputs to (N,N,1), i.e. have one colour dimension\n",
    "X_train = X_train[..., np.newaxis]\n",
    "X_test  = X_test [..., np.newaxis]\n",
    "\n",
    "# Standardise inputs\n",
    "print(\"Max. input before scaling: {}\".format(X_train.max()))\n",
    "X_train = X_train / 255.\n",
    "X_test  = X_test  / 255.\n",
    "\n",
    "# Get image shape\n",
    "shape = X_train.shape[1:]\n",
    "print(f\"Image shape:  {shape}\")\n",
    "\n",
    "# Shuffle (dataset is pre-shuffled, but still)\n",
    "ix_train = np.random.permutation(nb_train)\n",
    "ix_test  = np.random.permutation(nb_test)\n",
    "\n",
    "X_train = X_train[ix_train]\n",
    "y_train = y_train[ix_train]\n",
    "\n",
    "X_test  = X_test [ix_test]\n",
    "y_test  = y_test [ix_test]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The image below shows labels and images associated to them. They are in our standard (28,28,1) image format, as we had in quickdraw."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "label marks [6 9 0 1 7 3 2 2 1 8 8 4 6 6 4 1 1 7 9 5 5 8 8 4 5]\n"
     ]
    }
   ],
   "source": [
    "nb_show = 25\n",
    "plt.figure(figsize=(10,10))\n",
    "for i in range(nb_show):\n",
    "    plt.subplot(5,5,i+1)\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "    plt.grid(False)\n",
    "    plt.imshow(X_train[i], cmap=plt.cm.binary)\n",
    "    plt.xlabel(y_train[i])\n",
    "plt.show()\n",
    "print(\"label marks\",y_train[:nb_show])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Variational auto-encoder (VAE) utilities\n",
    "---\n",
    "Below, I am providing you with some utility methods for training VAEs. This is done to minimsie the overhead in getting you up and running and training cool neural networks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first function implements the crucial element for _variational_ auto-encoders: The random sampling in the latent space. The `sampling` function below takes one argument, which is assumed to be a list with two element: the means and the logarithm of the variances, respectively, of the latent probability density function (p.d.f.) for a given image example. The function then samples random numbers, and scales them such that they are effectively drawn from a p.d.f. with means and standard deviations as given by `args`. These sampled coordinates in the latent space are then returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sampling (args):\n",
    "\n",
    "  # Unpack arguments\n",
    "  z_mean, z_log_var = args\n",
    "\n",
    "  # Get shape of random noise to sample\n",
    "  epsilon = K.random_normal(shape=K.shape(z_mean))\n",
    "\n",
    "  # Return samples from latent space p.d.f.\n",
    "  return z_mean + K.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build a VAE (4 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Build the encoder of the VAE (1.5 mark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build an encoder. We will train for 50 epoches so a relatively fast NN may be desiriable. Here you have some freedom but some example structure is given:\n",
    "- the input layer has the dimension of the shape\n",
    "- Flat the input layer (either with or without convolution beforehand)\n",
    "    - The `1 2 4 8 16` convolution example worked well for me, but is a bit slower.\n",
    "- add a few hidden Dense layer (with relu activation function) with 256 dimension of hidden nodes\n",
    "- Add the 2 special layers:\n",
    "    * `z_mean` contains the means of the latent space p.d.f. for a given image, and\n",
    "    * `z_log_var` contains the logarithm of the assocated variances. \n",
    "- Pass these 2 layers into a layer `z` that just apply the `sampling` method above. Note: the sampling method is used to perform the reparametrization trick. `Lambda` has to be used to ensure the gradients to be computed (see Lecture example) \n",
    "- Output the 3 layers (z_mean, z_log_var and z)\n",
    "- build the encoder model with the `Model` method. \n",
    "    - If you used convolution and pooling make sure you complient this with Upsampleling and ConvolutionTranspose. \n",
    "    - A good structure here is to invert the stucture of the encoder, e.g. `16 8 4 2 1`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "intermediate_dim =256\n",
    "latent_dim = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"encoder\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input (InputLayer)             [(None, 28, 28, 1)]  0           []                               \n",
      "                                                                                                  \n",
      " conv2d (Conv2D)                (None, 28, 28, 16)   160         ['input[0][0]']                  \n",
      "                                                                                                  \n",
      " max_pooling2d (MaxPooling2D)   (None, 14, 14, 16)   0           ['conv2d[0][0]']                 \n",
      "                                                                                                  \n",
      " conv2d_1 (Conv2D)              (None, 14, 14, 8)    1160        ['max_pooling2d[0][0]']          \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 1568)         0           ['conv2d_1[0][0]']               \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 256)          401664      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 256)          65792       ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " z_mean (Dense)                 (None, 2)            514         ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " z_log_var (Dense)              (None, 2)            514         ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " z (Lambda)                     (None, 2)            0           ['z_mean[0][0]',                 \n",
      "                                                                  'z_log_var[0][0]']              \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 469,804\n",
      "Trainable params: 469,804\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "encoder_inputs = Input(shape=(28,28,1), name=\"input\")\n",
    "x1 = Conv2D(16, kernel_size=(3,3), padding=\"same\", activation=\"relu\")(encoder_inputs)\n",
    "x2 = MaxPooling2D(pool_size=(2,2))(x1)\n",
    "x3 = Conv2D( 8, kernel_size=(3,3), padding=\"same\", activation=\"relu\")(x2)\n",
    "e = Flatten()(x3)\n",
    "x = Dense(intermediate_dim, activation=\"relu\")(e)\n",
    "x = Dense(intermediate_dim, activation=\"relu\")(x)\n",
    "z_mean = Dense(latent_dim, name=\"z_mean\")(x)\n",
    "z_log_var = Dense(latent_dim, name=\"z_log_var\")(x)\n",
    "z = Lambda(sampling, output_shape=(latent_dim, ), name=\"z\")([z_mean,z_log_var])\n",
    "outputs = [z_mean, z_log_var, z]\n",
    "encoder = Model(encoder_inputs, outputs, name=\"encoder\")\n",
    "encoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nice model "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Build the decoder of the VAE (1.5 mark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Build a decoder:\n",
    "- the input layer has the latent space 2\n",
    "- Apply 2 hidden Dense layers with dimension 256 (same number as in the encoder) and relu activation function\n",
    "- Output sigmoid activation function such that it ends up with the same shape as the original image, with pixel intensities in the range $[0, 1]$.\n",
    "    - use as dimension of this layer `np.prod(shape)`, where shape is the latent space.\n",
    "- then reshape the output to the original image shape with `Reshape` method. This step is needed to bring back the data from shape (latent_dim, ) into some shape that can be reshaped into the output shape of the last layer of our encoder.\n",
    "- build the decoder model with the `Model` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"decoder\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_1 (InputLayer)        [(None, 2)]               0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 256)               768       \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 256)               65792     \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 1568)              402976    \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 14, 14, 8)         0         \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTra  (None, 14, 14, 16)       1168      \n",
      " nspose)                                                         \n",
      "                                                                 \n",
      " up_sampling2d (UpSampling2D  (None, 28, 28, 16)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2DT  (None, 28, 28, 8)        1160      \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " conv2d_transpose_2 (Conv2DT  (None, 28, 28, 1)        73        \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 471,937\n",
      "Trainable params: 471,937\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "latent_inputs = Input(shape=(latent_dim,))\n",
    "x = Dense(intermediate_dim, activation=\"relu\")(latent_inputs)\n",
    "x = Dense(intermediate_dim, activation=\"relu\")(x)\n",
    "x = Dense(14*14*8, activation=\"relu\")(x)\n",
    "x = Reshape((14, 14, 8))(x)\n",
    "x = Conv2DTranspose(16, kernel_size=(3,3), padding=\"same\", activation=\"relu\")(x)\n",
    "x = UpSampling2D(size=(2,2))(x)\n",
    "x = Conv2DTranspose(8, kernel_size=(3,3), padding=\"same\", activation=\"relu\")(x)\n",
    "outputs = Conv2DTranspose(1, kernel_size=(3,3), padding=\"same\", activation=\"sigmoid\")(x)\n",
    "decoder = Model(latent_inputs, outputs, name=\"decoder\")\n",
    "decoder.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Looks good "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. Build the VAE model (1 mark)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can connect the encoder and the decoder. \n",
    "- Build the VAE model using `Model` method using `i` (encoder) and `o` (decoder)\n",
    "- plot the model with the `plot_model` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Chain together to get VAE\n",
    "i = encoder.inputs\n",
    "if len(i) == 1:\n",
    "    i = i[0]\n",
    "    pass\n",
    "z = encoder(i)[2]\n",
    "o = decoder(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vae = Model(i, o,name=\"VAE\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"VAE\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input (InputLayer)          [(None, 28, 28, 1)]       0         \n",
      "                                                                 \n",
      " encoder (Functional)        [(None, 2),               469804    \n",
      "                              (None, 2),                         \n",
      "                              (None, 2)]                         \n",
      "                                                                 \n",
      " decoder (Functional)        (None, 28, 28, 1)         471937    \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 941,741\n",
      "Trainable params: 941,741\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "vae.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Might be a lot of parameters to train but otherwise looks okay "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(vae, to_file = \"vae.jpg\", show_shapes = True, show_layer_names = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Compile and train the VAE model (3 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `compile_vae` methods compiles the VAE model passed as the sole argument. The method computes\n",
    "\n",
    "1. the image reconstruction loss (`reco_loss`), as well as\n",
    "2. the Kullback-Leibler (KL) loss (`kl_loss`) for the latent space p.d.f.\n",
    "\n",
    "The first loss measures how well the VAE is doing at reconstructing the input images; the second loss quantifies how continuous the latent representation is. The two losses are then summed (and added as separate metrics for monitoring), and the VAE model is compiled with the Adam optimiser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compile_vae (vae):\n",
    "\n",
    "    # Get the latent p.d.f. mean and log-variance output layers from VAE encoder\n",
    "    encoder   = vae.get_layer('encoder')\n",
    "    z_log_var = encoder.get_layer('z_log_var').output\n",
    "    z_mean    = encoder.get_layer('z_mean').output\n",
    "\n",
    "    # Define reconstruction loss\n",
    "    def reco_loss (y_true, y_pred):\n",
    "        # Use binary cross-entropy loss\n",
    "        reco_loss_value = binary_crossentropy(y_true, y_pred) # Averages over axis=-1\n",
    "        reco_loss_value = K.sum(reco_loss_value, axis=(1,2))\n",
    "        return reco_loss_value\n",
    "\n",
    "    # Define Kullback-Leibler loss with reference to encoder output layers\n",
    "    def kl_loss (y_true, y_pred):\n",
    "        kl_loss_value = 0.5 * (K.square(z_mean) + K.exp(z_log_var) - 1. - z_log_var)\n",
    "        kl_loss_value = K.sum(kl_loss_value, axis=-1)\n",
    "        return kl_loss_value\n",
    "\n",
    "    # Define VAE loss\n",
    "    def vae_loss (y_true, y_pred):\n",
    "        return reco_loss(y_true, y_pred) + kl_loss(y_true, y_pred)\n",
    "\n",
    "    vae.compile(optimizer='adam', loss=vae_loss, metrics=[reco_loss, kl_loss])\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. Construct, compile, and train a VAE using the methods provided above (1 mark)\n",
    "\n",
    "* Compile the model.\n",
    "* Train for ca. **50 epochs**.\n",
    "* Plot the all of the loss histories in `vae.history.history`. Use a suitable fraction of training data for validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define parameters\n",
    "latent_dim =  2\n",
    "nb_epochs  =  50\n",
    "batch_size = 128"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "compile_vae(vae)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 48000 samples, validate on 12000 samples\n",
      "WARNING:tensorflow:OMP_NUM_THREADS is no longer used by the default Keras config. To configure the number of threads, use tf.config.threading APIs.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-19 21:53:03.674053: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-01-19 21:53:03.698796: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:354] MLIR V1 optimization pass is not enabled\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "48000/48000 [==============================] - ETA: 0s - loss: 193.0362 - reco_loss: 189.4236 - kl_loss: 3.6126"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda-2022.10/envs/daml/lib/python3.9/site-packages/keras/engine/training_v1.py:2332: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates = self.state_updates\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "48000/48000 [==============================] - 17s 350us/sample - loss: 193.0362 - reco_loss: 189.4236 - kl_loss: 3.6126 - val_loss: 167.7995 - val_reco_loss: 163.0262 - val_kl_loss: 4.7733\n",
      "Epoch 2/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 161.3915 - reco_loss: 156.2536 - kl_loss: 5.1379 - val_loss: 158.2976 - val_reco_loss: 153.0482 - val_kl_loss: 5.2494\n",
      "Epoch 3/50\n",
      "48000/48000 [==============================] - 17s 347us/sample - loss: 155.2564 - reco_loss: 149.7075 - kl_loss: 5.5489 - val_loss: 154.3381 - val_reco_loss: 148.6319 - val_kl_loss: 5.7063\n",
      "Epoch 4/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 152.2742 - reco_loss: 146.5059 - kl_loss: 5.7683 - val_loss: 152.3644 - val_reco_loss: 146.8031 - val_kl_loss: 5.5613\n",
      "Epoch 5/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 150.1859 - reco_loss: 144.2785 - kl_loss: 5.9074 - val_loss: 150.5071 - val_reco_loss: 144.8102 - val_kl_loss: 5.6969\n",
      "Epoch 6/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 148.8204 - reco_loss: 142.8035 - kl_loss: 6.0170 - val_loss: 149.3255 - val_reco_loss: 143.1899 - val_kl_loss: 6.1356\n",
      "Epoch 7/50\n",
      "48000/48000 [==============================] - 17s 348us/sample - loss: 147.6398 - reco_loss: 141.5450 - kl_loss: 6.0948 - val_loss: 148.6973 - val_reco_loss: 142.5720 - val_kl_loss: 6.1253\n",
      "Epoch 8/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 146.6622 - reco_loss: 140.4903 - kl_loss: 6.1718 - val_loss: 147.0565 - val_reco_loss: 140.8026 - val_kl_loss: 6.2538\n",
      "Epoch 9/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 145.6090 - reco_loss: 139.3827 - kl_loss: 6.2263 - val_loss: 146.5676 - val_reco_loss: 140.2824 - val_kl_loss: 6.2852\n",
      "Epoch 10/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 144.7348 - reco_loss: 138.4449 - kl_loss: 6.2898 - val_loss: 145.6485 - val_reco_loss: 139.1924 - val_kl_loss: 6.4561\n",
      "Epoch 11/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 143.9992 - reco_loss: 137.6596 - kl_loss: 6.3397 - val_loss: 145.6071 - val_reco_loss: 139.4630 - val_kl_loss: 6.1441\n",
      "Epoch 12/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 143.4998 - reco_loss: 137.1191 - kl_loss: 6.3807 - val_loss: 144.7420 - val_reco_loss: 138.4006 - val_kl_loss: 6.3414\n",
      "Epoch 13/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 142.8949 - reco_loss: 136.4715 - kl_loss: 6.4234 - val_loss: 144.5068 - val_reco_loss: 138.0154 - val_kl_loss: 6.4915\n",
      "Epoch 14/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 142.3692 - reco_loss: 135.8781 - kl_loss: 6.4910 - val_loss: 143.9489 - val_reco_loss: 137.4020 - val_kl_loss: 6.5469\n",
      "Epoch 15/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 142.0516 - reco_loss: 135.5455 - kl_loss: 6.5061 - val_loss: 143.7482 - val_reco_loss: 137.2283 - val_kl_loss: 6.5200\n",
      "Epoch 16/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 141.4259 - reco_loss: 134.8662 - kl_loss: 6.5597 - val_loss: 143.3455 - val_reco_loss: 136.5471 - val_kl_loss: 6.7984\n",
      "Epoch 17/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 141.0337 - reco_loss: 134.4415 - kl_loss: 6.5923 - val_loss: 143.0926 - val_reco_loss: 136.5707 - val_kl_loss: 6.5219\n",
      "Epoch 18/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 140.6566 - reco_loss: 134.0306 - kl_loss: 6.6260 - val_loss: 142.4904 - val_reco_loss: 135.7188 - val_kl_loss: 6.7716\n",
      "Epoch 19/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 140.2871 - reco_loss: 133.6368 - kl_loss: 6.6503 - val_loss: 142.1609 - val_reco_loss: 135.4880 - val_kl_loss: 6.6729\n",
      "Epoch 20/50\n",
      "48000/48000 [==============================] - 17s 349us/sample - loss: 139.9754 - reco_loss: 133.2880 - kl_loss: 6.6875 - val_loss: 142.1121 - val_reco_loss: 135.4325 - val_kl_loss: 6.6796\n",
      "Epoch 21/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 139.6118 - reco_loss: 132.9199 - kl_loss: 6.6919 - val_loss: 142.2937 - val_reco_loss: 135.5921 - val_kl_loss: 6.7016\n",
      "Epoch 22/50\n",
      "48000/48000 [==============================] - 17s 351us/sample - loss: 139.3884 - reco_loss: 132.6732 - kl_loss: 6.7152 - val_loss: 141.6752 - val_reco_loss: 134.9142 - val_kl_loss: 6.7610\n",
      "Epoch 23/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 138.9751 - reco_loss: 132.2318 - kl_loss: 6.7433 - val_loss: 141.3743 - val_reco_loss: 134.3989 - val_kl_loss: 6.9754\n",
      "Epoch 24/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 138.7126 - reco_loss: 131.9587 - kl_loss: 6.7539 - val_loss: 141.2704 - val_reco_loss: 134.5818 - val_kl_loss: 6.6886\n",
      "Epoch 25/50\n",
      "48000/48000 [==============================] - 17s 351us/sample - loss: 138.2673 - reco_loss: 131.4786 - kl_loss: 6.7887 - val_loss: 141.2526 - val_reco_loss: 134.5058 - val_kl_loss: 6.7468\n",
      "Epoch 26/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 138.1033 - reco_loss: 131.3056 - kl_loss: 6.7977 - val_loss: 141.2537 - val_reco_loss: 134.4236 - val_kl_loss: 6.8301\n",
      "Epoch 27/50\n",
      "48000/48000 [==============================] - 17s 352us/sample - loss: 137.9407 - reco_loss: 131.1161 - kl_loss: 6.8247 - val_loss: 141.3283 - val_reco_loss: 134.3770 - val_kl_loss: 6.9513\n",
      "Epoch 28/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 137.9197 - reco_loss: 131.0957 - kl_loss: 6.8240 - val_loss: 141.9520 - val_reco_loss: 135.3425 - val_kl_loss: 6.6095\n",
      "Epoch 29/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 137.7962 - reco_loss: 130.9628 - kl_loss: 6.8333 - val_loss: 140.9473 - val_reco_loss: 134.0581 - val_kl_loss: 6.8892\n",
      "Epoch 30/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 137.5617 - reco_loss: 130.7139 - kl_loss: 6.8477 - val_loss: 140.9883 - val_reco_loss: 134.1100 - val_kl_loss: 6.8783\n",
      "Epoch 31/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 137.3030 - reco_loss: 130.4257 - kl_loss: 6.8774 - val_loss: 141.0725 - val_reco_loss: 134.4414 - val_kl_loss: 6.6311\n",
      "Epoch 32/50\n",
      "48000/48000 [==============================] - 17s 350us/sample - loss: 137.2205 - reco_loss: 130.3377 - kl_loss: 6.8829 - val_loss: 141.0727 - val_reco_loss: 134.3172 - val_kl_loss: 6.7556\n",
      "Epoch 33/50\n",
      "48000/48000 [==============================] - 17s 347us/sample - loss: 137.0364 - reco_loss: 130.1403 - kl_loss: 6.8961 - val_loss: 140.4446 - val_reco_loss: 133.6312 - val_kl_loss: 6.8134\n",
      "Epoch 34/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 136.8681 - reco_loss: 129.9618 - kl_loss: 6.9063 - val_loss: 140.0770 - val_reco_loss: 133.1420 - val_kl_loss: 6.9350\n",
      "Epoch 35/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 136.6208 - reco_loss: 129.6809 - kl_loss: 6.9398 - val_loss: 140.4068 - val_reco_loss: 133.4610 - val_kl_loss: 6.9457\n",
      "Epoch 36/50\n",
      "48000/48000 [==============================] - 17s 347us/sample - loss: 136.5730 - reco_loss: 129.6317 - kl_loss: 6.9412 - val_loss: 140.1082 - val_reco_loss: 133.1168 - val_kl_loss: 6.9914\n",
      "Epoch 37/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 136.1638 - reco_loss: 129.2021 - kl_loss: 6.9617 - val_loss: 140.3683 - val_reco_loss: 133.3707 - val_kl_loss: 6.9977\n",
      "Epoch 38/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 136.1086 - reco_loss: 129.1550 - kl_loss: 6.9536 - val_loss: 139.9523 - val_reco_loss: 132.8478 - val_kl_loss: 7.1045\n",
      "Epoch 39/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 136.0846 - reco_loss: 129.0863 - kl_loss: 6.9984 - val_loss: 140.5218 - val_reco_loss: 133.4437 - val_kl_loss: 7.0781\n",
      "Epoch 40/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 136.0544 - reco_loss: 129.0710 - kl_loss: 6.9834 - val_loss: 140.3463 - val_reco_loss: 133.3203 - val_kl_loss: 7.0260\n",
      "Epoch 41/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 135.8598 - reco_loss: 128.8609 - kl_loss: 6.9989 - val_loss: 139.9889 - val_reco_loss: 132.9850 - val_kl_loss: 7.0038\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 42/50\n",
      "48000/48000 [==============================] - 17s 345us/sample - loss: 135.7558 - reco_loss: 128.7449 - kl_loss: 7.0108 - val_loss: 139.9777 - val_reco_loss: 133.0226 - val_kl_loss: 6.9551\n",
      "Epoch 43/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 135.6253 - reco_loss: 128.5885 - kl_loss: 7.0369 - val_loss: 139.9680 - val_reco_loss: 132.9128 - val_kl_loss: 7.0552\n",
      "Epoch 44/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 135.5503 - reco_loss: 128.5326 - kl_loss: 7.0176 - val_loss: 140.3880 - val_reco_loss: 133.4656 - val_kl_loss: 6.9224\n",
      "Epoch 45/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 135.3914 - reco_loss: 128.3531 - kl_loss: 7.0383 - val_loss: 140.6746 - val_reco_loss: 133.7708 - val_kl_loss: 6.9038\n",
      "Epoch 46/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 135.4502 - reco_loss: 128.3945 - kl_loss: 7.0556 - val_loss: 139.6666 - val_reco_loss: 132.8317 - val_kl_loss: 6.8349\n",
      "Epoch 47/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 135.1903 - reco_loss: 128.1504 - kl_loss: 7.0399 - val_loss: 139.6394 - val_reco_loss: 132.6581 - val_kl_loss: 6.9813\n",
      "Epoch 48/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 134.9962 - reco_loss: 127.9473 - kl_loss: 7.0489 - val_loss: 139.7064 - val_reco_loss: 132.7227 - val_kl_loss: 6.9837\n",
      "Epoch 49/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 134.8538 - reco_loss: 127.7637 - kl_loss: 7.0901 - val_loss: 139.4715 - val_reco_loss: 132.3622 - val_kl_loss: 7.1094\n",
      "Epoch 50/50\n",
      "48000/48000 [==============================] - 17s 346us/sample - loss: 134.8950 - reco_loss: 127.8080 - kl_loss: 7.0870 - val_loss: 139.7182 - val_reco_loss: 132.7091 - val_kl_loss: 7.0090\n"
     ]
    }
   ],
   "source": [
    "history = vae.fit(X_train,X_train, epochs=nb_epochs, batch_size=batch_size, shuffle=True, validation_split=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vae.history.history[\"loss\"],label=\"loss\")\n",
    "plt.plot(vae.history.history[\"reco_loss\"],label=\"reco_loss\")\n",
    "plt.plot(vae.history.history[\"val_loss\"],label=\"val_loss\")\n",
    "plt.plot(vae.history.history[\"val_reco_loss\"],label=\"val_reco_loss\")\n",
    "plt.title(\"Loss and validation loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vae.history.history[\"kl_loss\"],label=\"kl_loss\")\n",
    "plt.plot(vae.history.history[\"val_kl_loss\"],label=\"val_kl_loss\")\n",
    "plt.title(\"kl_loss and val_kl_loss\")\n",
    "plt.xlabel(\"Epochs\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Yes -- but please put them superimposed one 1 canvas "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 5. Predict the test digits into the 2D laten space (1 mark)\n",
    "\n",
    "- Use the encoder to predict images into the laten space\n",
    "- Make a scatter plot per digit (use masking, see our first ML Lecture 2) and include a legend\n",
    "- Include a grid.\n",
    "- Discuss the results. Why do you think some digits overlap, and why are some clearly seperated? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda-2022.10/envs/daml/lib/python3.9/site-packages/keras/engine/training_v1.py:2356: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.\n",
      "  updates=self.state_updates,\n"
     ]
    }
   ],
   "source": [
    "z_mean, z_log_var, z = encoder.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for num in np.unique(y_test):\n",
    "    mask = np.where(y_test==num)[0]\n",
    "    plt.scatter(z[:,0][mask],z[:,1][mask],alpha=0.5,label=num)\n",
    "    \n",
    "plt.xlabel(\"latent dim 1\")\n",
    "plt.ylabel(\"latent dim 2\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the results. Why do you think some digits overlap, and why are some clearly seperated?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This plot shows where the encoder predicts the different digits within the latent space. Each of the different colours represents a given digit.\n",
    "\n",
    "The results show that test digits are predicted into defined clusters in the 2D latent space. Digits of the same number tend to be mapped onto the same region.\n",
    "\n",
    "The clusters formed by some digits overlap as the digits share many of the features which the network has learned about them.\n",
    "\n",
    "On the other hand, some of the clusters are more clearly separated from the rest as the network has been able to learn many features unique to these digits, making them more distinguishable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Grand! Quite happy with this -- seems liek you doin't have a lot distinction between 0 and 5 it looks like, though, or is that 7? I would like more discussion about the overlaps and how it could be avoided  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6. Generate samples across the latent space of the VAE (2 marks)\n",
    "\n",
    "* Define an equally-spaced set of $\\boldsymbol{N \\approx 10}$ **points** along each of the two latent dimensions, that you want to sample (_e.g._ using the `np.linspace` method in the range $(-2,2)$). You can play around with this range in order to focus on areas you personally think might be interesting (look at your grid plot above).\n",
    "* Convert these into a grid of pairs of coordinates using the `np.meshgrid` method.\n",
    "* Define an $(N^{2}, 2)$ array, and fill it with all of these pairs of latent space coordinates (you'll need to `flatten` the two outputs from `np.meshgrid`).\n",
    "* Get the decoder predictions (_i.e._ the generated images) for this set of $N^{2}$ coordinates in the latent space.\n",
    "* Display them in a grid, _e.g._ using the `plt.imshow` method.\n",
    "* Discuss the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,Y = np.meshgrid(np.linspace(-2,2,10),np.linspace(-2,2,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.flatten()\n",
    "Y = Y.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_coords = np.array([X,-Y]).T # minus sign to get correct spatial orientation of generated images on the grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "gen_imgs = decoder.predict(latent_coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 100 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for i in range(100):\n",
    "    plt.subplot(10,10,i+1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(gen_imgs[i],cmap=plt.cm.binary_r)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discuss the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A range of equally spaced coordinate values were selected within a region of the latent space which represents a square with length 4 centered on the origin and parallel with the axes. \n",
    "\n",
    "The latent space coordinates were then fed to a decoder which generated images based on the features that were encoded within those points in the latent space. \n",
    "\n",
    "These set of generated images show that as we move around regions in the latent space the features that are encoded produce different digits, with some digits being clearly decoded and others being quite ambiguous.\n",
    "\n",
    "The distinctly generated digits correspond to regions at the centre of pure digit clusters that are well separated. These regions have a clear encoding which contains the features of only a single digit (such as 0 and 1 at the bottom-left and top-left corners in the grid respectively).\n",
    "\n",
    "The less legibly generated digits correspond to regions at the boundary of different clusters, where multiple digit representations lie. At these regions the encoding is quite ambiguous, storing a mixture of features from different digits (such as 9 and 4, 6 and 0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Nice well done and good interaction with the code and the outputs -- I can tell you're really thinking about it "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generative Adversarial networks (2 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Putting together a GAN by yourself is a big task and outside the scope of what we can cover.\n",
    "\n",
    "Instead here we provide the building blocks for creating the GAN on the MNIST dataset. Your task is to go through individual components and show your understanding. The source material can be found [here](https://machinelearningmastery.com/how-to-develop-a-generative-adversarial-network-for-an-mnist-handwritten-digits-from-scratch-in-keras/) with ample explenations, so think of it as a short literature review, the point being to get you to engage with the material.\n",
    "\n",
    "\n",
    "**Suggestion**: you could choose to answer the question in the same markdown block they are asked. Make the answer distiguishable by for example using a seperator by typing `---` on a new line.\n",
    "\n",
    "\n",
    "\n",
    "#### 7. Discuss components of generative adverserial NN (0.5 marks)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# all imports needed from here\n",
    "from numpy import expand_dims\n",
    "from numpy import zeros\n",
    "from numpy import ones\n",
    "from numpy import vstack\n",
    "from numpy.random import randn\n",
    "from numpy.random import randint\n",
    "from tensorflow.keras.datasets.mnist import load_data\n",
    "from keras.optimizers import Adam\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.layers import Reshape\n",
    "from tensorflow.keras.layers import Flatten\n",
    "from tensorflow.keras.layers import Conv2D\n",
    "from tensorflow.keras.layers import Conv2DTranspose\n",
    "from tensorflow.keras.layers import LeakyReLU\n",
    "from tensorflow.keras.layers import Dropout\n",
    "from matplotlib import pyplot\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "- Defined is the discriminator model using the `Sequential` keras function\n",
    "   \n",
    "    - What is the purpose of the discriminator?\n",
    "\n",
    "The purpose of the discriminator network is to try and distinguish between real images from the MNIST dataset and fake images that are created by the generator model.\n",
    "\n",
    "    - Discuss the structure. What are the LeakyReLU and Dropout layers?\n",
    "    \n",
    "The structure of the discriminator network consists of two convolutional layers each followed by a leaky relu and dropout layer. The final image representation is then flattened and passed to a single output node with the sigmoid activation function to predict whether the given image is real or fake.   \n",
    "\n",
    "The convolutional layer has 64 (3,3) kernels to that are trained and used to pick out important images features. \n",
    "\n",
    "The leaky relu layer consists of an activation function that acts on the convolved image to down scale the negative values while unaltering the positive values. The dropout layer\n",
    "\n",
    "The dropout layer randomly sets values of the convolved and leaky relu treated image to zero with a given rate. The image pixel values are rescaled by 1/(1-rate) to make sure the sum of pixel values remains the same after performing dropout. \n",
    "    \n",
    "    \n",
    "    - Why is binary crossentropy used as the loss function?\n",
    "    \n",
    "The binary crossentropy function is used as the loss function as it is suitable for training in a binary classification problem."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# yeah all good! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the standalone discriminator model\n",
    "def define_discriminator(in_shape=(28,28,1)):\n",
    "\tmodel = Sequential()\n",
    "\tmodel.add(Conv2D(64, (3,3), strides=(2, 2), padding='same', input_shape=in_shape))\n",
    "\tmodel.add(LeakyReLU(alpha=0.2))\n",
    "\tmodel.add(Dropout(0.4))\n",
    "\tmodel.add(Conv2D(64, (3,3), strides=(2, 2), padding='same'))\n",
    "\tmodel.add(LeakyReLU(alpha=0.2))\n",
    "\tmodel.add(Dropout(0.4))\n",
    "\tmodel.add(Flatten())\n",
    "\tmodel.add(Dense(1, activation='sigmoid'))\n",
    "\t# compile model\n",
    "\topt = Adam(lr=0.0002, beta_1=0.5)\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "\treturn model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_3\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_5 (Conv2D)           (None, 14, 14, 64)        640       \n",
      "                                                                 \n",
      " leaky_re_lu_5 (LeakyReLU)   (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " dropout_2 (Dropout)         (None, 14, 14, 64)        0         \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 7, 7, 64)          36928     \n",
      "                                                                 \n",
      " leaky_re_lu_6 (LeakyReLU)   (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " dropout_3 (Dropout)         (None, 7, 7, 64)          0         \n",
      "                                                                 \n",
      " flatten_2 (Flatten)         (None, 3136)              0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 3137      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 40,705\n",
      "Trainable params: 40,705\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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BQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJShERGxdRGkjrVr1+Lf//43iouLb7WdOHECANC6detbbQ4ODnjiiScQFRVV4zWSuhhoZFFHjhxBx44dq9X38OHD6NChg5UrotqEgUYW16ZNm1tnZZUJCQnBqVOnaqgiqi14D40sbty4cdBqtZVO12q1mDRpUg1WRLUFz9DI4s6cOYOQkBBUdWidOnUKISEhNVgV1QY8QyOLa9GiBTp16gSNRlNhmkajQZcuXRhmZBUMNLKK8ePHw9HRsUK7o6Mjxo8fb4OKqDbgJSdZRWpqKgICAvQ+vgGUfFzj4sWL8Pf3t1FlpDKeoZFV+Pr64sEHH9Q7S3N0dETv3r0ZZmQ1DDSymnHjxlWrjchSeMlJVpOdnY2GDRuioKAAQMnHNVJTU+Ht7W3jykhVPEMjq/H09MSgQYPg5OQEJycnDB48mGFGVsVAI6uKjo5GUVERioqK+HebZHVOti7AEhISEnD+/Hlbl0FGFBQUwNnZGSKCGzduYMOGDbYuiYwICgpCWFiYrcu4Y0rcQ4uIiEBcXJytyyCyWzqdDrGxsbYu444pcYYGqLNDVLRt2zZoNBqEh4dXmKbRaBATE4PIyEgbVEZAyQmBKpQJNLp79e/f39YlUC3BQCOrc3LiYUY1g085iUgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQLMTRZePYeuyvyOqbyia1HeHs5s3GrXsgD5RryPmSCaKbz8ECtfroNFoSl6u0dhschXJWNJTUzaGwStw+i7TV8xsRUg78DlemTgQnZr7wcvNBe71GyOkXVf0HfU3vLH0P9hzOgtFNVhRdZi2H7dhYh0j29rBGR71/NC8w0OImPoW1h1MRaGN1uduw0CzE9tnh2HwtFgUDVuEb/5IRnbGaez6bBqaHH4XY7p0wuPxqbcdw2lMHEQuY2m/GijYmoqTsW32A2j5wN+R4DsaS7YexYWsbKQc24X/LJ6MztlbMX/qKPQImYjYu+ydbtp+HIhVVwVyaC6CAWDYahSIQAqycen4Lqx7cyT8jy/DhPtaoNOkVTiWa6OVupuIAnQ6neh0OluXYVVbn/AS38nfSqFBe+HhV6Q1IGj5dzlSrZEuy9J+ELhEySaTq0iSxT2ayosHTJ6xUgAkJibGhDlyZe/LHcUZ3jJo+ekK20NERAr/lE8G1RdgmKwrsFChFmLWfjw0V4IBwbDVUnF1MuTHOfeJKyB+j66Us8Wm16TS+4dnaHZi4IospHzyMAx/i9yxQ3d0dQNwOhGJdv/dw7cnxxdjytuHUdBlDj54qkWF7QEAcGyGJ1+diKCaLq4aLL8f6+PBt2PwZjcnpHz1PGauz7BcsXaIgWbvrmUgIw9Au/Zop7F1MdaXsHwZjhQDXSIiSy7DKuEQtgjnJB5j7OWr2O5kP2paYMqzQ6BFNjZ+vAbJ1qjPTtTqQJOMX7Bi5kh0C/aBh4srvANbo0v/cXht1U84l1dJP2dnuNdrgo6DpmDJzotlN53jo+F068btUHxx9ju8Nfp+NPF2g3uDpugwdDbiT+eX9M1agf4GN3o7zj9eMq1wPYaXn6ZbX+U6pMduwE7UQ8Tcp9HKYFre8Q2YM7wzgrxc4eLhg1a9JmLxrjTc2YlcHn5d/TwGd2wGHw8XuHoFoG2vMZj75VHk3NG41fPTT+cBeCA0tKo4M85e92N1ePbsiVAAsvdn7CowYwBV2Pqa1xLMugeQFC/jmmvFwb+/zNv0q1zMzpWc5N9ky7xw8QWkx+Lzev00fn3lzU2/ysUrOZJy4jtZPLK5OGl8ZeinJ8vdDymQ1cMgQLB0HDZRVuw5J1lXs+TM9rnSzQ2i7fq2nLrVN01WDHQTOHSWdxMNi/tLFoZ5y8gvM6teh+Q40fk5Sosnt0iGwaTCU0ulrzfEofEgWfDtcUnNuSIXj26Ulwf0krbN7uQempu0G/eefH0wUdKuZsul4zvlo3H3iDtc5J5p38plE0eEiffQpgVAgCCZvc/EBdnpfhSR29xDuylvtQwBBHhA3r90+81Rnkr30GppoOXK5ok+AtSViBjDt2CmrBiovRloZf1GrTM4KK/vlCmNIHAbIJ8llzaWvhHqyYRN18p3lvU6FwF6y9K0cq07/ib+gARO/V7yy/Uu2DVdgpo8J3uquqGd/r3M6FhXmo5eI2cr3BnPkZgITwE8ZUzcFf1JFz+Rh1zMDbTKnJJ37tcK0FRm7S0yaU7zAq2JzNlvylLsdT/eVJ1Ay/1CBjPQautDgd3YuDENQBgGDTL8Je96eGJrPnZND9TrN2RIPf1uLn0wuJ87kLcDG78xvNi6F127upfvjKAgXwCXcOlSudZ+M/FsJwdcWLUQZfdys7B+wSoETZ+OsMru/1zbj1fCh2HjPZ/i57VRaFrhzvgebNuWDSAM4eGe+pMa9UEfc65pqhSCCF0nAH9h06ajlh5cT6NGAJCB9HRT5rLX/WiCpCQkAYC2ERo1vINx7FztDLQbKUi5AsDVF751ze/n5+cLoBjJyWkGU7zg5aXf4uzsDKAYxXqfnGyJp194FB65W7Ho4z9Kmk4uw6KfBmDWk82N11SYiE8iHsFnjRdjx+ejEWTsTXAjDWk5AFx94FPHcKIvfH2ND30nAgICAACpqbf/PNyd6N27CYBrOHr0dPVnstf9aIIru3bhVwCasAfRS3tnY9mz2hloLn7w8wJwPRWpVd3Jvk2/lJRUAA7w9/cxu5T6o2djUpDgyIeLsP1GPrYveh9Xn5qFYUaDNg2bpgzCazdewrcbnkDwrf/5D+OlkEDM2ltatw986gK4noa0q4ZjZCIz0+xyK3Xp5imLrzXSspzuU55BFyfgYFws/qy0VzH2z2kJjUMo/nEc9rsfq6v4NJZ9tBWF8ILumShYdw/c3WpnoKEHRozwAZCAr7/OMpiWiLc6OaDZjN0oLtdvy5bL+t1u7MSW73IBt/4YEV7Vad5tOD2Amc+HwTF1LRYuWoSF61ti5nNdjeyYXOx/Yyie+j0Km/9vBtq5VDXoAxg0yAtAws1Lz3LSd2HXCfNKzVoxEJou/8AZwwlyEus3HALQFI88Emre4NWkafU8lr9+P1x/eRfPfXbW6J985Z/4CC9+kogGka9iahsAdrsfqyMTP/19DN7YX4iAER9gUUT9Ox3Qvtn6Jp4lmP+U00kcAvrL/M2/yaXsXLly/oCsebqzuPs9Imv+0u+n8esr8zb9JpeycyT1xPdlT8eWG3s6NkRW5+kv7sCLwQIEy9xDRmrJjhOdFwQajTQc/5XkVuhQLGdWDhUfQFDpq7G8kFA2R1Hicnm4PsSh8WBZ8O0JScvJlqTft8ib4Z2lka95DwUufxougId0nbpSfj6VKlfzciTp+PfyYVRbcYOL3DPtG7nN87wKYPJfCoiIpMj2l3pIPW0j6f/iKvnxRIpcvZEnly8ck+9XzJTeAQ7i3fMt+eVquVnsdD+KSMWHAkXXJTvllOyN/0Cm9Q0SZ3hI+8c/l+MVF1gtKj0UqL2BJiLF6ftl+fTh0rV5A3HTuohnQEt5YMzrEn8yt4p+TuLqFSgdwifLv747X/YmSHhBGhscnK3nHhKRWBlleNAOWSn675Mi2Tc7RIDW8toRY3+7kicrh1T1JjD+Rsg9EStzhneWQE9n0brVk2ZddfL65sPyQb+y+bye2Fr9DXY9SfbHLpRpI3pJaHCAeLpoxdWrkdzb5zF55cujkl39kW4xL9BERAoldf/n8vK4ftI+qL64a53Eua6ftOw+XKa9t13O3ag4h/3tx60ywcNIH42TuHn5SNP2D8qop+fLuoOplT/9rAaVAk0jInb/BzMREREAgNjYWBtXQqbSaDSIiYlBZGSkrUuptVR6/9TSe2hEpCIGGhEpg4FW68VBV8kXNpZ/tXvjN1sXSnRb9vJdBGQ1OsTZ/21UIgA8QyMihTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZSjzbRsXLlzAhg0bbF0GmSEhIcHWJdRqFy5cQGBgoK3LsAhlvoI7Li7O1mUQ2S2dTqfEV3ArEWh0dyv9vQCeQZO18R4aESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKcPJ1gWQWvbt24cjR47otZ05cwYAsHz5cr320NBQdO/evcZqI/Ux0MiiUlNTMWXKFDg6OsLBoeQCQEQAAM8++ywAoLi4GEVFRfjqq69sViepSSOlRxuRBRQUFKBhw4bIzs6usl/dunWRnp4OZ2fnGqqMagPeQyOL0mq1GDNmTJVBpdVqMXbsWIYZWRwDjSxu7NixyM/Pr3R6QUEBHnvssRqsiGoLXnKSxRUXF6NRo0ZISUkxOt3HxwfJycm37rERWQqPKLI4BwcHREdHG72kdHZ2xoQJExhmZBU8qsgqKrvszM/Px9ixY21QEdUGvOQkqwkJCcHp06f12po2bYqzZ8/apiBSHs/QyGqio6Oh1Wpv/dvZ2RmTJk2yYUWkOp6hkdUkJiaiZcuWem0nTpxAq1atbFQRqY5naGQ1ISEhCA0NhUajgUajQWhoKMOMrIqBRlY1fvx4ODo6wtHREePHj7d1OaQ4XnKSVV26dAlBQUEQEZw7dw6BgYG2LolUJmbQ6XQCgC+++OLLKi+dTmdONInZ37bRvXt3zJgxw9zZqRbZsWMHNBoN+vXrV2mfhIQELFmyBDExMTVYGd2NFi9ebPa8ZgdaYGAgIiMjzV4w1R6lQdagQYMq+y1ZsoTHFCE2Ntbsefl9aGR1twsyIkvhU04iUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZNRJoZxd2v/U1zJrA6dir2PJsrebXNxlLemrKlmnwCpy+y+oVAMDEOhWX3X3h2RpZdinrbfttRtev9OXk6o2g0IF4+qM9yBDLLsPoNrywBN0r1OGE6Hj9bodfaWPQpx3mHze3PtPVSKA1m7UXIqcwr0NNLK3ml2drtW19S626KpBDcxEMAMNWo0AEe2c1q9EarLftBxpdPxFB8Y0snN37GR7z/AXLnu2DB1/chTwLLsPoNgycjr0iKFg3CgAQ/OIBiBRizXD9bh3nH4dIGj7oDYR/ehkiv+GVNmYVZxZecpKZmuLFAyVvMMPXhSU9bV2c0jTOXgjsOBLvrHsV3R0K8MfiN7Eu09ZV3R0YaET2KqgbugUAKDyIg0dsXczdgYFGZLcEJT9x5ApXV1vXcne4KwJN0vZi6XOPokuzBnB3doGHTzC6jXwBnx++ot+xMA0HY+Zj4sOdEeJfBy5u3ghsPwCT3/sZacW3X871NcMrvcla+upYegezcD2Gl5+mW1+9lYmPhtOt+YZi5YkteD2yG5o3cL/Z3gfL0k1cbzMdn9+xrP6eS5BcOmHbk6hT2t7wb/jBrNHz8Ovq5zG4YzP4eLjA1SsAbXuNwdwvjyLHItVbh1LH2rl92J8EwDMMYfeauZ6KsX2gJf0H0V17YeqGaxj58W78lZmOxB+WIDxzNSaGPYhXEnLL+m6bjR5j3kdK33ex7VgqMs//gi8me2L7zL548MXduH6bRbmO/RibZ3ZH64ffQkJm6T2fNHwa7gY4dMa7iYLDpXcwncYgXv7CwjAvjFibCYkbU731Gb4GhdhGepwAACAASURBVFKA1cMA4L94c8oX8Jv6JX45n4lLe1/FfY5mrLeZ2rxyGCJbMcHDYMLAFbgqf+KfXe5k9BycuxyMaZ99h2Op6fhz/zpMa/4rlkTdj+7PbUfWnQxtLYoca1JwBRcO/wcvPjYPe7XNEPnhOxjlaeZ6qsbcn7Ez/WemTsm8DhA0fl4SbrXlyqYJDQWoKyPWpul3T/pU+rlAHLr8UxJL2zZNEPc+H0qKXsc8iY+uJ9AOkdVXqlje5X3yTnhr6fb8JrlQqL+o6988Jb6ABE79XvLLtRfsmi5BjafKT/liogJZPQwCNJDHv75mZLqJ610txraviMhWmeABQY/FkqTX/qf8swsEDabITpOWU3UN79yvFaCpzNpbZNKcMTExYtbheGiuBAOCYauloMqOdnqsla5fhZdWmo94R75JzL6z9Sy/jNtuQ5GCdaMEgAS/eKCKXmnyQW9I+KeXbzOaceblSwkbn6HtRnx8OuDwAB4d2lB/kn8/9LsXKD4Yj00XbrYNXYVrO5+Br15HV3To0AooOIRDvxtfyvUTqxHdrTf+x2EONi0ZisaO+tNdBszE0+01uLBqIdZnlLZmYf2Cz+A3bTp6ac1dv/a4/353I+0mrrfdCEGErhOAv7Bp01FbF2PAzo+1Wx/bKMDVC/uxdHRj/LXxDcxefgjX7mQ9TeTgWLJCRUVFVfQqQlER4OjoWEUf67BtoN1IQcoVAMXfYJKX4T2GFnj5vwBwCqdO3ex/5TDWvDYBfds3g389Nzje7Nt89j4Aucg1diZ9eQPGDf8CRYFeyN36d0xZcw4VP4fYBs9MHwjX3K1Y9PEfJU0nl2HR930wc3LLO1hBD3gYXu6Zs952JCAgAACQmppq40oMKHOsOcGjcVf8bdUaPBt8HUcXPIn5vxSav54mcqhTB+4AsrOzq+iVhawsB3h61jFvIXfAtoHm4g9/bwBOoxBbYPwzTSIZ+OghADiL9x/pgXHzvke9p1bh55NpuF5c0uf84h4oe+JjwKkT5u3YhnX/txGz21/BxqeG440DFY9Gn6iZiPITHPlwEbbfyMf2Re8jY8JMRNSz9XrfKQc4OADIz0e+XnsWsqxwo+vSpUsAAF9f39v0rGGqHWuuPfD6P3TwklN476UVuGTWepqhVSu0ApD5++9lD5kM3fgdvycGo2VLjZkLMZ+NLzl7YORIX6BwH37cXfE266l37odjk+ewpxBA0QH8uDsX8B+Omc/1QUufOtDe3F55eVV8TrpuS7Rp7AjUCcPbXy3D4DqHMG/4JPxvkkE/l/6YOTUUmtS1WLhoERauDcC053tb6YdLTVjvOxaAgAAAFy9A7yojeR/2nTNvxKwVA6Hp8g+cMZwgJ7F+wyEATfHII6HmDW5x8Rjj1A7zj6t3rNWPmIdZnRyR993/4H+2lwanNY6t0m0IIHgIhrYBsHcNVp8y/jdXqRs+xzafQRjUzsQVsoSau2lXyU3rlK9kYrBWnFoMl0Vf/yYXs/IkNyNRflw2Qdq6B8romPM3O56Tj/t6COAvQ9/dLifSrsmN3Aw58f1iGd7EUQAvmbL99su78sNMaauFeHSfL0euG5SYtlLC3SDQaKTOsNVi3i1NkbKHAkNkdV4lXaq93tVV2UOBG/LDs00F8JVHPtgnl3KuSUbiDnk7sr/c29i8hwKXPw0XwEO6Tl0pP59Klat5OZJ0/Hv5MKqtuMFF7pn2jWSaOKb1HgpslNGO98q8Y2Kfx9pt1i9nyyTxBcSp83w5Xnyz0dRjy5RtKCKZW5+WYC3EOXi4/PN/98vZjFwpKLwuVy7+Kls/elxCPRvJY7FJRkeqjjt5KFAjgfbngm4VntK0nnvo1vTijF9kxcwRcn+LBuKudZY6Pk2lw4DJ8q/tf+lv4LQ98v6UAdIuqIG4a7VSx6+19Jm4QNa+FFY2dpd/yqmbT2LKv3osPi+S8II0Nmh3idpYbgE35Jun/AVoLNN+NPnRZgkjywBGSayRrtVe79u43faVrIOy9Mne0irAU1zd6klwz8fl4wOHS55y3uxf9VMrA9eTZH/sQpk2opeEBgeIp4tWXL0ayb19HpNXvjwqhs/dqsOcQJvgYezpn7FX2ZvRfo61rUbXz3G04ZH0lyzp6VLW5+bT7Oqtp/Fl3G4biohkHPxCXo7uJx2aNZQ6Lk7i6Owh9QLvlV4RM2XZ7iQx7Rm3vjsJNI2I0bsBVYqIiAAAxMbGmjrrXe/yyiHw/6gX/vjlpZI/2KUasWHDBowePRpmHI52i8eacXeSL9a5RWS3zmLFsl0YMnMNDzCyMh5r1mD7vxSwsawVA1FvxCpcupqBo8tmYMHlyXg50hqPNqm247FmfbU+0AAgK34SGtdrgvClTnht/Ru4z+h5axx0t/nbPI1Gg3Zv/GbBympymbZYv9qnescamavWb07vJ7dBnqxOTx3iavz+Tk0u0xbrV7tU/1gjc/EMjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUYfa3bcTFxUGjqflfdSG18ZgiANDpdGbNZ9ZXcCckJOD8+fNmLZBqn8WLFwMAZsyYYeNKyF4EBQUhLCzM5PnMCjQiU0RGRgIo+d0AImviPTQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBlOti6A1JKbm4sbN27oteXn5wMALl++rNfu4uICd3f3GquN1KcREbF1EaSOjz76CM8++2y1+n744Yd45plnrFwR1SYMNLKotLQ0BAQEoKioqMp+jo6OSEpKgo+PTw1VRrUB76GRRfn4+KBv375wdHSstI+joyP69evHMCOLY6CRxUVHR6OqE38RQXR0dA1WRLUFLznJ4nJycuDj41Ph4UApZ2dnpKWlwdPTs4YrI9XxDI0srm7duhg6dCi0Wm2FaU5OTnj00UcZZmQVDDSyiqioKBQWFlZoLyoqQlRUlA0qotqAl5xkFfn5+WjYsCFycnL02uvUqYP09HS4uLjYqDJSGc/QyCqcnZ2h0+ng7Ox8q02r1SIyMpJhRlbDQCOreeyxx279lQAAFBQU4LHHHrNhRaQ6XnKS1RQXF8PPzw/p6ekAgAYNGiAlJaXKz6gR3QmeoZHVODg4ICoqCs7OztBqtYiOjmaYkVUx0Miqxo4di/z8fF5uUo2wu2/biIiIsHUJZKLSb9RYsGCBjSshU8XGxtq6BJPY3RlaXFwcLly4YOsyyARNmzZF06ZNq+xz4cIFxMXF1VBFdDv2uj/s7qGARqNBTEwMIiMjbV0KVdPvv/8OALj33nsr7bNhwwaMHj26yr8BpZpjr/vD7i45yf5UFWRElmR3l5xERJVhoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpo1YGWuF6HTQaTcnLNRqbbV2QHSm6fAxbl/0dUX1D0aS+O5zdvNGoZQf0iXodMUcyUVyDtUysoynbj6UvB2d41PND8w4PIWLqW1h3MBUVf0yPVFUrA81pTBxELmNpP1tXYn+2zw7D4GmxKBq2CN/8kYzsjNPY9dk0NDn8LsZ06YTH41NrrJZVVwVyaC6CAWDYahSIQAqycen4Lqx7cyT8jy/DhPtaoNOkVTiWW2NlkQ3VykCrvbaVnNX0XILkOxjF9/GlWPv8w2jr7wlX9wZo0etJrPxyFloXncPnc5bgqMXqNYOjK7z8WqL7sGn44Psj2DGnLRJXTcJDY1fhL/v6aq87YJn9bI8YaGSSgSuykPLJwzD8qRPHDt3R1Q3A6UQk3jXBUR8Pvh2DN7s5IeWr5zFzfYatCyIrY6CRZVzLQEYegHbt0U5j62LK0bTAlGeHQItsbPx4Ta07Y6ltakWg5R3fgDnDOyPIyxUuHj5o1WsiFu9KQ4UTifhoON26wTwUK09sweuR3dC8gfvN9j5YVvITk5CMX7Bi5kh0C/aBh7Mz3Os1QcdBU7Bk50UU3Rzu7MLuZTerA6dj64GPMbnfPfCv6wpnd1+0eegJvL87rUK91Rn7+PyOZWOXv7TY9iTqlLY3/Bt+0KtlED6/BmD3DASU9nEag3gLbOP02A3YiXqImPs0WllgPEvy7NkToQBk78/YVQDuZ5WJnQEgMTEx1e5feGqp9PWGODQeJAu+PS6pOVfk4tGN8vKAXtK2GQQuUbJJb44CWT0MAgRIs96R8tHOREm/licpe1+V+xx7y9I0EUmKl3HNtaLx6ytvbvpVLl7JkZQT38nikc3FSeMrQz89KYW3xjsl8zpA4OEvLcIel5V7/pTMq1ny14HVMiXUQ+DcRmb+kF22eJPG3ioTPCDosViS9NbhT/lnFwgaTJGdeu2V9b9DyXGi83OUFk9ukQwzh4iJiRGzDsdDcyUYEAxbLQWV9clbLUMAAR6Q9y+VNnI/V8Xs/WFjdlexaYGWIzERngJ4ypi4K/qTLn4iD7lUFWgN5PGvrxkZM1c2T/QRoK6MWpepP+n6TpnSCAK3AfJZcmnjzQMdrWTuoUK97sVHX5e2gGg6zJMTZo19FwRa+vcyo2NdaTp6jZwtvH33ylg10HK/kMGVBhr3szH2GmiKX3LuwbZt2QDCEB7uqT+pUR/0qfLaqD3uv9/dSPtubNyYBiAMQ4bU05/k0geD+7kDeTuw8Zsc/WkeYejZUf9WuqZ9OB5uBMiRrfgm6Q7GtpVr+/FK+DBsvOdT/Lw2Ck3v1h9FT0pCEgBoG6FRQ8OJ3M8qUTvQbqQhLQeAqw986hhO9IWvb1Uze8DDw9iYKUi5AsDVF751K0728/MFUIzkZIN7Jt7e8K7Qu7SGVKSm3sHYtlCYiE8iHsFnjRdjx+ejEXS3hhmAK7t24VcAmrAH0UtrOJX7WSVqB5qLD3zqAriehrSrhhMzkZlpzph+8PMCcD0VqUb+A01JSQXgAH9/H/0JGelIr/AU4uYBXnrAmzy2AxwcAOTnI1+vZxaysowVr4HGIk8g07BpyiC8duMlfLvhCQTf+jHEw3gpJBCz9lpiGRZSfBrLPtqKQnhB90wUqvw/rDzuZ7ukdqDhAQwa5AUg4ealZznpu7DrhDlj9sCIET4AErBly2X9STd2Yst3uYBbf4wIN/iv9/oe/HRA/49w5NdvsP0SoOkwCOEB5owdgIAAABcvQO+35JP3Yd85Y7W7w90d5d4Yv2Juaw0GLDcl2XOx/42heOr3KGz+vxlo52LCrDUuEz/9fQze2F+IgBEfYFFEfRPmre372U7Z+iaeqWDiU86ixOXycH2IQ+PBsuDbE5KWky1Jv2+RN8M7SyPfqh4KDJHVeZUMmhQv45o7icavr8zb9Jtcys6R1BPflz2hWm7k6ZdXa+nUb6L8++bTr3MH1tx6+vVChadf1R37hvzwbFMBfOWRD/bJpZxrkpG4Q96O7C/3NjZ2szhTVgzUCrwelVXnrkr6npck1KmdzP+juluzWM6sHCo+gKDSV2N5IaG645Wx2EOBouuSnXJK9sZ/INP6BokzPKT945/L8VzDGbmfq2KvDwXsrmJTA01EJPdErMwZ3lkCPZ1F61ZPmnXVyeubD8sH/creiF5PbBVJeEEaV3iDjpJYI2MWp++X5dOHS9fmDcRN6ySuXoHSIXyy/Ou786L/jOvmgd74efnujzXyXHg7CajrLFq3htKq9+Py3q5UKTZ7bBHJOihLn+wtrQI8xdWtngT3fFw+PnC45OnXzXUIfvHAre43jq+Vyb2CpaGHm3gH3SdRHx2Uq9XeknmyckhVYVazgTbBw8jyNU7i5uUjTds/KKOeni/rDqZWfPrJ/XxbDLQaYk6g2VbZgW7G+7zWsNc3UBm19rO97g/F76ERUW3CQCMiZTDQrKjk7+pa4tUjAC6+hzCNBm1eOWzrsoyIg87wixKNvNq98ZutC70r2c9+Vp/T7buQuZrN2guZZesqqkOHOLlrvvPH7tjPflYfz9CISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBl2+W0bixcvRmxsrK3LIAu6cKHk5z8iIiJsXAkBZfvD3mhE7Ot7Y3jA259jx44BANq2bWvjSshU9nbiYHeBRvYnMjISALBhwwYbV0Kq4z00IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUoZGRMTWRZA61q5di3//+98oLi6+1XbixAkAQOvWrW+1OTg44IknnkBUVFSN10jqYqCRRR05cgQdO3asVt/Dhw+jQ4cOVq6IahMGGllcmzZtbp2VVSYkJASnTp2qoYqotuA9NLK4cePGQavVVjpdq9Vi0qRJNVgR1RY8QyOLO3PmDEJCQlDVoXXq1CmEhITUYFVUG/AMjSyuRYsW6NSpEzQaTYVpGo0GXbp0YZiRVTDQyCrGjx8PR0fHCu2Ojo4YP368DSqi2oCXnGQVqampCAgI0Pv4BlDycY2LFy/C39/fRpWRyniGRlbh6+uLBx98UO8szdHREb1792aYkdUw0Mhqxo0bV602IkvhJSdZTXZ2Nho2bIiCggIAJR/XSE1Nhbe3t40rI1XxDI2sxtPTE4MGDYKTkxOcnJwwePBghhlZFQONrCo6OhpFRUUoKiri322S1TnZugBLSEhIwPnz521dBhlRUFAAZ2dniAhu3LiBDRs22LokMiIoKAhhYWG2LuOOKXEPLSIiAnFxcbYug8hu6XQ6xMbG2rqMO6bEGRqgzg5R0bZt26DRaBAeHl5hmkajQUxMDCIjI21QGQElJwSqUCbQ6O7Vv39/W5dAtQQDjazOyYmHGdUMPuUkImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDzU4UXT6Grcv+jqi+oWhS3x3Obt5o1LID+kS9jpgjmSi+/RAoXK+DRqMpeblGY7O5xRRcxPbFUzGkS3P41HVDHd8QdBk0GYs2HUd2jX0ZVRHSDnyOVyYORKfmfvByc4F7/cYIadcVfUf9DW8s/Q/2nM5CUU2VU02m7cdtmFhHU7bPSl8OzvCo54fmHR5CxNS3sO5gKgpttD53HVGATqcTnU5n6zKsausTXgKnYBm95Fv5I+mK5F1Ll9M/fSrj7nEVODaRCRtTqjnSZVnaDwKXKNlkTiFXf5H5Pb2lTujj8smPpyQjN0+u/LVbPtY1Fw1ay+u/mjYcAImJiTFtpqIk2TrrfvFyCpC+sz+TH44lS/b165KdfEaOfLtcXujfRBwBAYbJugLThrY2s/bjobkSDAiGrZYCEZHCPMlKPikJ8e/Lsw8FihYe0m7iSvnjmnk1qfT+YaDZia1PeInv5G+l0KC98PAr0hoQtPy7HKnWSHcSaLny3dPNBJ5DZXWywaSr62SYS00EWq7sfbmjOMNbBi0/XWF7iIhI4Z/yyaD6d22gmbwfDQNNT4b8OOc+cQXE79GVcrbY9JpUev/wktNODFyRhZRPHobhb5E7duiOrm4ATici0dqXeylfYN7ys/CLmoHH/AymeYxB/PXjeKOddUuQ44sx5e3DKOgyBx881aLC9gAAODbDk69ORJB1SzGL5fdjfTz4dgze7OaElK+ex8z1GZYr1g4x0OzdtQxk5AFo1x7tNNZdVMZX/8GPRQ54oGeYzQ6chOXLcKQY6BIRieAq+jmELcI5iccYe/kqtjvZj5oWmPLsEGiRjY0fr0GyNeqzE7U60CTjF6yYORLdgn3g4eIK78DW6NJ/HF5b9RPO5VXSz9kZ7vWaoOOgKViy82LZTef4aDjdunE7FF+c/Q5vjb4fTbzd4N6gKToMnY340/klfbNWoL/Bjd6O84+XTCtcj+Hlp+nWV7kO6bEbsBP1EDH3abQymJZ3fAPmDO+MIC9XuHj4oFWviVi8Kw3mnsgd/u9/IfBBk3qJ+PyFR9EpyBuuzm6o37QThj63DAcyrf9E4KefzgPwQGhoVXFmnL3ux+rw7NkToQBk78/YVWDGAKqw9TWvJZh1DyApXsY114qDf3+Zt+lXuZidKznJv8mWeeHiC0iPxef1+mn8+sqbm36Vi1dyJOXEd7J4ZHNx0vjK0E9PlrsfUiCrh0GAYOk4bKKs2HNOsq5myZntc6WbG0Tb9W05datvmqwY6CZw6CzvJhoW95csDPOWkV9mVr0OyXGi83OUFk9ukQyDSYWnlkpfb4hD40Gy4NvjkppzRS4e3SgvD+glbZuZcw+tdN3qiL9/Q2kdtVR+Pp0uOZfPyr7Pn5ZQD4i21VT5LsukQU2+hzYtAAIEyex9pi3HXvejiNzmHtpNeatlCCDAA/L+pdtvjvJUuodWSwMtVzZP9BGgrkTEXDaYlikrBmpvBlpZv1HrDA7K6ztlSiMI3AbIZ7dukJe+EerJhE3lHzldl/U6FwF6y9K0cq07/ib+gARO/V7yy/Uu2DVdgpo8J3uquqGd/r3M6FhXmo5eI2cr3BnPkZgITwE8ZUzcFf1JFz+Rh1zMCbQc+TQcAkDQfIbsM6jtj/mdRANI21er92iilHmB1kTm7DdlKfa6H2+qTqDlfiGDGWi19aHAbmzcmAYgDIMGGf6Sdz08sTUfu6YH6vUbMqSefjeXPhjczx3I24GN3+QYjHEvunZ1L98ZQUG+AC7h0qVyrf1m4tlODriwaiHK7uVmYf2CVQiaPh1hld3/ubYfr4QPw8Z7PsXPa6PQtMKd8T3Yti0bQBjCwz31JzXqgz7mXNPAHR4eJTd36vUfiPsNamv7yFCEADj2zTfmDF5tjRoBQAbS002Zy173owmSkpAEANpGaNTwDsaxc7Uz0G6kIOUKAFdf+NY1v5+fny+AYiQnpxlM8YKXl36Ls7MzgGIU631ysiWefuFReORuxaKP/yhpOrkMi34agFlPNjdeU2EiPol4BJ81Xowdn49GkLE3wY00pOUAcPWBTx3Dib7w9TU+dNUc0KxZyXPDBg0aVJzs6wtfAEgz3BaW1bt3EwDXcPTo6erPZK/70QRXdu3CrwA0YQ+il/bOxrJntTPQXPzg5wXgeipSDf9TNqFfSkoqAAf4+/uYXUr90bMxKUhw5MNF2H4jH9sXvY+rT83CMKNBm4ZNUwbhtRsv4dsNTyD41v/8h/FSSCBm7S2t2wc+dQFcT0PaVcMxMpGZaV6tHXv2hAeA5KSkihNTU5EKAH6Gn+ewrO5TnkEXJ+BgXCz+rLRXMfbPaQmNQyj+cRz2ux+rq/g0ln20FYXwgu6ZKJj1/5UiamegoQdGjPABkICvv84ymJaItzo5oNmM3Sgu12/Llsv63W7sxJbvcgG3/hgRXtVp3m04PYCZz4fBMXUtFi5ahIXrW2Lmc12N7Jhc7H9jKJ76PQqb/28G2rlUNegDGDTIC0DCzUvPctJ3YdcJ80p1G/wUohsDV7fFY8d1/WnHNm3GaTjgvuGPmDd4NWlaPY/lr98P11/exXOfnTX6J1/5Jz7Ci58kokHkq5jaBoDd7sfqyMRPfx+DN/YXImDEB1gUUf9OB7Rvtr6JZwnmP+V0EoeA/jJ/829yKTtXrpw/IGue7izufo/Imr/0+2n8+sq8Tb/JpewcST3xfdnTseXGno4NkdV5+os78GKwAMEy95CRWrLjROcFgUYjDcd/JbkVOhTLmZVDxQc3b8obfTWWFxLK5ihKXC4P14c4NB4sC749IWk52ZL0+xZ5M7yzNPI1/0+f0rb+TUKcHKXJsH/J9yfTJOfyX7L/i5KnnHW7vSm/VCy+SjDnT58kRba/1EPqaRtJ/xdXyY8nUuTqjTy5fOGYfL9ipvQOcBDvnm/JL1fLzWKn+1FEKj4UKLou2SmnZG/8BzKtb5A4w0PaP/65HDdx25dS6aFA7Q00ESlO3y/Lpw+Xrs0biJvWRTwDWsoDY16X+JO5VfRzElevQOkQPln+9d35sjdBwgvS2ODgbD33kIjEyijDg3bIStF/nxTJvtkhArSW144Y+9uVPFk5pKo3gfE3Qu6JWJkzvLMEejqL1q2eNOuqk9c3H5YP+pXN5/XEVhO3WpGk7flYng4PlcZeLuLkXFf82/SWcW/8R06a8YYyL9BERAoldf/n8vK4ftI+qL64a53Eua6ftOw+XKa9t13O3ag4h/3tx60ywcNIH42TuHn5SNP2D8qop+fLuoOplT/9rAaVAk0jIjX2/QjWEhERAQCIjY21cSVkKo1Gg5iYGERGRtq6lFpLpfdPLb2HRkQqYqARkTIYaLVeHHSGXyBo5NXujd9sXSjRbdnLdxGQ1egQZ/+3UYkA8AyNiBTCQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJShzLdtXLhwARs2bLB1GWSGhIQEW5dQq124cAGBgYG2LsMilPkK7ri4OFuXQWS3dDqdEl/BrUSg0d2t9PcCeAZN1sZ7aESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaRylNngAAIABJREFUESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMJ1sXQGrZt28fjhw5otd25swZAMDy5cv12kNDQ9G9e/caq43Ux0Aji0pNTcWUKVPg6OgIB4eSCwARAQA8++yzAIDi4mIUFRXhq6++slmdpCaNlB5tRBZQUFCAhg0bIjs7u8p+devWRXp6OpydnWuoMqoNeA+NLEqr1WLMmDFVBpVWq8XYsWMZZmRxDDSyuLFjxyI/P7/S6QUFBXjsscdqsCKqLXjJSRZXXFyMRo0aISUlxeh0Hx8fJCcn37rHRmQpPKLI4hwcHBAdHW30ktLZ2RkTJkxgmJFV8Kgiq6jssjM/Px9jx461QUVUG/CSk6wmJCQEp0+f1mtr2rQpzp49a5uCSHk8QyOriY6OhlarvfVvZ2dnTJo0yYYVkep4hkZWk5iYiJYtW+q1nThxAq1atbJRRaQ6nqGR1YSEhCA0NBQajQYajQahoaEMM7IqBhpZ1fjx4+Ho6AhHR0eMHz/e1uWQ4njJSVZ16dIlBAUFQURw7tw5BAYG2rokUpmYQafTCQC++OKLL6u8dDqdOdEkZn/bRvfu3TFjxgxzZ6daZMeOHdBoNOjXr1+lfRISErBkyRLExMTUYGV0N1q8eLHZ85odaIGBgYiMjDR7wVR7lAZZgwYNquy3ZMkSHlOE2NhYs+fl96GR1d0uyIgshU85iUgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZdRIoJ1d2P3W1zBrAqdjr2LLszWbrW/BRWxfPBVDujSHT1031PENQZdBk7Fo03Fki/UXP7GOpmy9b766Lzxr/QWXY71tv83o+pW+nFy9ERQ6EE9/tAcZZm9r48swug0vLEH3CnU4ITpev9vhV9oY9GmH+cfNrc8M5n7Bo+lfwHZK5nWAoPHzkmDOQk1W08uztRpe36u/yPye3lIn9HH55MdTkpGbJ1f+2i0f65qLBq3l9V9NGy4mJkbMOhwPzZVgQDBstRSYPreFWHHbG1m/4htZcv7Q/8qcHg0E0Mo9s3+WXAsvozIF60YJAAl+8UAVvdLkg96Q8E8vm1WOeflSgpecZIY8fD9bh1eO9sTSb/+NyQ+GoL6bKzybPICnV/0Dj7rYuj61aZy9ENhxJN5Z9yq6OxTgj8VvYl2mrau6OzDQyHQpX2De8rPwi5qBx/wMpnmMQfz143ijnU0qq12CuqFbAIDCgzh4xNbF3B0YaGSyjK/+gx+LHPBAzzAeQDYlKPmJI1e4utq6lrvDXXE8StpeLH3uUXRp1gDuzi7w8AlGt5Ev4PPDV/Q7FqbhYMx8THy4M0L868DFzRuB7Qdg8ns/I6349su5vmZ4pTdZS18dS+9gFq7H8PLTdOurtzLx0XC6Nd9QrDyxBa9HdkPzBu432/tgWbqJ622m4/M7ltXfcwmSSydsexJ1Stsb/g0/mDju4f/+FwIfNKmXiM9feBSdgrzh6uyG+k07Yehzy3AgswaeCJhJqWPt3D7sTwLgGYawe81cT8XYPtCS/oPorr0wdcM1jPx4N/7KTEfiD0sQnrkaE8MexCsJuWV9t81GjzHvI6Xvu9h2LBWZ53/BF5M9sX1mXzz44m5cv82iXMd+jM0zu6P1w28hIVMgIhBJw6fhboBDZ7ybKDj8SpuSzk5jEC9/YWGYF0aszYTEjane+gxfg0IpwOphAPBfvDnlC/hN/RK/nM/Epb2v4j5HM9bbTG1eOQyRrZjgYTBh4ApclT/x/+3dd3wUZf4H8M9mS0gH0oHQQj2QgEgJXUC60lKAhKrCKdhQ+elxKgp36KHigQinqCBwmnJHTvQAQbHQlaMoUgIWSkIapEBC6vf3RxJg08hushny7Of9eu0fzDz7zHd2Jh9m5pmdXdrdml4LkJiYCuAaomYNxtKkUVj5zVmkJp/EtsXBOP/BI+gbPA9f3Yl/N4rsa5KfgQtH/o3/m7IY+40tEfb2a5jobuV6qqbuRiEqGgnKli3TvQRwk/GbUsybJ74nQxwhDt2XypnSaVumi/OgtyXJrGGOxEU2EhhHy4aMKpZ35YC8Nry99Hpii1woMF/U9e0Piw8gzR79SvJumZ6/+0kJaPqofJsnFsqXDWMhgKfM+u+1CuZbuN7VUtlI21aZ7gJB3+WSaDb9V1naHQLPObLLouVkyXvDS35urNVTcqDMsNjPS7qJDpCOLxy1qFfbj3LW032tdP3KvYzSavxrsv1MZs3W89ZlcJSzpvYgLi4VcOiDB8Z4mc/yG4IhnYCiQ3HYcqFk2ph1uLZrLnzMGjZAUFA7IP8wDh+veCnXT21AZK+BeNlhAba8NQZN9ebzHYfNxyN36XBh3ev4JK10ajo+WfYBfB97Ev2N1q7fXejZ07mC6Rau9x3FGS4uOgBAo6Ej0LPMz+x0vH8M2gA4sX173ZdWpXq+r43dgHwRiOTj6oWDWB3eFL9vXoRn3z2MazVZTws56ItXqLCwsIpWhSgsBPR6fRVtbEPbQMtNQlIGgKLtmOlR9hpDa/zpfwAQj/j4kvYZR7DxxekYfFdL+DVygr6kbatnDwDIRnZFR9JXojF13EcobOaB7K3PY87Gcyh/hacD5j45Ag2yt+KNd34unnR6Dd74ahDmz25bgxV0gUvZ0z1r1vuO4oCWLQMAVPJrTj4+xSGQklKnVd2WMvuaAS5Ne+CP6zZiXuB1HFv2EJb8UGD9elrIwdUVzgAyMzOraJWO9HQHuLu7WreQGtA20Bz94NcQgGEiYvJLrzOUfaVh1b0A8BtW3N8XUxd/hUYPr8N3p1Nwvai4zfnlfXFzxKcMQzcs3rkNH/9nM569KwObHx6HRd+X3xu9I+Yjwldw9O03sCM3DzveWIG06fMR2kjr9a4pBzg4AMjLQ57Z9HSkp1vXY9d+/eAC4FJiYvmZyclIBgDfsvdzaEy1fa1BX7z01xB4SDz+/txaJFi1nlZo1w7tAFw+fvzmIFNZucdx/Ewg2rbVWbkQ62l8ytkXEyb4AAUH8M2e8pdZ41/rCX3zx7G3AEDh9/hmTzbgNw7zHx+Ett6uMJZ8Xjk5OZUvwq0tOjTVA67BePXTNRjlehiLx83Ev8r+LToOxfxHu0CXvAmvv/EGXt/kj8eeGGijHy61YL1rzB/+/gAuXoDZWcalAzhwzroenUY9jMimwNVtcdhZpvwTWz7DWTjgnnH3W1lvbYvDJENnLDmp3r7WOHQxnummR86XL+PlHaXBaYt9q/QzBBA4GmM6ANi/ERviKx7NTo5ej23eIzFSi3sR6+6iXSUXrZM+lRmBRjG0Hidv/PcnuZieI9lpZ+SbNdOlo3MzCY86X9LwnLwz2EUAPxnztx1yKuWa5Ganyamvlsu45noBPGTOjtsvL+Pr+dLRCHHpvUSOXi9TYsqHMtwJAp1OXMduEOsuaYrcHBQYLRtyKmlS7fWursoGBXLl63ktBPCR+1cekISsa5J2Zqe8GjZUOjW1ZlCgWMrWP0obg16aj31TvjqdIllXfpeDHz0iXVwgbr1ekR8s/C6O7QYFNku4vpMsPiH1c1+7zfplfT5TfAAx3L1EThaVTLR037LkMxSRy1sfkUAjxBQ4Tpb+66D8lpYt+QXXJePij7J11Szp4t5EpsQkVthTddRkUKBOAu3XZb3KjdK0X3j4xvyitB9k7fzx0rO1pzgbTeLq3UKChs2WN3f8bv4Bp+yVFXOGSecAT3E2GsXVt70MmrFMNj0XfLPv7kslvmQk5tZX3+XnRfY9LU3LTHeM2HzLAnJl+8N+AjSVx76xeGizWAXLACZKTAVNq73et3G7z1fSD8nqhwZKO393aeDUSAL7zZJ3vj9SPMpZ0r7qUauKFErK3nfkkeFdpKmHoxhMbuLXYaBMXfRvOW3FFwutCbTpLhWN/lX0uvnHWH/2ta0Vrp8+vOye9Lu81c/xZpuS0ezqrWfFy7jdZygiknboI/lT5BAJauklro4G0ZtcpFGzTtI/dL6s2ZMohRZtSXM1CTSdSIVXA6oUGhoKAIiJibH0rXe8Kx+Oht+q/vj5h+cQqHUxdiQ6Ohrh4eGwYnest7ivVawm+WKbS0T11m9Yu2Y3Rs/fyB2MbIz7mi1o/00BjaWvHYFG49ch4Woajq15CsuuzMafwmwxtEn2jvua7dl9oAFAetxMNG3UHMNXG/DiJ4twT4XHrbEIuc1383Q6HTov+qkWK6vLZWqxfvanevsaWcvuP86GD22DPFSdliGIrfPrO3W5TC3Wz75Uf18ja/EIjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUYfXTNmJjY6HT1f2vupDauE8RAISEhFj1Pqsewb1v3z6cP3/eqgWS/Vm+fDkA4KmnntK4EqovAgICEBwcbPH7rAo0IkuEhYUBKP7dACJb4jU0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZBq0LILVkZ2cjNzfXbFpeXh4A4MqVK2bTHR0d4ezsXGe1kfp0IiJaF0HqWLVqFebNm1ettm+//Tbmzp1r44rInjDQqFalpKTA398fhYWFVbbT6/VITEyEt7d3HVVG9oDX0KhWeXt7Y/DgwdDr9ZW20ev1GDJkCMOMah0DjWpdZGQkqjrwFxFERkbWYUVkL3jKSbUuKysL3t7e5QYHSplMJqSkpMDd3b2OKyPV8QiNap2bmxvGjBkDo9FYbp7BYMADDzzAMCObYKCRTURERKCgoKDc9MLCQkRERGhQEdkDnnKSTeTl5cHLywtZWVlm011dXZGamgpHR0eNKiOV8QiNbMJkMiEkJAQmk+nGNKPRiLCwMIYZ2QwDjWxmypQpN74lAAD5+fmYMmWKhhWR6njKSTZTVFQEX19fpKamAgA8PT2RlJRU5T1qRDXBIzSyGQcHB0RERMBkMsFoNCIyMpJhRjbFQCObmjx5MvLy8ni6SXWi3j1tIzQ0VOsSyEKlT9RYtmyZxpWQpWJiYrQuwSL17ggtNjYWFy5c0LoMskCLFi3QokWLKttcuHABsbGxdVQR3U593R71blBAp9MhKioKYWFhWpdC1XT8+HEAQKdOnSptEx0djfDw8Cq/A0p1p75uj3p3ykn1T1VBRlSb6t0pJxFRZRhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMqwy0Ar+CQEOp2u+NUgEp9pXVB9Itk4t+cTvDZ3HILb+cHN0RFuPi3RqV8YXtj4P1yuw4czzHDV3dyOpS8HE1wa+aJV0L0IffQv+PhQMsr/mB6pyi4DzTApFiJXsHqI1pXUQ6fexKh+k/Hm6SAsjP0fLmZcwa/7N+Dx5kexdGpv9Hv2O+TUUSnrrgrk8EIEAsDYDcgXgeRnIuHkbnz8ygT4nVyD6fe0RreZ63Aiu46KIk3ZZaDZr23FRzX93sKlmnRjGI7Xol/GmC5N4N7AGV6t+2POuhWY6pmPE2+vwH9za6teK+gbwMO3LXqPfQwrvzqKnQs64sy6mbh38jr8Xr8e7VUDtbSd6yEGGlmmw5/xU/42zGhUZrqpDdoEAMjNQMZ1LQqrSGMMeDUKr/QyIOnTJzD/kzStCyIbY6BR7Ug/iIPxgKHbIPT10LqYW+haY8680TAiE5vf2Wh3Ryz2xi4CLedkNBaMuxsBHg3g6OKNdv1nYPnuFJQ7A4mLhOHGBeYx+PDU53gprBdaeTqXTB+ENcU/MQlJ+wFr509Ar0BvuJhMcG7UHF1HzsFbuy6isKS7317vffNidbMnsfX7dzB7yB/g59YAJmcfdLj3QazYk1Ku3ur0fXJJ15t933pqse0huJZO9/ojvjarZSTWXwOw5yn4l7YxTEKc1Z+sIC8zCfF7NuKZB57Adr8JWP3R02hvdX+24d6vH7oAkP3fYXc+uJ1VJvUMAImKiqp2+4L41TK4IcSh6UhZ9sVJSc7KkIvHNsufhvWXji0hcIyQLWbvyJcNYyGAv7QcGCardp2R1Gs5krT/BblHP1BWp4hIYpxMbWUUne9geWXLj3IxI0uSTn0pyye0EoPOR8a8d1oKbvQXL4uDIHDxk9bBs+TDvb/K5avp8vv3G2ROFxeBqYPM/zrz5uIt6nurTHeBoO9ySTRbh19laXcIPOfILrPplbW3zonFQQJAAIM0GTRfNh27IkVW9hUVFSVW7Y6HF0ogIBi7QfIra5OzQUYDAvSRFQmlE7mdq2L19tBYvavYskDLkqhQdwHcZVJshvmsi/+Qex2rCjRPmfXfaxX0mS2fzfAWwE0mfnzZfNb1XTKnCQROw+SDS6UTS3Z0tJOFhwvMmhcde0k6AqILWiynrOpb20ATESnKTZfzJ3bJP/7YQxrqfeXeV76RK1b0Y9NAy/5IRlUaaNzOFamvgab4KedebNuWCSAYw4e7m89qMgiD2lX13rvQs6dzBdP3YPPmFADBGD26zJVxx0EYNcQZyNmJzduzzOe5BKNfV/NfDdfdNRz3NQHk6FZsT6xB3xrSmTzQrMMgzF79X6x6IB+7XpyM53Ze07osc4mJSAQAYxM08So7k9tZJWoHWm4KUrIANPCGt2vZmT7w8anqzS5wcamozyQkZQBo4AMft/KzfX19ABTh0qUy10waNkTDcq1La0hGcnIN+r4jeOH++3sDSMBnnx3WuhgzGbt340cAuuAB6G8sO5fbWSVqB5qjN7zdAFxPQcrVsjMv4/Jla/r0ha8HgOvJSK7gP9CkpGQADvDz8zafkZaK1HKjECU7eOkOb3HfDnBwAJCXhzyzlulIT6+oeB10uqpXryaMjo5wAHDZqg/WRorOYs2qrSiAB0LmRqDK/8Nuxe1cL6kdaOiDkSM9AOwrOfW8Repu7D5lTZ99MX68N4B9+PzzK+azcnfh8y+zAaehGD+8zH+91/fi2+/Nv4QjP27HjgRAFzQSw/2t6dsf/v4ALl6A2W/JXzqAA+cqqt0Zzs645Q/jRyxsr8Owd6sfQPufaQmPyDiUv3c2E9u37kURHNCjx93V7s+2LuPb5ydh0cEC+I9fiTdCG1vwXvvezvWW1hfxLAULRzkLz7wr9zWGODQdJcu+OCUpWZmSePxzeWX43dLEp6pBgdGyIaeSThPjZGorg+h8B8viLT9JQmaWJJ/66uYI1bsVjH55tJduQ2bI+yWjX+e+33hj9OvpcqNf1e07V76e10IAH7l/5QFJyLomaWd2yqthQ6VT04ouFl+WtSOMAo8HZN25q5K69znpYugsS36u9scp+55uIdD5y8iXY+Xgryly9Xq2pP56QP65YIB4A+La/UU5mF39/krV2qBA4XXJTIqX/XEr5bHBAWKCi9w1a72cLFcTt3NV6uugQL2r2NJAExHJPhUjC8bdLc3cTWJ0aiQte4TIS58dkZVDUHLbAcTjwa0i+56Wprg5rfg1UWIq6LMo9aC8++Q46dHKU5yMBmng0UyChs+WN788L+ZjXCU7etMn5MufN8rjwzuLv5tJjE5e0m7gLPn77uRytzpUv28RST8kqx8aKO383aWBUyMJ7DdL3vn+SPHoV8k6BP7f9zea557cJLP7B4qXi5M0DLhHIlYdkqsWfJaFGfGyY+2LMn14L/lDS19xMxnE5OotrbuPlIeWxkm8FWEmYt0f0HSXstsKAp1BnDy8pcVdA2TiI0vk40PJ5Uc/uZ1vi4FWR6wJNG3d3NH3aV3KHay+/gHdpNZ2rq/bQ/FraERkTxhoRKQMBpoNFX+vri1eOArg4t8RrNOhw5+PaF1WBWIRUvZBiRW8Oi/6SetC70j1Zzurz6B1ASpr+cx+yDNaV1EdIYgVu3lYWK2rP9tZfTxCIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJl1MunbSxfvhwxMTFal0G16MKF4p//CA0N1bgSAm5uj/pGJ1K/nhvDHb7+OXHiBACgY8eOGldClqpvBw71LtCo/gkLCwMAREdHa1wJqY7X0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZOhERrYsgdWzatAnvv/8+ioqKbkw7deoUAKB9+/Y3pjk4OODBBx9EREREnddI6mKgUa06evQounbtWq22R44cQVBQkI0rInvCQKNa16FDhxtHZZVp06YN4uPj66gishe8hka1burUqTAajZXONxqNmDlzZh1WRPaCR2hU63755Re0adMGVe1a8fHxaNOmTR1WRfaAR2hU61q3bo1u3bpBp9OVm6fT6dC9e3eGGdkEA41sYtq0adDr9eWm6/V6TJs2TYOKyB7wlJNsIjk5Gf7+/ma3bwDFt2tcvHgRfn5+GlVGKuMRGtmEj48PBgwYYHaUptfrMXDgQIYZ2QwDjWxm6tSp1ZpGVFt4ykk2k5mZCS8vL+Tn5wMovl0jOTkZDRs21LgyUhWP0Mhm3N3dMXLkSBgMBhgMBowaNYphRjbFQCObioyMRGFhIQoLC/m9TbI5g9YF2MqFCxewd+9ercuwe/n5+TCZTBAR5ObmIjo6WuuS7F6fPn3QrFkzrcuwCWWvoUVHRyM8PFzrMojuOFFRUQgLC9O6DJtQ9gitlKJ5Xa9s27YNOp0Ow4cPr3B+aGgoACAmJqYuy7JLFX17QyXKBxppb+jQoVqXQHaCgUY2ZzBwN6O6wVFOIlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0MrJxsmoZzG6ayt4Oemh0+nMX82exH6tS9RI4ZUT2LrmeUQM7oLmjZ1hcmqIJm2DMCjiJUQdvYyi23dRS7Zhhquu/Lap5DVibTp+e703t6EdYKCVcX3PnzFq8ltIGvY+DqdcQ+aZTzClWXu89GM8FgdpXZ22djwbjFGPxaBw7BvY/vMlZKadxe4PHkPzI3/DpO7dMCsuuY4qGYF1VwVyeCECAWDsBuSLQMq9UrByYPE7Wj6zHyLchqpjoJXxQ0w0fpV7MPWJwQhwbQC3wHBsOn8SizrXRu8lRxb93sIlq+Zrz2fWamx64j509HNHA2dPtO7/ED785zNoX3gO6xe8hWNaF2hz9X8bqozPdTFThPPnEwB0haen1rXceUasTUdSBdP1Qb3Rwwk4dfYMzgjQ5Y55hqAX5n0tmKd1GVRneIRmpgiFhXzCrcWupSEtB0Dnu9D5Dgmzr+f5odmTu7Uug+oYA61UXCQMOiOm/gcAPsdUp5ILyL1fx4Wq3leQgkNRSzDjvrvRxs8Vjk4N0eyuYZj99++QcstV8uKL0iOx/hqAPU/Bv/QCtWES4qoxv5Sk7Mfqxx9A95aecDY5wsU7EL0mPI31RzLKrEvpRfEx+Oi3L/GX8J5o3tAJzp4tEDTmWcSdzau1jy41Jhq70AihCx9Bu1rrtQ5xG6pDFBUVFSWWr16+bBgLAUbLhpyy8+JlcRAETZ+QfbdO3jJdHOEtI/66Q+IvX5OrKfHy5YqJ0tLBIB2e2S3m3WyV6S4Q9F0uiRUu/zbzE/4lU1oYBL6DZcnnJyQ5K1MSfvpUXhjoLWjQRRbuvVbBugRK17EzZO3ec5J+NV1+2bFQejlBjD1elXgLP50KXYqVEF+9tH7oc0mzsouQkBAJCQmx/I2HF0ogIKjk1fSJ78q8gdsQgERFRVn4rvqDR2i1QD/oJax/fijaNHKGi1cbDH5sI96a4oaTf1+K2MzaWkoOPnt+Dv75uxPGvxmFhaM6wNvVDf6d7scrn/wVQ+QYlj62AmfLve8ygh5ahQeDA+Dh4oFWQ1/AU6Mdkf/9VuxMrWFJabswf8RMfD9oPb5aMwqNa9id1SoY5dw119eiLux2GyqGgVZTY9bh2q658DGb2ABBQe2A/MM4fLy2FrQHcXGpgEMfPDDGy3yW3xAM6QQUHYrDlnLnx53Qo4fzLf92RECAD4AEJCTUoJxrB/Hn4WOx+Q/v4btNEWihr0FfWrPXbaggjnLWVMYRbHxjOT7Y/A1+vpCElPTrt9xg2hDZ2bW0nNwkJGUAwHbM9NBhZoWN4hEfD8DsN2Q94OFh3spkMgEoQpG1d8IWnME/Qu/HB02X47v14Qi4A8Ns0NuXqr72eSt73IaK4hFajfyGFff3xdTFX6HRw+vw3ekUXC8qPuU5v7wvAIH5z4LqUPXPIlYx39EPfg0BGCYiJr+im0gFImlYdW/trFnlUrBlzki8mPscvoh+EIE3/ks8gufaNMMz9e4WfHvchupioNVE4ff4Zk824DcO8x8fhLberjCW7Mw5OTkVvMEZzs4A8vJQPD71Ixa212HYu5erMb8vJkzwAQoO4Js918v1HP9aT+ibP469BbW9krfKxsFFY/Dw8Qh89p+n0NnRlsuqI3a3DdXGQKsJfW8MHeQCXIrFq8t24nRqNvJyLuP0rrewYM3RCt7QGXffbQRO78Gu89eQtu+f+OyXzhjYv3E15jfAyKVrMSMwCWtmTcabW48jIeM6ci6fxbf/mIGxryQi9PUF6GOziwiCX9eFY8zLB5F04GX0dCv7ncl7mFJlAAALQElEQVRueK381ew7n11tQzugwchqnbD4to3NEaIvN/TfVJ7eJ/Lrsl7lbglov/Bw8ftS9sqKOcOkc4CnOBuN4urbXgbNWCabngu+2b77Uvm1ZDG5JzfJ7P6B4uXiJA0D7pGIVYfk6i1l3G5+UdoPsnb+eOnZ2lOcjSZx9W4hQcNmy5s7fpf80kb7npamFdYbIxPLruPoD6XcHSoVypEPR1d+i8Stn5elLL9to+TWiLLL950rZW/UKMVtWAyK37ahEzG/QqCK6OhohIeHQ9HVU0poaCgAICYmRuNK1KfT6RAVFYWwsDCtS7EJnnISkTIYaESkDAaa3YtFSDUekth50U9aF0p0WxxPsXshiOV1RlIEj9CISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBnKP20jOjpa6xLoNi5cKP7BOW4rqinlAy08PFzrEqiauK2oppT9TQG6c5Q+v55HYGRrvIZGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEymCgEZEyGGhEpAwGGhEpg4FGRMpgoBGRMhhoRKQMBhoRKYOBRkTKYKARkTIYaESkDAYaESmDgUZEyjBoXQCp5cCBAzh69KjZtF9++QUA8O6775pN79KlC3r37l1ntZH6GGhUq5KTkzFnzhzo9Xo4OBSfAIgIAGDevHkAgKKiIhQWFuLTTz/VrE5Sk05K9zaiWpCfnw8vLy9kZmZW2c7NzQ2pqakwmUx1VBnZA15Do1plNBoxadKkKoPKaDRi8uTJDDOqdQw0qnWTJ09GXl5epfPz8/MxZcqUOqyI7AVPOanWFRUVoUmTJkhKSqpwvre3Ny5dunTjGhtRbeEeRbXOwcEBkZGRFZ5SmkwmTJ8+nWFGNsG9imyistPOvLw8TJ48WYOKyB7wlJNspk2bNjh79qzZtBYtWuC3337TpiBSHo/QyGYiIyNhNBpv/NtkMmHmzJkaVkSq4xEa2cyZM2fQtm1bs2mnTp1Cu3btNKqIVMcjNLKZNm3aoEuXLtDpdNDpdOjSpQvDjGyKgUY2NW3aNOj1euj1ekybNk3rckhxPOUkm0pISEBAQABEBOfOnUOzZs20LokUZteBFhoaitjYWK3LIKo1ISEhiImJ0boMzdj90zZ69+6Np556SusylLZz507odDoMGTKk0jbh4eF48sknERwcXIeVqWX58uVal6A5uw+0Zs2aISwsTOsylFYaZJ6enpW2CQ8PR3BwMLdFDdjzkVkpuw80sr2qgoyoNnGUk4iUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDrRYUfBJy4zHTugaR+Ezrgmwsdc3Qm+tb2Wvk+7hmswq2YYZrBct0MMGlkS9aBd2L0Ef/go8PJaPAZjXQnYiBVgsMk2IhcgWrK3/cl93p0icYLjbrfQTWXRXI4YUIBICxG5AvAsnPRMLJ3fj4lQnwO7kG0+9pjW4z1+FEts0KoTsMA42sUxoiZV+nl6Kr4yA8+vAf6r4mfQN4+LZF77GPYeVXR7FzQUecWTcT905eh9/t9rnM9oWBRhZzbHMvwvu3rGDnKcS3K1fj13FzEemnQWFmGmPAq1F4pZcBSZ8+gfmfpGldENUBBhpZzG3oQnzydL/yO0/WZqxYX4Bpj46z4emmBXStMWfeaBiRic3vbMQlreshm2OgWSHnZDQWjLsbAR4N4OjijXb9Z2D57hRUdlYjKfux+vEH0L2lJ5xNjnDxDkSvCU9j/ZGMm43iImG4cYF7DD767Uv8Jbwnmjd0grNnCwSNeRZxZ/PMO849jX+9OAX9OjSDp7MjnBq3xD33z8XyT3/ClUIraqih8x+uwH+aP4xHB9w5D0J279cPXQDI/u+wO//mdHvZJnZH7FhISIiEhIRY9J6C+NUyuCHEoelIWfbFSUnOypCLxzbLn4b1l44tIXCMkC23viHhXzKlhUHgO1iWfH5CkrMyJeGnT+WFgd6CBl1k4d5rtzTOlw1jIUCgdB07Q9buPSfpV9Pllx0LpZcTxNjjVYm/0TZbtj7kLzqPAbLoi1OSmn1drl46Jpuf6SNu8JS5u6ytwUpFR+T/2hlk8DsXrXo7AImKirLsTYcXSiAgGLtB8itrk7NBRgMC9JEVCSXTFN0m1uzPqmGgWbQDZElUqLsA7jIpNsN81sV/yL2OZQMtW7ZM9xLATcZvSjFvn/ieDHGEOHRfKmduTCz942kk07fcukNfl09CHAUYKKtvdHNInm0FQZ83JeHWfuWoPN/u1j8eS2uwTs7nM8XTLURis6x7v80CLfsjGWUWaOpuEwaaCE85LbIX27ZlAgjG8OHu5rOaDMKgdmXb70FcXCrg0AcPjPEyn+U3BEM6AUWH4rDlQtn3dUKPHs63/NsRAQE+ABKQkFA6rT2GjWgJh71LMH72G/j3/t+QVQgAXfDXU6l4e1BNa7BEKjau+BgNps3FWNea9GMDiYlIBABjEzTxAuxnm9gnBpolclOQkgWggTe8y/3h+sDHp2z7JCRlACjajpkeZW8EbY0//Q8A4hEfX7YvD3h4mE8xmUwAilBUVDrFBUNXHcKBj+ah/S/rMXdIazR090fQiIfx180ncePWK6trsMDpd7Hyi9aY/eigO+5nxDJ278aPAHTBA9DfCPvZJnaKgWYJR294uwG4noKUq2VnXsbly2Xb+8GvIQDDRMTkV3DPlghE0rDqXivr0TXGPVNfxvqdx5CYnoJjcX/BSPkCL064G0PfPFU3NaAAX69YjRMDHoEWt55Vqegs1qzaigJ4IGRuBHwAO9km9ouBZpE+GDnSA8C+klPPW6Tuxu5TZdv3xYQJPkDBAXyz53q53uJf6wl988ex16rv5+zEQw07Y8nJkn8aPdHpvll4Ne4vGKHLwb7Pv8IVm9cAIPPfWPFROsbPnQZ/K7uwjcv49vlJWHSwAP7jV+KN0MYl0+1gm9gxBppFXDHxr8twX+NMxDw5Ga/vOI3Uq1m49PN/sTjy7zha7jS0AUYuXYsZgUlYM2sy3tx6HAkZ15Fz+Sy+/ccMjH0lEaGvL0Afq8/TjmP5Hxdiy7FEZObm4VrySWz924fYK0Z0G9wPjeqghnMfrMCnrpF4dLz77RvbWlEuspLP4MB/3sbjQ7rivr+dQNtZ67Fr01QE6Eobqb9N7FrdjkHcWawdFco+FSMLxt0tzdxNYnRqJC17hMhLnx2RlUMgQPHL48GtN9oXpf0ga+ePl56tPcXZaBJX7xYSNGy2vLnj95ujc/uelqa4+X4A0n7hYRGJkYllpmP0h5IjBZJ65F/y2pzR0rNjc2nsbBLnxk2kXe+J8ux7+ySlyLzmatVgqaL/yYI2kE4vHrO2hxtg0SjnVpnuUuYzAQQ6gzh5eEuLuwbIxEeWyMeHkitdNxW3CUc5RXQiYrffcgsNDQUAxMTEaFwJ6XQ6REVFISwsTOtS6i3uzzzlJCKFMNCISBkMNCoRi5DbPbRRp0PnRT9pXShRpTiOQiVCEGu/l1NJETxCIyJlMNCISBkMNCJSBgONiJTBQCMiZTDQiEgZDDQiUgYDjYiUwUAjImUw0IhIGQw0IlIGA42IlMFAIyJl2P0Ta2NjY7Uug6jWhISE2PUTa+060Pbt24fz589rXQZRrQkICEBwcLDWZWjGrgONiNTCa2hEpAwGGhEpg4FGRMowALDfIREiUsr/A1h8A3Bp69ewAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the discriminator\n",
    "d_model = define_discriminator()\n",
    "d_model.summary()\n",
    "plot_model(d_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Defined is the generator model\n",
    "    - What is used as input to the generative model?\n",
    "\n",
    "A random noise image.\n",
    "    \n",
    "    - What is the Conv2DTranspose layer used for?\n",
    "\n",
    "The Conv2DTranspose layer is used as part of decoding data from a random noise image in order to create features so that a meaningful image can be constructed.    \n",
    "    \n",
    "    - Why is sigmoid used as the activation function?\n",
    "    \n",
    "A sigmoid activation function is used to make sure the output image pixel values are in the range between 0 and 1."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Good! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the standalone generator model\n",
    "def define_generator(latent_dim):\n",
    "\tmodel = Sequential()\n",
    "\t# foundation for 7x7 image\n",
    "\tn_nodes = 128 * 7 * 7\n",
    "\tmodel.add(Dense(n_nodes, input_dim=latent_dim))\n",
    "\tmodel.add(LeakyReLU(alpha=0.2))\n",
    "\tmodel.add(Reshape((7, 7, 128)))\n",
    "\t# upsample to 14x14\n",
    "\tmodel.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))\n",
    "\tmodel.add(LeakyReLU(alpha=0.2))\n",
    "\t# upsample to 28x28\n",
    "\tmodel.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))\n",
    "\tmodel.add(LeakyReLU(alpha=0.2))\n",
    "\tmodel.add(Conv2D(1, (7,7), activation='sigmoid', padding='same'))\n",
    "\treturn model\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_4\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_8 (Dense)             (None, 6272)              633472    \n",
      "                                                                 \n",
      " leaky_re_lu_7 (LeakyReLU)   (None, 6272)              0         \n",
      "                                                                 \n",
      " reshape_2 (Reshape)         (None, 7, 7, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose_5 (Conv2DT  (None, 14, 14, 128)      262272    \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " leaky_re_lu_8 (LeakyReLU)   (None, 14, 14, 128)       0         \n",
      "                                                                 \n",
      " conv2d_transpose_6 (Conv2DT  (None, 28, 28, 128)      262272    \n",
      " ranspose)                                                       \n",
      "                                                                 \n",
      " leaky_re_lu_9 (LeakyReLU)   (None, 28, 28, 128)       0         \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 28, 28, 1)         6273      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,164,289\n",
      "Trainable params: 1,164,289\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# size of the latent space\n",
    "latent_dim = 100\n",
    "# create the generator\n",
    "g_model = define_generator(latent_dim)\n",
    "g_model.summary()\n",
    "plot_model(g_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- We define the GAN model as a sequence of both generator and discriminator\n",
    "    - Explain the order of the sequence, and the flow of input to output, through the generator and discriminator\n",
    "\n",
    "1. The untrained generator is first used to create an image from an input of random noise. **(input noise, output fake image)**\n",
    "\n",
    "2. The untrained discriminator is then used to classify the generated images as either real or fake. **(input real and fake images, output 0 (fake) or 1 (real) prediction)**\n",
    "\n",
    "3. The discriminator loss is calculated by comparing the discriminator's predictions on real images (where output should be 1) to an array of 1s and the predictions of the fake images (where output should be 0) to an array of 0s.\n",
    "\n",
    "4. The generator loss is then calcuated by using the combined gan model. The trained discriminator is used to make prediction on outputs from the generator when the generator is given input of random noise. The predictions of the discriminator are then compared to an array of 1s to calculate the generator loss.\n",
    "\n",
    "5. The training loop proceeds with the generator taking another set of random noise images to produce fake images. **(input noise, output fake image)**\n",
    "\n",
    "6. The discriminator is then used to classify real and the generated fake images. **(input real and fake images, output 0 (fake) or 1 (real) prediction)**\n",
    "\n",
    "7. The loss is calculated for each of the models and the gradients are used to update the weights of each model.\n",
    "\n",
    "8. The training loop is repeated until the discriminator can no longer distinguish between the real and generated fake images.\n",
    "    \n",
    "    - Notice the discriminator is not trainable in the gan model. Can you think of why that might be? This may become clear when we discuss the training loop.\n",
    "\n",
    "\n",
    "\n",
    "To train the generator model the whole gan model needs to be used. This is because to calculate the generator loss (in order to train it), both the generator outputs and discriminator outputs are needed together.\n",
    "\n",
    "However we want to just get the discriminator outputs without updating its weights, so to do this we need to \"freeze\" it, or in other words make it untrainable within the combined gan model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Really nice! Very detailed and engaging "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define the combined generator and discriminator model, for updating the generator\n",
    "def define_gan(g_model, d_model):\n",
    "\t# make weights in the discriminator not trainable\n",
    "\td_model.trainable = False\n",
    "\t# connect them\n",
    "\tmodel = Sequential()\n",
    "\t# add generator\n",
    "\tmodel.add(g_model)\n",
    "\t# add the discriminator\n",
    "\tmodel.add(d_model)\n",
    "\t# compile model\n",
    "\topt = Adam(lr=0.0002, beta_1=0.5)\n",
    "\tmodel.compile(loss='binary_crossentropy', optimizer=opt)\n",
    "\treturn model\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " sequential_4 (Sequential)   (None, 28, 28, 1)         1164289   \n",
      "                                                                 \n",
      " sequential_3 (Sequential)   (None, 1)                 40705     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,204,994\n",
      "Trainable params: 1,164,289\n",
      "Non-trainable params: 40,705\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create the gan\n",
    "gan_model = define_gan(g_model, d_model)\n",
    "gan_model.summary()\n",
    "plot_model(gan_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "##### 8. Discuss the training datasets (0.5 marks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Given are real mnist datasets. \n",
    "- `load_real_samples` prepares mnist\n",
    "- `generate_real_samples` allows us to select a subset of data for barch training\n",
    "    - what is this data used for?\n",
    "    \n",
    "The real mnist digit samples will be shuffled in with the fake generated images by the generator and be used as input to the discriminator. The goal of the discriminator is to predict 1 for real images and 0 for fake images. \n",
    "\n",
    "These predictions are compared to their true values and the loss for the discriminator network is computed. This is how the discriminator is trained.\n",
    "\n",
    "The discriminator is then given just the output images from the generator and its predictions are compared to an array of 1s. This is how the loss of the generator is calculated and how the generator is trained.\n",
    "    \n",
    "    - what is the `y = ones((n_samples, 1))` varible used for?\n",
    "    \n",
    "y is set to equal a vector of 1s with the same size as the number of real samples the discriminator has to predict.\n",
    "\n",
    "This array is used later to compare the discriminator predictions of the real samples to this array of 1s. This comparison is combined with the comparison of the discriminator predictions of the fake samples to an array of 0s. These are used together to calculate the discriminator loss.\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and prepare mnist training images\n",
    "def load_real_samples():\n",
    "\t# load mnist dataset\n",
    "\t(trainX, _), (_, _) = load_data()\n",
    "\t# expand to 3d, e.g. add channels dimension\n",
    "\tX = expand_dims(trainX, axis=-1)\n",
    "\t# convert from unsigned ints to floats\n",
    "\tX = X.astype('float32')\n",
    "\t# scale from [0,255] to [0,1]\n",
    "\tX = X / 255.0\n",
    "\treturn X\n",
    " \n",
    "# select real samples\n",
    "def generate_real_samples(dataset, n_samples):\n",
    "\t# choose random instances\n",
    "\tix = randint(0, dataset.shape[0], n_samples)\n",
    "\t# retrieve selected images\n",
    "\tX = dataset[ix]\n",
    "\ty = ones((n_samples, 1))\n",
    "\treturn X, y\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "- Given are functions to generate random numbers from laten space\n",
    "    - use `generate_fake_samples` to create a single random image propegated through the generator. Discuss the result.\n",
    "    \n",
    "The result of feeding the random noise image and propagating it through the untrained generator is another image of random noise. \n",
    "\n",
    "This is because the input random noise was conv2Dtransposed using randomly initiated kernels to create random features in the image. These random features were slightly processed with the leaky relu and dropout layers but all of the pixel values were randomised again at the end by sigmoid activation function applied to each pixel value. This just creates another random image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate points in latent space as input for the generator\n",
    "def generate_latent_points(latent_dim, n_samples):\n",
    "\t# generate points in the latent space\n",
    "\tx_input = randn(latent_dim * n_samples)\n",
    "\t# reshape into a batch of inputs for the network\n",
    "\tx_input = x_input.reshape(n_samples, latent_dim)\n",
    "\treturn x_input\n",
    " \n",
    "# use the generator to generate n fake examples, with class labels\n",
    "def generate_fake_samples(g_model, latent_dim, n_samples):\n",
    "\t# generate points in latent space\n",
    "\tx_input = generate_latent_points(latent_dim, n_samples)\n",
    "\t# predict outputs\n",
    "\tX = g_model.predict(x_input)\n",
    "\t# create 'fake' class labels (0)\n",
    "\ty = zeros((n_samples, 1))\n",
    "\treturn X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The image output from the generator as a result inputting a random noise image.\n",
    "generator_output = generate_fake_samples(g_model,latent_dim,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(generator_output[0][:][:][0],cmap=\"gray_r\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Some functions for saving GAN models and output images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create and save a plot of generated images (reversed grayscale)\n",
    "def save_plot(examples, epoch, n=10):\n",
    "\t# plot images\n",
    "\tfor i in range(n * n):\n",
    "\t\t# define subplot\n",
    "\t\tpyplot.subplot(n, n, 1 + i)\n",
    "\t\t# turn off axis\n",
    "\t\tpyplot.axis('off')\n",
    "\t\t# plot raw pixel data\n",
    "\t\tpyplot.imshow(examples[i, :, :, 0], cmap='gray_r')\n",
    "\t# save plot to file\n",
    "\tfilename = 'generated_plot_e%03d.png' % (epoch+1)\n",
    "\tpyplot.savefig(filename)\n",
    "\tpyplot.close()\n",
    "# evaluate the discriminator, plot generated images, save generator model\n",
    "\n",
    "def summarize_performance(epoch, g_model, d_model, dataset, latent_dim, n_samples=100):\n",
    "\t# prepare real samples\n",
    "\tX_real, y_real = generate_real_samples(dataset, n_samples)\n",
    "\t# evaluate discriminator on real examples\n",
    "\t_, acc_real = d_model.evaluate(X_real, y_real, verbose=0)\n",
    "\t# prepare fake examples\n",
    "\tx_fake, y_fake = generate_fake_samples(g_model, latent_dim, n_samples)\n",
    "\t# evaluate discriminator on fake examples\n",
    "\t_, acc_fake = d_model.evaluate(x_fake, y_fake, verbose=0)\n",
    "\t# summarize discriminator performance\n",
    "\tprint('>Accuracy real: %.0f%%, fake: %.0f%%' % (acc_real*100, acc_fake*100))\n",
    "\t# save plot\n",
    "\tsave_plot(x_fake, epoch)\n",
    "\t# save the generator model tile file\n",
    "\tfilename = 'generator_model_%03d.h5' % (epoch + 1)\n",
    "\tg_model.save(filename) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9 Training the GANs (1 marks)\n",
    "\n",
    "The GAN training sequnce is done in batches of data per epoch. Because the generator weights cannot be updated in the standard way, this is done iteratively in a loop. \n",
    "\n",
    "\n",
    "- Discuss the lines within the `j` loop. Explain how the generator and discriminator are trained in this context. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In j loop:\n",
    "\n",
    "<ins>Line 1.</ins> \n",
    "\n",
    "Loads half_batch number of real sample digits from mnist dataset into **X_real** variable.\n",
    "\n",
    "Loads an array of 1s with size of number of real samples in **y_real** variable.\n",
    "\n",
    "<ins>Line 2.</ins> \n",
    "\n",
    "Loads half_batch number output images of the generator when the generator is input random noise images into **X_fake** variable.\n",
    "\n",
    "Loads an array of 0s with size of number of fake samples in **y_fake** variable.\n",
    "\n",
    "<ins>Line 3.</ins> \n",
    "\n",
    "Combine both real and fake images into a single set and store in **X** variable.\n",
    "\n",
    "Combine both arrays of 1s and 0s into a single array and store in **y** variable.\n",
    "\n",
    "<ins>Line 4.</ins> \n",
    "\n",
    "Train the discriminator model using the **X** image set of real and fake images.\n",
    "\n",
    "Find discriminator loss by comparing predictions of discriminator to the array **y** of true classifications.\n",
    "\n",
    "Store discriminator loss in **d_loss** variable.\n",
    "\n",
    "<ins>Line 5.</ins> \n",
    "\n",
    "Generate nbatch number of random images to use as input to the generator and store in **X_gan** variable.\n",
    "\n",
    "<ins>Line 6.</ins> \n",
    "\n",
    "Create array of 1s with size of nbatch and store in **y_gan** variable. This array of 1s will be used to be compared to the predictions of the discriminator to calculate the generator loss.\n",
    "\n",
    "\n",
    "<ins>Line 7.</ins> \n",
    "\n",
    "The generator is fed with the random noise images in **X_gan** as input. The generator outputs the fake images it generates from the noise as inputs to the discriminator. The discriminator predicts whether the fake images are real or fake. The predictions of the discriminator are compared to the array of 1s stored in **y_gan** to calculate the generator loss. All of this is done through the *gan_model* function where the discriminator is just used to predict and is not trained again.\n",
    "\n",
    "<ins>Line 8.</ins> \n",
    "\n",
    "The discriminator and generator losses are printed for each batch that is used for training at each epoch.\n",
    "\n",
    "\n",
    "<br></br>\n",
    "The discriminator and generator are trained by iteratively repeating the j loop for each epoch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Super detailed! I love to see this "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# train the generator and discriminator\n",
    "def train(g_model, d_model, gan_model, dataset, latent_dim, n_epochs, n_batch=256):\n",
    "\tbat_per_epo = int(dataset.shape[0] / n_batch)\n",
    "\thalf_batch = int(n_batch / 2)\n",
    "\t# manually enumerate epochs\n",
    "\tfor i in range(n_epochs):\n",
    "\t\t# enumerate batches over the training set\n",
    "\t\tfor j in range(bat_per_epo):\n",
    "\t\t\t# get randomly selected 'real' samples\n",
    "\t\t\tX_real, y_real = generate_real_samples(dataset, half_batch)\n",
    "\t\t\t# generate 'fake' examples\n",
    "\t\t\tX_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
    "\t\t\t# create training set for the discriminator\n",
    "\t\t\tX, y = vstack((X_real, X_fake)), vstack((y_real, y_fake))\n",
    "\t\t\t# update discriminator model weights\n",
    "\t\t\td_loss, _ = d_model.train_on_batch(X, y)\n",
    "\t\t\t# prepare points in latent space as input for the generator\n",
    "\t\t\tX_gan = generate_latent_points(latent_dim, n_batch)\n",
    "\t\t\t# create inverted labels for the fake samples\n",
    "\t\t\ty_gan = ones((n_batch, 1))\n",
    "\t\t\t# update the generator via the discriminator's error\n",
    "\t\t\tg_loss = gan_model.train_on_batch(X_gan, y_gan)\n",
    "\t\t\t# summarize loss on this batch\n",
    "\t\t\tprint('>%d, %d/%d, d=%.3f, g=%.3f' % (i+1, j+1, bat_per_epo, d_loss, g_loss))\n",
    "\t\t# evaluate the model performance, sometimes\n",
    "\t\t#if (i+1) % 2 == 0:\n",
    "\t\tsummarize_performance(i, g_model, d_model, dataset, latent_dim) # save performance every epoch\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Train the GAN model. \n",
    "    - This takes a very long time, so I would recommend aiming for 3 epochs and checking the figures that are generated (saved locally). They won't be good, but you will get some idea already that we don't have random noise\n",
    "    - Discuss the print out. How do the loss functions change over time?\n",
    "    - Generate some images with your new number generator and plot them. \n",
    "    - Feel free to try for more epoches (if you insist!). If you have a GPU or some hours to waste, 10 should start showing some good digits, and by 50 you should get some good results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Discuss the print out. How do the loss functions change over time?**\n",
    "\n",
    "The print out shows how the discriminant loss and generator loss vary over each epoch.\n",
    "\n",
    "The print out shows that the generator and the discriminator losses oscillate with the number of epochs. First the discriminator trains and gets better (d_loss goes down, g_loss goes up), then discriminator stops training and the generator begins training to get better (d_loss goes up, g_loss goes down), then generator stops training and discriminator starts training again and the cycle continues with the losses oscillating."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "g_model.load_weights(\"generator_model_003.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator_output_trained = generate_fake_samples(g_model,latent_dim,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YM888U6x3d8envbXqdYvmtv3227fk+lPV1taaO34AAEkIfgAASQh+AABJCH4AAEkIfgAASdjV24DowdQvvPBCi2dCs9hpCK1hrbWnffbZp1h///vfH/Ycd9xxxfrcuXPDni1bthTrCxcuDHs2btwYjhGzqxcAgKqqBD8AgDQEPwCAJAQ/AIAkBD8AgCQEPwCAJBznQmqOmIDWsNamj66urmJ9eHi4xTOhxHEuAABUVSX4AQCkIfgBACQh+AEAJCH4AQAk0T3ZEwAAJke0s7luZ6jdu1ObO34AAEkIfgAASQh+AABJCH4AAEkIfgAASQh+AABJdIy245OzAQBoOnf8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJLoHut/OHPmzGJ9YGCgaZMZr46OjmJ9dHS0xTMZu2jOVVVVc+fOLdZffPHFiZrOtFb3Wo+MjLRwJmPT3V1elsPDwy2eye/q7e0t1gcHB8Oedl6f45lbq3qmm3b8Xfv6+or1zZs3t3gmE6vub2BPT0+xXvd+RX+jZsyY0fAcXnjhhbAni66urnBs1qxZxfqcOXPCntWrV9dezx0/AIAkBD8AgCQEPwCAJAQ/AIAkBD8AgCQ6Rse41apuVxBMVe2409BaYzqy1qaWup2m0QkD0YkUVVVVL7300sueE/9XdMJCVW39tBV3/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJJwnAupOWICWsNag9bY2lpzxw8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgie7JngAA0F66u+N4MDw8XKx3dsb3kqIeWs8dPwCAJAQ/AIAkBD8AgCQEPwCAJAQ/AIAkBD8AgCQc5wIASb3tbW8r1u+8886w5wMf+ECxfuGFF4Y9jnNpH+74AQAkIfgBACQh+AEAJCH4AQAkIfgBACTRMTo6Ojqm/7CjY6LnAi03xo9/S1lrU9P8+fPDsYGBgWJ98+bNEzSb9mOtTZ6urq5w7Nlnny3Wh4aGwp6VK1cW64888kjYs3z58nCM5traWnPHDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnHuZCaIyZyO+yww4r1hx9+OOy55pprivVHH3007Dn66KOL9b6+vprZTS/W2uSp+5xt2LChWF+3bl3YMzg4WKwvW7Ys7Fm/fn04RnM5zgUAgKqqBD8AgDQEPwCAJAQ/AIAkBD8AgCS6J3sCWdU9NHvHHXcs1useAv/ggw8W65dffnnY8/GPf7xYHxkZCXs2bdpUrNf9PsPDw+EYTLSzzz47HPu7v/u7Yr1ut2e0Bg444ICw57rrrivWe3t7w55o5yQ0ao899gjHor/PdTtDP/nJTxbr3d0ixVTgjh8AQBKCHwBAEoIfAEASgh8AQBKCHwBAEoIfAEASHaNjfHJ2lodZN9vs2bOL9e9///thzxFHHFGs1x2z0tnZvAx/9dVXh2OrVq0q1s8///ywZ2Bg4GXPaaJ4cPz0sXTp0mL90UcfDXuauW7qPktr164t1rfbbrumXb/dWWuTZ++99w7HfvnLXxbrjz/+eNhz1113FevHHXdc2LNly5ZwjOba2lpzxw8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCU9UboK+vr5wLHpw+5/8yZ80fJ26HWjRztkZM2aEPdEuq56enrDnrLPOCsdgotWtgVtvvbVYb+bO3aqqqvXr1xfrdX8HFi9e3NQ5QCN+7/d+Lxzr7+8v1jdu3Bj2/PrXvy7W/+Iv/iLsueGGG4r1DRs2hD1MDHf8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkugYHeOTs7M8zLrOkiVLivW6YxwuvfTSYn377bcPe6IHan/hC18IezZv3lysf/3rXw975s6dW6z/6le/CnumGw+On1q6u+MTqKJjKeqOc4le61NPPTXs+epXv1qsDw0NhT1Ya60QfdYPOeSQsCc6ZuWpp54Ke7bddtti/emnnw57dtttt2L9z/7sz8Kev/mbv2l4bu95z3uK9bpjyiLt+Jkdi63N2x0/AIAkBD8AgCQEPwCAJAQ/AIAkBD8AgCTiLXJJLViwIBy7+uqri/Vdd9017Il2Wc2bNy/siXbkHHzwwWHP2972tmL9zW9+c9gT/T4w2ebMmVOsP/fcc2FPV1dXw9eJ1tqjjz4a9ti9S7uKdikPDg6GPdddd12xvnDhwrDnoYceKtaPOOKIsCfakX/TTTeFPc20fv36cGybbbZpyRzahTt+AABJCH4AAEkIfgAASQh+AABJCH4AAEkIfgAASXSMjvEpxNPtYdaRgw46KBy74447ivW6rfIzZswo1jdu3Bj2zJ07t1hfunRp2BMdG/PYY4+FPdFDq+t+n+mmHR/CnWWt1Yk+g+N50Pp41B0NMzIy0pI5TDfW2sSLPrfR8UhVVVXvfve7i/Xe3t6w5zOf+Uyxvnjx4rBnsl/rzZs3h2Ovec1rivU1a9ZM1HQm1NbWmjt+AABJCH4AAEkIfgAASQh+AABJCH4AAEmUn5qcQPTA6EsuuSTsiXZMzZo1K+yJdtdEO3frnH/++eHYmWeeWaxfdtllYc+nPvWpYj3Trl4mz8UXXxyONXP3bt0u3Geffbbhnki0s368Pw9K6nbHRp+zgYGBsOeGG24o1rfffvuw54orrijWP/nJT4Y9rdrVe+GFFxbrp556akuuPxW44wcAkITgBwCQhOAHAJCE4AcAkITgBwCQhOAHAJBE2uNcFixYUKzXPZw92o5ed1RD1LNx48awJ3qg9kUXXRT2/MEf/EGxvnz58rDnwAMPLNZvv/32sAcadfTRRxfrJ510Ukuu/6UvfSkce+ihh5p2HUe20ArREWF1+vv7w7HVq1cX60899VTY8/zzzxfrdcfGnHHGGcV63fr82te+Vqw/8cQTYQ9b544fAEASgh8AQBKCHwBAEoIfAEASgh8AQBLTeldv3Q7df/7nfy7W99xzz4avU/fw6Wg3Vd0D3detW1es77777mFP9NDsz3zmM2HP5z//+XAMGnHWWWeFY9FuvvEYHh4OxxYvXlysr127tmnXr9PT09PwWN1uS7uEaZa676jxfM6efPLJYn3FihVhz5o1a4r1b3zjG2GP3bsTwx0/AIAkBD8AgCQEPwCAJAQ/AIAkBD8AgCQEPwCAJDpGx/jE57rt4O1q3rx54dgzzzxTrPf29jZ8nbrt8NHxE/fff3/YEx0ps2TJkrAnmvfg4GDYc/rppxfrn/3sZ8Oe6WY8DzyfaO281qLPc92cm/n7nHTSSeHYpZde2rTrNFv0N6LuyKl2/Gy+HO34+7TzWmuV6DWoe7+6u8snwdV9f/785z8v1vfdd9+a2TEeW1tr7vgBACQh+AEAJCH4AQAkIfgBACQh+AEAJFHemjPFvOIVryjWH3jggbCn7oHqjRoYGAjHrrzyymJ9y5YtYc+NN95YrM+fPz/sefbZZ4v1uodc33vvvcV63U63dtyZR+scfvjhxfr5558f9jz88MPFen9/f9jz/ve/v7GJtYHxrI26EwEuv/zyYv0Tn/hE2PPSSy81PAfaU/R3eNasWWHP5s2bG77OeD630fdX3S71b37zm8X6zJkzw57ou3XGjBlhT93fFf43d/wAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACS6Bgd417udn6Y9UMPPVSs77jjjmHP3Llzi/XoIfRVFR+98I53vCPsueuuu4r1F198MexZvHhxsf7000+HPfPmzSvWX/nKV4Y999xzT7He19cX9kw37Xg8TTuvNarqqquuKtaPPfbYllx/qn4+rLXGRN8DdceHbdiwoViv+15rleXLlxfr11xzTdjz85//vFjv7o5Pootet3Z+r5tta2vNHT8AgCQEPwCAJAQ/AIAkBD8AgCQEPwCAJOKtMW1m2bJl4djSpUuL9bqdP9Eup6GhobDn9ttvL9ZXrVoV9qxbt65Y7+npabhnhx12CHvWrl1brN9///1hz09/+tNive5B2+2wOwwm2rbbbhuOtWr3bqbd9VnV7TQ94YQTivXXv/71Yc9HPvKRYj36Tqmqqurv7w/HmumKK64o1o888siw58knnyzW999//4avX7fTtbOzfA8sqlfV1P4udMcPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgiSlznMuBBx4YjkVHo9Rtt462aT/88MNhz+DgYLH+29/+NuyJ1B0bEz3M+rLLLgt7om3vIyMjYc+b3vSmYn0qb1OHZoiObmql3XbbrVj/xS9+0eKZMFHqjnM588wzi/UVK1aEPWeccUax/vGPf7zhOdQdfzIeixYtKtajo8iqanzHtoxH9D1Zd6TS5s2bJ2o6E84dPwCAJAQ/AIAkBD8AgCQEPwCAJAQ/AIAkpsyu3vvvvz8cW7NmTbHe1dUV9px33nnF+j333BP23HvvvcX6eHY/zZ49Oxzba6+9ivWvfe1rYc+Xv/zlYv3v//7vw563vOUt4Rhk0Oydi42q29XJ9NfdHX8Fr169uljfaaedwp558+YV63fddVfYc8455xTrP/7xj8Oe5557rliPTtioqqr66Ec/Wqzvu+++YU+0Ppu9bqL3YbqecOGOHwBAEoIfAEASgh8AQBKCHwBAEoIfAEASgh8AQBJT5jiXugeT77333sV63cOfo23irTreYePGjeHYzJkzi/Vjjz027Jk7d26xfv755zc2MZhmjjzyyJZc5+mnnw7HlixZ0pI5MLUMDg6GYyMjI8X6rbfeGvaccMIJxfqGDRvCnu9+97vF+hFHHBH2rFy5slh/4YUXwp758+cX629/+9vDnuhImxkzZoQ9nZ3l+1l1x7tFr/V05Y4fAEASgh8AQBKCHwBAEoIfAEASgh8AQBIdo2Pcxuph4q3T29tbrA8NDYU9k/2w+amqHV83ay1Wtzv2vvvua7hnPF7zmtcU648//nhTrzPdWGsTL/p9Tj/99LDn+eefL9Z32WWXsOfTn/50sV63c3bWrFnF+ubNm8OeyEMPPRSOnXzyycX6zTff3PB1pqqtrTV3/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJJwnEsbaua2d+o5YqI9XX311cV63QPde3p6mnb96GH3VVVVV1xxRdOuk4m1Nnm6u7vDsT//8z8v1m+55ZawZ+PGjcX68PBw2BMd9VLXM573p7OzfD+r7jrTjeNcAACoqkrwAwBIQ/ADAEhC8AMASELwAwBIwq5eUrPTsD1Fr8Hv//7vhz0f/vCHi/W+vr6w56STTirW161bF0+OcbHWoDXs6gUAoKoqwQ8AIA3BDwAgCcEPACAJwQ8AIAnBDwAgCce5kJojJqA1rDVoDce5AABQVZXgBwCQhuAHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITgBwCQhOAHAJBEx2g7PjkbAICmc8cPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIInusf6Hvb29xfrQ0FDTJtPuOjo6ivXR0dGwp7OznK1nzJgR9my33XbF+lNPPRX2RHMYGRkJe6abrq6uYr27O/6Y9/f3T9R0xq2np6dY37JlS4tnMrGitVFVVTV37txifWBgIOx51ateVay/4Q1vCHs+8IEPFOuHH3542DM8PFyst+NnaaJE793MmTPDno0bN07UdMatr6+vWK97L+v+3jeqbg1Er+WmTZsa/nnt8D0Qza3uNYhyx7bbbhv2RN/Tdd+f0Zqeqrb2GXXHDwAgCcEPACAJwQ8AIAnBDwAgiY7RMf5L1egfTDI+df+gtR3+IW4WzfyH2s1ircWijS9VFa+b5cuXhz1f/epXi/VMGzVaxVqjWV7xileEY88//3yxXve9anMHAADTkuAHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITjXEjNERPQGtYatIbjXAAAqKpK8AMASEPwAwBIQvADAEhC8AMASKJ7sicAMJE6O+P/v617cDswubq6uor1z372s2HP9ddfX6yvXLky7BkeHm5sYlOcO34AAEkIfgAASQh+AABJCH4AAEkIfgAASQh+AABJdIyO8cnZHmbdOjNmzCjWBwYGWjyT6c+D49tTb29vsT40NBT27LXXXsX6qlWrmjInXh5rjUb927/9W7G+4447hj277757sR79Tamq9vxsvhxb+33c8QMASELwAwBIQvADAEhC8AMASELwAwBIwq7eJuju7g7Hoh1G3//+98OeG2+8sVg/7bTTGpsYW9WOu7mstap67WtfW6wfcsghYc/g4GCxXreb74orrmhsYoybtZbbgw8+WKzvscceYU9XV1fTrn/llVeGY+9973ubdp12YFcvAABVVQl+AABpCH4AAEkIfgAASQh+AABJCH4AAEk4zqUBs2bNKtZ32223sOeuu+4q1l988cWwJzoeZuHChTWzYzwcMdGeTj311GL9kUceCXvOOeecYr3uCJhNmzY1NjHGzVqb/l73uteFYytWrGjhTH7XTjvtFI799re/beFMJp7jXAAAqKpK8AMASEPwAwBIQvADAEhC8AMASKK8fZSiT3/608X68uXLw55oh+52220X9lx22WUNzQumm6uvvrpYf9e73hX2ROtwl112CXtWrVrV0Lwgi76+vnDs/vvvL9aXLVs2UdMZs5tuuqlYn247d18Od/wAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACS6Bgd45OzPcy6qrZs2VKs1702nZ3lbD00NBT2PPDAA8X6/vvvH0+OcfHg+Pb0xje+sVhfsGBB2POhD32oWD/uuOPCnhdffLGxiTFu1lp7OuWUU4r18847L+yZM2fORE1nTF566aVw7JBDDinWo+/V6Whra80dPwCAJAQ/AIAkBD8AgCQEPwCAJAQ/AIAkuid7Au3m1FNPDce6uroa/nnR7pq6XTc//elPi/W6HWjtuGMOxmuPPfYo1v/qr/4q7Il2/N59991hz3vf+95i/b777gt7rDWmmt7e3nDsH/7hH4r1np6eiZrO/2fdunXhWN0ufsbPHT8AgCQEPwCAJAQ/AIAkBD8AgCQEPwCAJAQ/AIAkOkbHeDbBdHuY9TbbbFOsr1y5MuzZZZddGr5O9PLWHTExODhYrB966KENX7/OF7/4xWL9E5/4RFOv087a8WiO6bbWxuOXv/xlsf7KV74y7Jk3b16xPjIyEvZERzR97GMfC3u23XbbYv2xxx4Le77xjW8U63XHbEw31trked3rXheOrVixolgfz/u1Zs2acOyNb3xjsf7EE080fB3qbe29c8cPACAJwQ8AIAnBDwAgCcEPACAJwQ8AIIlpvat3u+22C8fuueeeYv3Vr3512BO9BnW7Bq+55ppifXh4OOz5wQ9+UKzvtNNOYc+ll15arG/atCns2bhxY0PXr6qqOv7448OxqchOw8kzZ86ccGz16tXFel9fX9gTvW7Nfj23bNlSrHd3d4c90efsqquuCnve+973NjaxNmetTbwlS5YU6z/+8Y/Dnl133bVYr/teW7t2bbF+ySWXhD1nnXVWOEZz2dULAEBVVYIfAEAagh8AQBKCHwBAEoIfAEASgh8AQBLx+QNTyL777lusn3vuuWFP3bEtjXrkkUfCsei4hug4maqqqssvv7xYr9sq//TTTxfrdccVRMdp1L1u0Cz77bdfODZ79uyGf16rjuaoO7YlEs3tpJNOernTIZm6z/npp59erM+dO7fh66xfvz4ci77zzjnnnIavQ+u54wcAkITgBwCQhOAHAJCE4AcAkITgBwCQxLTY1XvssccW63/0R38U9kQPWq97MPVvf/vbYv20004Le2677bZi/dBDDw17dtppp2L9uuuuC3ua6cknn2zJdZg+urq6wrGjjjqqWD///PPDnmjn4tYePt4Kw8PDxXrda7B06dJi/aWXXmrKnMijblfvv/7rvxbrb3rTm8Ke7bffvlifOXNm2LPPPvsU6ytXrgx7DjzwwGJ948aNYc9k6+xs/N5YXYZoF+74AQAkIfgBACQh+AEAJCH4AQAkIfgBACQh+AEAJNExOsbzEVr1APTx+Md//Mdive7B1H19fcV63RETv/nNb4r1devWxZML1L2e//Ef/1Gsn3DCCWHPzTffXKyvWLEi7Ln44ouL9VYdG9MO2uF4kP+pndfaokWLivXddtst7LnmmmuK9e7u+DSpbbfdtljftGlT2BMdp3LfffeFPfvtt1+xHh1xUVVVtWHDhnCMmLU28WbNmlWsf/CDHwx7LrjggmK97v2K1m5dT/RaR39Tqqqqnn/++YavM2fOnGK9bt0uWbKk4bn94Ac/KNZ33XXXsKdVtrbW3PEDAEhC8AMASELwAwBIQvADAEhC8AMASCLeVtdm6h6WHO3Wqdtp+JGPfKRYf+aZZ8KezZs3h2ONOuCAA8Kx6IHud955Z9jz2GOPFeuHHnpo2PPHf/zH4Rh51e22ffvb316sL1++POxZs2ZNsV63o3JwcLBY/853vhP23HbbbcX6woULw56//Mu/LNanwoPW4X8aGhoq1qMd71UVf6/NnDkz7Il2jY5nl/Szzz4bjp1++unF+llnnRX2vPjii8X6D3/4w7DnzDPPLNbvueeesCfaQT0VuOMHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITgBwCQxJQ5zqXueIXFixcX65/61KfCnujYlrqHwDfT448/Ho719PQU69FW/aqKj4B517ve1dC8IHrIeVVV1UUXXVSs33vvvWHPzjvvXKxHDzmvqqpasGBBsV53jEN0DE3dw9mj445a9XcAmin6nlyxYkVTrzOeY1vG4/zzz2+4Z9GiRcX6PvvsE/asWrWqWL/iiivCnug1iI66aSfu+AEAJCH4AQAkIfgBACQh+AEAJCH4AQAk0TE6xi0ordrFE+nsjDPqOeecU6zX7QQ+77zzivX+/v6wZzy7daLXbe3atWHP/PnzG75OtEv41ltvDXtOPPHEhq8z3bTjDqzJXmvjUbc+69ZhK0S7/Kqqqh577LFi/YEHHgh77r777mL9lFNOCXuinc3R6QLTkbU28V796lcX69/61rfCngMOOKBYr1vT0evW7NfzN7/5TbG+ZMmSsOfb3/52sf7BD34w7Jnsv1HNtrW15o4fAEASgh8AQBKCHwBAEoIfAEASgh8AQBKCHwBAEuWnmrehuu3Wy5YtK9brHrAcPdC92UcORNvbjz/++LDn2muvLda/973vhT0f+9jHivU1a9aEPdAs7Xwcwvr168OxuXPnFusHH3xw2FM3FonW4XQ7ToSJ19PTE45F3wM77bRT2BN9Bjdt2hT2RN+fzz33XNizcOHCYr3uuKVoDu14NNBU4o4fAEASgh8AQBKCHwBAEoIfAEASgh8AQBIdo2PcHtPOu8/22muvYn3//fcPe6688spive73HB4eLtajHU5VVVXbbLNNsb569eqwp6urq1hft25d2LNgwYJwjFg77g5r57U2FV111VXh2LHHHtvCmfyu559/PhzbbrvtWjiTiWetNSb6HrjsssvCnqOOOqpYj3avV1VVdXaW7//UvV8DAwPFejTnqop31+++++5hT936ILa1teaOHwBAEoIfAEASgh8AQBKCHwBAEoIfAEASgh8AQBLxOSRTyG677Vas33TTTWHP7Nmzi/UNGzaEPQcccECxvmrVqrDn3//934v16GiYqoq3xI/n4fCQ3axZsyZ7CqE1a9ZM9hRoUx/4wAeK9X322SfsiY4Pa/axNTNnzizW165dG/Zsu+22xbojW1rPHT8AgCQEPwCAJAQ/AIAkBD8AgCQEPwCAJKbFrt7rrruuWK97MPXg4GCxHj3kuqqq6otf/GKxPn/+/LAn2j08nl1Wd9xxRzi2aNGihn8eTDUzZswIx6L1+c53vnOipjNmO++8c7H+3//93y2eCVPFCy+8UKxHn6WqqqqRkZFiPTopYryi7885c+aEPc8++2yx3tkZ33+Kfh9eHnf8AACSEPwAAJIQ/AAAkhD8AACSEPwAAJIQ/AAAkpgWx7lEW77Xr18f9kRHvWzcuDHs2XHHHYv1/v7+sKeZD8c++OCDm/azoJ2deOKJxfo999wT9px88snF+ujoaNgTrc8jjzwy7Ln22muL9eHh4bAHGhUdf/KOd7wj7PnRj35UrNd9Ry1YsKBYX7lyZdizePHiYr3uOLSTTjqpWHdkS+u54wcAkITgBwCQhOAHAJCE4AcAkITgBwCQRMdo3Za3//c/bOLu1Hb27W9/Oxw75phjivW6h0xH6l7PaJdTtBO5qqpq06ZNDc+B+h2fkyXLWrvhhhvCsSOOOKIlc4h2Tka7Fhk/a23yNPv3bMf3kv9ra++PO34AAEkIfgAASQh+AABJCH4AAEkIfgAASQh+AABJdE/2BNrN6tWrw7Gurq6mXWc8D453ZAtT0c4771ysv/Wtb23J9aMjW6qqqpYuXdqSOcBkcvwK/y93/AAAkhD8AACSEPwAAJIQ/AAAkhD8AACS6Bgd43YfD7Ouqr322qtYX79+fdjz5JNPNjwHO7Bapx1f6yxrbffddw/HfvaznxXrZ599dtjzla985WXPiYljrUFrbG2tueMHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITgBwCQhONcSM0RE9Aa1hq0huNcAACoqkrwAwBIQ/ADAEhC8AMASELwAwBIQvADAEhC8AMASELwAwBIQvADAEhC8AMASELwAwBIQvADAEiiY7Qdn5wNAEDTueMHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITgBwCQhOAHAJCE4AcAkITgBwCQxP8CNjppo12Ii7UAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "\n",
    "for i in range(9):\n",
    "    plt.subplot(3,3,i+1)\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(generator_output_trained[0][:][:][i],cmap=plt.cm.binary_r)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">1, 1/234, d=0.691, g=0.722\n",
      ">1, 2/234, d=0.681, g=0.741\n",
      ">1, 3/234, d=0.671, g=0.758\n",
      ">1, 4/234, d=0.664, g=0.779\n",
      ">1, 5/234, d=0.660, g=0.792\n",
      ">1, 6/234, d=0.652, g=0.810\n",
      ">1, 7/234, d=0.642, g=0.827\n",
      ">1, 8/234, d=0.637, g=0.847\n",
      ">1, 9/234, d=0.635, g=0.844\n",
      ">1, 10/234, d=0.633, g=0.839\n",
      ">1, 11/234, d=0.640, g=0.823\n",
      ">1, 12/234, d=0.642, g=0.798\n",
      ">1, 13/234, d=0.642, g=0.771\n",
      ">1, 14/234, d=0.647, g=0.751\n",
      ">1, 15/234, d=0.645, g=0.734\n",
      ">1, 16/234, d=0.642, g=0.721\n",
      ">1, 17/234, d=0.635, g=0.714\n",
      ">1, 18/234, d=0.629, g=0.708\n",
      ">1, 19/234, d=0.625, g=0.704\n",
      ">1, 20/234, d=0.619, g=0.703\n",
      ">1, 21/234, d=0.609, g=0.701\n",
      ">1, 22/234, d=0.601, g=0.699\n",
      ">1, 23/234, d=0.591, g=0.699\n",
      ">1, 24/234, d=0.584, g=0.699\n",
      ">1, 25/234, d=0.568, g=0.699\n",
      ">1, 26/234, d=0.561, g=0.700\n",
      ">1, 27/234, d=0.557, g=0.701\n",
      ">1, 28/234, d=0.544, g=0.701\n",
      ">1, 29/234, d=0.529, g=0.702\n",
      ">1, 30/234, d=0.521, g=0.704\n",
      ">1, 31/234, d=0.512, g=0.705\n",
      ">1, 32/234, d=0.503, g=0.706\n",
      ">1, 33/234, d=0.485, g=0.707\n",
      ">1, 34/234, d=0.482, g=0.709\n",
      ">1, 35/234, d=0.472, g=0.711\n",
      ">1, 36/234, d=0.457, g=0.712\n",
      ">1, 37/234, d=0.453, g=0.714\n",
      ">1, 38/234, d=0.443, g=0.716\n",
      ">1, 39/234, d=0.434, g=0.719\n",
      ">1, 40/234, d=0.423, g=0.721\n",
      ">1, 41/234, d=0.418, g=0.724\n",
      ">1, 42/234, d=0.412, g=0.726\n",
      ">1, 43/234, d=0.403, g=0.729\n",
      ">1, 44/234, d=0.403, g=0.732\n",
      ">1, 45/234, d=0.396, g=0.736\n",
      ">1, 46/234, d=0.390, g=0.739\n",
      ">1, 47/234, d=0.383, g=0.744\n",
      ">1, 48/234, d=0.371, g=0.748\n",
      ">1, 49/234, d=0.372, g=0.754\n",
      ">1, 50/234, d=0.368, g=0.760\n",
      ">1, 51/234, d=0.360, g=0.766\n",
      ">1, 52/234, d=0.352, g=0.772\n",
      ">1, 53/234, d=0.351, g=0.780\n",
      ">1, 54/234, d=0.347, g=0.788\n",
      ">1, 55/234, d=0.339, g=0.799\n",
      ">1, 56/234, d=0.339, g=0.808\n",
      ">1, 57/234, d=0.328, g=0.819\n",
      ">1, 58/234, d=0.319, g=0.832\n",
      ">1, 59/234, d=0.315, g=0.844\n",
      ">1, 60/234, d=0.306, g=0.860\n",
      ">1, 61/234, d=0.306, g=0.875\n",
      ">1, 62/234, d=0.298, g=0.892\n",
      ">1, 63/234, d=0.289, g=0.911\n",
      ">1, 64/234, d=0.285, g=0.930\n",
      ">1, 65/234, d=0.271, g=0.951\n",
      ">1, 66/234, d=0.270, g=0.976\n",
      ">1, 67/234, d=0.258, g=1.001\n",
      ">1, 68/234, d=0.252, g=1.029\n",
      ">1, 69/234, d=0.243, g=1.057\n",
      ">1, 70/234, d=0.236, g=1.088\n",
      ">1, 71/234, d=0.226, g=1.120\n",
      ">1, 72/234, d=0.221, g=1.156\n",
      ">1, 73/234, d=0.209, g=1.191\n",
      ">1, 74/234, d=0.199, g=1.233\n",
      ">1, 75/234, d=0.191, g=1.276\n",
      ">1, 76/234, d=0.184, g=1.312\n",
      ">1, 77/234, d=0.176, g=1.364\n",
      ">1, 78/234, d=0.166, g=1.400\n",
      ">1, 79/234, d=0.157, g=1.460\n",
      ">1, 80/234, d=0.150, g=1.509\n",
      ">1, 81/234, d=0.143, g=1.556\n",
      ">1, 82/234, d=0.139, g=1.606\n",
      ">1, 83/234, d=0.126, g=1.666\n",
      ">1, 84/234, d=0.128, g=1.708\n",
      ">1, 85/234, d=0.118, g=1.765\n",
      ">1, 86/234, d=0.113, g=1.826\n",
      ">1, 87/234, d=0.108, g=1.864\n",
      ">1, 88/234, d=0.095, g=1.934\n",
      ">1, 89/234, d=0.095, g=1.986\n",
      ">1, 90/234, d=0.090, g=2.041\n",
      ">1, 91/234, d=0.087, g=2.092\n",
      ">1, 92/234, d=0.078, g=2.141\n",
      ">1, 93/234, d=0.081, g=2.181\n",
      ">1, 94/234, d=0.077, g=2.165\n",
      ">1, 95/234, d=0.140, g=1.702\n",
      ">1, 96/234, d=1.817, g=0.091\n",
      ">1, 97/234, d=3.223, g=0.013\n",
      ">1, 98/234, d=2.875, g=0.032\n",
      ">1, 99/234, d=2.076, g=0.139\n",
      ">1, 100/234, d=1.345, g=0.516\n",
      ">1, 101/234, d=0.823, g=1.205\n",
      ">1, 102/234, d=0.607, g=1.940\n",
      ">1, 103/234, d=0.492, g=2.317\n",
      ">1, 104/234, d=0.480, g=2.489\n",
      ">1, 105/234, d=0.441, g=2.348\n",
      ">1, 106/234, d=0.404, g=2.142\n",
      ">1, 107/234, d=0.399, g=1.871\n",
      ">1, 108/234, d=0.405, g=1.634\n",
      ">1, 109/234, d=0.381, g=1.509\n",
      ">1, 110/234, d=0.412, g=1.416\n",
      ">1, 111/234, d=0.411, g=1.479\n",
      ">1, 112/234, d=0.414, g=1.408\n",
      ">1, 113/234, d=0.404, g=1.427\n",
      ">1, 114/234, d=0.425, g=1.472\n",
      ">1, 115/234, d=0.433, g=1.415\n",
      ">1, 116/234, d=0.436, g=1.389\n",
      ">1, 117/234, d=0.432, g=1.343\n",
      ">1, 118/234, d=0.437, g=1.381\n",
      ">1, 119/234, d=0.487, g=1.343\n",
      ">1, 120/234, d=0.445, g=1.309\n",
      ">1, 121/234, d=0.480, g=1.340\n",
      ">1, 122/234, d=0.482, g=1.391\n",
      ">1, 123/234, d=0.458, g=1.334\n",
      ">1, 124/234, d=0.445, g=1.378\n",
      ">1, 125/234, d=0.450, g=1.382\n",
      ">1, 126/234, d=0.445, g=1.375\n",
      ">1, 127/234, d=0.428, g=1.368\n",
      ">1, 128/234, d=0.429, g=1.425\n",
      ">1, 129/234, d=0.429, g=1.369\n",
      ">1, 130/234, d=0.430, g=1.414\n",
      ">1, 131/234, d=0.466, g=1.322\n",
      ">1, 132/234, d=0.569, g=1.008\n",
      ">1, 133/234, d=0.797, g=0.726\n",
      ">1, 134/234, d=0.788, g=0.854\n",
      ">1, 135/234, d=0.771, g=0.860\n",
      ">1, 136/234, d=0.751, g=0.834\n",
      ">1, 137/234, d=0.786, g=0.791\n",
      ">1, 138/234, d=0.784, g=0.709\n",
      ">1, 139/234, d=0.848, g=0.663\n",
      ">1, 140/234, d=0.853, g=0.650\n",
      ">1, 141/234, d=0.872, g=0.714\n",
      ">1, 142/234, d=0.853, g=0.703\n",
      ">1, 143/234, d=0.845, g=0.738\n",
      ">1, 144/234, d=0.844, g=0.729\n",
      ">1, 145/234, d=0.813, g=0.724\n",
      ">1, 146/234, d=0.806, g=0.736\n",
      ">1, 147/234, d=0.798, g=0.729\n",
      ">1, 148/234, d=0.831, g=0.753\n",
      ">1, 149/234, d=0.810, g=0.805\n",
      ">1, 150/234, d=0.828, g=0.809\n",
      ">1, 151/234, d=0.786, g=0.830\n",
      ">1, 152/234, d=0.782, g=0.848\n",
      ">1, 153/234, d=0.791, g=0.865\n",
      ">1, 154/234, d=0.798, g=0.854\n",
      ">1, 155/234, d=0.787, g=0.854\n",
      ">1, 156/234, d=0.758, g=0.869\n",
      ">1, 157/234, d=0.771, g=0.836\n",
      ">1, 158/234, d=0.783, g=0.836\n",
      ">1, 159/234, d=0.765, g=0.825\n",
      ">1, 160/234, d=0.776, g=0.845\n",
      ">1, 161/234, d=0.766, g=0.864\n",
      ">1, 162/234, d=0.756, g=0.869\n",
      ">1, 163/234, d=0.732, g=0.905\n",
      ">1, 164/234, d=0.732, g=0.869\n",
      ">1, 165/234, d=0.733, g=0.864\n",
      ">1, 166/234, d=0.744, g=0.870\n",
      ">1, 167/234, d=0.750, g=0.860\n",
      ">1, 168/234, d=0.735, g=0.861\n",
      ">1, 169/234, d=0.752, g=0.857\n",
      ">1, 170/234, d=0.751, g=0.887\n",
      ">1, 171/234, d=0.746, g=0.875\n",
      ">1, 172/234, d=0.740, g=0.905\n",
      ">1, 173/234, d=0.753, g=0.903\n",
      ">1, 174/234, d=0.739, g=0.904\n",
      ">1, 175/234, d=0.751, g=0.927\n",
      ">1, 176/234, d=0.777, g=0.953\n",
      ">1, 177/234, d=0.754, g=0.978\n",
      ">1, 178/234, d=0.743, g=0.975\n",
      ">1, 179/234, d=0.735, g=0.983\n",
      ">1, 180/234, d=0.749, g=0.960\n",
      ">1, 181/234, d=0.700, g=0.959\n",
      ">1, 182/234, d=0.709, g=0.903\n",
      ">1, 183/234, d=0.706, g=0.906\n",
      ">1, 184/234, d=0.719, g=0.939\n",
      ">1, 185/234, d=0.703, g=0.924\n",
      ">1, 186/234, d=0.699, g=0.938\n",
      ">1, 187/234, d=0.681, g=0.934\n",
      ">1, 188/234, d=0.700, g=0.929\n",
      ">1, 189/234, d=0.696, g=0.948\n",
      ">1, 190/234, d=0.679, g=0.935\n",
      ">1, 191/234, d=0.679, g=0.917\n",
      ">1, 192/234, d=0.712, g=0.907\n",
      ">1, 193/234, d=0.682, g=0.882\n",
      ">1, 194/234, d=0.667, g=0.848\n",
      ">1, 195/234, d=0.687, g=0.842\n",
      ">1, 196/234, d=0.682, g=0.825\n",
      ">1, 197/234, d=0.683, g=0.814\n",
      ">1, 198/234, d=0.670, g=0.785\n",
      ">1, 199/234, d=0.680, g=0.779\n",
      ">1, 200/234, d=0.666, g=0.772\n",
      ">1, 201/234, d=0.670, g=0.745\n",
      ">1, 202/234, d=0.663, g=0.732\n",
      ">1, 203/234, d=0.662, g=0.734\n",
      ">1, 204/234, d=0.651, g=0.727\n",
      ">1, 205/234, d=0.636, g=0.744\n",
      ">1, 206/234, d=0.637, g=0.753\n",
      ">1, 207/234, d=0.616, g=0.762\n",
      ">1, 208/234, d=0.620, g=0.787\n",
      ">1, 209/234, d=0.603, g=0.818\n",
      ">1, 210/234, d=0.597, g=0.821\n",
      ">1, 211/234, d=0.576, g=0.828\n",
      ">1, 212/234, d=0.579, g=0.851\n",
      ">1, 213/234, d=0.580, g=0.856\n",
      ">1, 214/234, d=0.578, g=0.840\n",
      ">1, 215/234, d=0.564, g=0.838\n",
      ">1, 216/234, d=0.571, g=0.822\n",
      ">1, 217/234, d=0.573, g=0.803\n",
      ">1, 218/234, d=0.574, g=0.800\n",
      ">1, 219/234, d=0.569, g=0.770\n",
      ">1, 220/234, d=0.580, g=0.795\n",
      ">1, 221/234, d=0.599, g=0.768\n",
      ">1, 222/234, d=0.608, g=0.781\n",
      ">1, 223/234, d=0.610, g=0.765\n",
      ">1, 224/234, d=0.610, g=0.762\n",
      ">1, 225/234, d=0.619, g=0.763\n",
      ">1, 226/234, d=0.629, g=0.768\n",
      ">1, 227/234, d=0.659, g=0.783\n",
      ">1, 228/234, d=0.642, g=0.739\n",
      ">1, 229/234, d=0.647, g=0.735\n",
      ">1, 230/234, d=0.676, g=0.729\n",
      ">1, 231/234, d=0.664, g=0.735\n",
      ">1, 232/234, d=0.656, g=0.720\n",
      ">1, 233/234, d=0.685, g=0.692\n",
      ">1, 234/234, d=0.694, g=0.714\n",
      ">Accuracy real: 63%, fake: 65%\n",
      ">2, 1/234, d=0.696, g=0.706\n",
      ">2, 2/234, d=0.698, g=0.737\n",
      ">2, 3/234, d=0.693, g=0.714\n",
      ">2, 4/234, d=0.718, g=0.728\n",
      ">2, 5/234, d=0.710, g=0.727\n",
      ">2, 6/234, d=0.698, g=0.745\n",
      ">2, 7/234, d=0.704, g=0.717\n",
      ">2, 8/234, d=0.740, g=0.725\n",
      ">2, 9/234, d=0.720, g=0.715\n",
      ">2, 10/234, d=0.721, g=0.715\n",
      ">2, 11/234, d=0.727, g=0.710\n",
      ">2, 12/234, d=0.759, g=0.711\n",
      ">2, 13/234, d=0.757, g=0.705\n",
      ">2, 14/234, d=0.728, g=0.692\n",
      ">2, 15/234, d=0.781, g=0.667\n",
      ">2, 16/234, d=0.749, g=0.646\n",
      ">2, 17/234, d=0.778, g=0.670\n",
      ">2, 18/234, d=0.784, g=0.674\n",
      ">2, 19/234, d=0.771, g=0.651\n",
      ">2, 20/234, d=0.782, g=0.690\n",
      ">2, 21/234, d=0.775, g=0.690\n",
      ">2, 22/234, d=0.742, g=0.717\n",
      ">2, 23/234, d=0.732, g=0.731\n",
      ">2, 24/234, d=0.739, g=0.742\n",
      ">2, 25/234, d=0.702, g=0.765\n",
      ">2, 26/234, d=0.683, g=0.762\n",
      ">2, 27/234, d=0.676, g=0.813\n",
      ">2, 28/234, d=0.658, g=0.811\n",
      ">2, 29/234, d=0.655, g=0.808\n",
      ">2, 30/234, d=0.628, g=0.833\n",
      ">2, 31/234, d=0.619, g=0.826\n",
      ">2, 32/234, d=0.597, g=0.840\n",
      ">2, 33/234, d=0.578, g=0.846\n",
      ">2, 34/234, d=0.565, g=0.844\n",
      ">2, 35/234, d=0.561, g=0.827\n",
      ">2, 36/234, d=0.557, g=0.815\n",
      ">2, 37/234, d=0.562, g=0.812\n",
      ">2, 38/234, d=0.565, g=0.795\n",
      ">2, 39/234, d=0.566, g=0.735\n",
      ">2, 40/234, d=0.574, g=0.741\n",
      ">2, 41/234, d=0.587, g=0.702\n",
      ">2, 42/234, d=0.579, g=0.691\n",
      ">2, 43/234, d=0.584, g=0.673\n",
      ">2, 44/234, d=0.598, g=0.672\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">2, 45/234, d=0.618, g=0.617\n",
      ">2, 46/234, d=0.650, g=0.632\n",
      ">2, 47/234, d=0.645, g=0.633\n",
      ">2, 48/234, d=0.681, g=0.618\n",
      ">2, 49/234, d=0.704, g=0.651\n",
      ">2, 50/234, d=0.722, g=0.644\n",
      ">2, 51/234, d=0.754, g=0.646\n",
      ">2, 52/234, d=0.786, g=0.663\n",
      ">2, 53/234, d=0.767, g=0.692\n",
      ">2, 54/234, d=0.770, g=0.673\n",
      ">2, 55/234, d=0.802, g=0.699\n",
      ">2, 56/234, d=0.801, g=0.701\n",
      ">2, 57/234, d=0.802, g=0.714\n",
      ">2, 58/234, d=0.812, g=0.747\n",
      ">2, 59/234, d=0.780, g=0.760\n",
      ">2, 60/234, d=0.780, g=0.789\n",
      ">2, 61/234, d=0.758, g=0.814\n",
      ">2, 62/234, d=0.752, g=0.818\n",
      ">2, 63/234, d=0.749, g=0.783\n",
      ">2, 64/234, d=0.724, g=0.787\n",
      ">2, 65/234, d=0.711, g=0.771\n",
      ">2, 66/234, d=0.714, g=0.765\n",
      ">2, 67/234, d=0.701, g=0.776\n",
      ">2, 68/234, d=0.696, g=0.761\n",
      ">2, 69/234, d=0.676, g=0.756\n",
      ">2, 70/234, d=0.629, g=0.779\n",
      ">2, 71/234, d=0.633, g=0.789\n",
      ">2, 72/234, d=0.618, g=0.805\n",
      ">2, 73/234, d=0.639, g=0.829\n",
      ">2, 74/234, d=0.632, g=0.835\n",
      ">2, 75/234, d=0.635, g=0.838\n",
      ">2, 76/234, d=0.649, g=0.821\n",
      ">2, 77/234, d=0.641, g=0.814\n",
      ">2, 78/234, d=0.659, g=0.792\n",
      ">2, 79/234, d=0.649, g=0.773\n",
      ">2, 80/234, d=0.672, g=0.735\n",
      ">2, 81/234, d=0.680, g=0.726\n",
      ">2, 82/234, d=0.715, g=0.676\n",
      ">2, 83/234, d=0.719, g=0.668\n",
      ">2, 84/234, d=0.722, g=0.626\n",
      ">2, 85/234, d=0.731, g=0.643\n",
      ">2, 86/234, d=0.731, g=0.630\n",
      ">2, 87/234, d=0.759, g=0.625\n",
      ">2, 88/234, d=0.738, g=0.666\n",
      ">2, 89/234, d=0.755, g=0.681\n",
      ">2, 90/234, d=0.755, g=0.722\n",
      ">2, 91/234, d=0.751, g=0.729\n",
      ">2, 92/234, d=0.738, g=0.724\n",
      ">2, 93/234, d=0.753, g=0.735\n",
      ">2, 94/234, d=0.741, g=0.712\n",
      ">2, 95/234, d=0.724, g=0.709\n",
      ">2, 96/234, d=0.763, g=0.714\n",
      ">2, 97/234, d=0.729, g=0.698\n",
      ">2, 98/234, d=0.738, g=0.719\n",
      ">2, 99/234, d=0.731, g=0.710\n",
      ">2, 100/234, d=0.740, g=0.719\n",
      ">2, 101/234, d=0.749, g=0.705\n",
      ">2, 102/234, d=0.749, g=0.694\n",
      ">2, 103/234, d=0.751, g=0.712\n",
      ">2, 104/234, d=0.739, g=0.710\n",
      ">2, 105/234, d=0.741, g=0.719\n",
      ">2, 106/234, d=0.749, g=0.712\n",
      ">2, 107/234, d=0.753, g=0.704\n",
      ">2, 108/234, d=0.744, g=0.708\n",
      ">2, 109/234, d=0.735, g=0.712\n",
      ">2, 110/234, d=0.743, g=0.724\n",
      ">2, 111/234, d=0.735, g=0.718\n",
      ">2, 112/234, d=0.744, g=0.721\n",
      ">2, 113/234, d=0.723, g=0.732\n",
      ">2, 114/234, d=0.730, g=0.730\n",
      ">2, 115/234, d=0.703, g=0.754\n",
      ">2, 116/234, d=0.707, g=0.751\n",
      ">2, 117/234, d=0.720, g=0.746\n",
      ">2, 118/234, d=0.710, g=0.726\n",
      ">2, 119/234, d=0.710, g=0.707\n",
      ">2, 120/234, d=0.701, g=0.704\n",
      ">2, 121/234, d=0.702, g=0.717\n",
      ">2, 122/234, d=0.710, g=0.689\n",
      ">2, 123/234, d=0.701, g=0.699\n",
      ">2, 124/234, d=0.709, g=0.703\n",
      ">2, 125/234, d=0.696, g=0.703\n",
      ">2, 126/234, d=0.723, g=0.730\n",
      ">2, 127/234, d=0.710, g=0.732\n",
      ">2, 128/234, d=0.717, g=0.756\n",
      ">2, 129/234, d=0.699, g=0.748\n",
      ">2, 130/234, d=0.706, g=0.769\n",
      ">2, 131/234, d=0.689, g=0.758\n",
      ">2, 132/234, d=0.698, g=0.758\n",
      ">2, 133/234, d=0.713, g=0.752\n",
      ">2, 134/234, d=0.695, g=0.767\n",
      ">2, 135/234, d=0.695, g=0.734\n",
      ">2, 136/234, d=0.699, g=0.730\n",
      ">2, 137/234, d=0.709, g=0.738\n",
      ">2, 138/234, d=0.700, g=0.748\n",
      ">2, 139/234, d=0.698, g=0.745\n",
      ">2, 140/234, d=0.704, g=0.734\n",
      ">2, 141/234, d=0.704, g=0.723\n",
      ">2, 142/234, d=0.687, g=0.739\n",
      ">2, 143/234, d=0.685, g=0.745\n",
      ">2, 144/234, d=0.692, g=0.737\n",
      ">2, 145/234, d=0.674, g=0.744\n",
      ">2, 146/234, d=0.683, g=0.753\n",
      ">2, 147/234, d=0.689, g=0.746\n",
      ">2, 148/234, d=0.676, g=0.754\n",
      ">2, 149/234, d=0.669, g=0.746\n",
      ">2, 150/234, d=0.677, g=0.730\n",
      ">2, 151/234, d=0.682, g=0.714\n",
      ">2, 152/234, d=0.676, g=0.717\n",
      ">2, 153/234, d=0.681, g=0.694\n",
      ">2, 154/234, d=0.682, g=0.702\n",
      ">2, 155/234, d=0.680, g=0.687\n",
      ">2, 156/234, d=0.672, g=0.695\n",
      ">2, 157/234, d=0.662, g=0.690\n",
      ">2, 158/234, d=0.695, g=0.699\n",
      ">2, 159/234, d=0.662, g=0.698\n",
      ">2, 160/234, d=0.673, g=0.696\n",
      ">2, 161/234, d=0.673, g=0.712\n",
      ">2, 162/234, d=0.672, g=0.738\n",
      ">2, 163/234, d=0.659, g=0.736\n",
      ">2, 164/234, d=0.682, g=0.741\n",
      ">2, 165/234, d=0.680, g=0.737\n",
      ">2, 166/234, d=0.657, g=0.743\n",
      ">2, 167/234, d=0.660, g=0.761\n",
      ">2, 168/234, d=0.668, g=0.737\n",
      ">2, 169/234, d=0.655, g=0.726\n",
      ">2, 170/234, d=0.673, g=0.741\n",
      ">2, 171/234, d=0.671, g=0.709\n",
      ">2, 172/234, d=0.683, g=0.720\n",
      ">2, 173/234, d=0.666, g=0.717\n",
      ">2, 174/234, d=0.678, g=0.704\n",
      ">2, 175/234, d=0.675, g=0.693\n",
      ">2, 176/234, d=0.699, g=0.683\n",
      ">2, 177/234, d=0.697, g=0.691\n",
      ">2, 178/234, d=0.693, g=0.688\n",
      ">2, 179/234, d=0.683, g=0.670\n",
      ">2, 180/234, d=0.689, g=0.665\n",
      ">2, 181/234, d=0.698, g=0.691\n",
      ">2, 182/234, d=0.704, g=0.706\n",
      ">2, 183/234, d=0.706, g=0.667\n",
      ">2, 184/234, d=0.697, g=0.679\n",
      ">2, 185/234, d=0.695, g=0.672\n",
      ">2, 186/234, d=0.709, g=0.671\n",
      ">2, 187/234, d=0.709, g=0.673\n",
      ">2, 188/234, d=0.700, g=0.674\n",
      ">2, 189/234, d=0.714, g=0.689\n",
      ">2, 190/234, d=0.708, g=0.682\n",
      ">2, 191/234, d=0.714, g=0.676\n",
      ">2, 192/234, d=0.717, g=0.689\n",
      ">2, 193/234, d=0.709, g=0.692\n",
      ">2, 194/234, d=0.707, g=0.696\n",
      ">2, 195/234, d=0.716, g=0.722\n",
      ">2, 196/234, d=0.716, g=0.719\n",
      ">2, 197/234, d=0.707, g=0.726\n",
      ">2, 198/234, d=0.710, g=0.710\n",
      ">2, 199/234, d=0.692, g=0.700\n",
      ">2, 200/234, d=0.702, g=0.703\n",
      ">2, 201/234, d=0.693, g=0.707\n",
      ">2, 202/234, d=0.703, g=0.714\n",
      ">2, 203/234, d=0.711, g=0.739\n",
      ">2, 204/234, d=0.718, g=0.715\n",
      ">2, 205/234, d=0.703, g=0.721\n",
      ">2, 206/234, d=0.696, g=0.745\n",
      ">2, 207/234, d=0.714, g=0.749\n",
      ">2, 208/234, d=0.694, g=0.726\n",
      ">2, 209/234, d=0.709, g=0.739\n",
      ">2, 210/234, d=0.688, g=0.751\n",
      ">2, 211/234, d=0.674, g=0.751\n",
      ">2, 212/234, d=0.696, g=0.760\n",
      ">2, 213/234, d=0.685, g=0.764\n",
      ">2, 214/234, d=0.701, g=0.759\n",
      ">2, 215/234, d=0.697, g=0.761\n",
      ">2, 216/234, d=0.702, g=0.762\n",
      ">2, 217/234, d=0.689, g=0.759\n",
      ">2, 218/234, d=0.701, g=0.745\n",
      ">2, 219/234, d=0.699, g=0.769\n",
      ">2, 220/234, d=0.690, g=0.769\n",
      ">2, 221/234, d=0.694, g=0.763\n",
      ">2, 222/234, d=0.697, g=0.763\n",
      ">2, 223/234, d=0.704, g=0.778\n",
      ">2, 224/234, d=0.701, g=0.767\n",
      ">2, 225/234, d=0.694, g=0.758\n",
      ">2, 226/234, d=0.690, g=0.768\n",
      ">2, 227/234, d=0.696, g=0.750\n",
      ">2, 228/234, d=0.677, g=0.763\n",
      ">2, 229/234, d=0.692, g=0.762\n",
      ">2, 230/234, d=0.687, g=0.734\n",
      ">2, 231/234, d=0.695, g=0.754\n",
      ">2, 232/234, d=0.711, g=0.717\n",
      ">2, 233/234, d=0.704, g=0.750\n",
      ">2, 234/234, d=0.687, g=0.736\n",
      ">Accuracy real: 37%, fake: 68%\n",
      ">3, 1/234, d=0.693, g=0.745\n",
      ">3, 2/234, d=0.681, g=0.740\n",
      ">3, 3/234, d=0.683, g=0.755\n",
      ">3, 4/234, d=0.698, g=0.729\n",
      ">3, 5/234, d=0.689, g=0.731\n",
      ">3, 6/234, d=0.711, g=0.712\n",
      ">3, 7/234, d=0.704, g=0.720\n",
      ">3, 8/234, d=0.693, g=0.716\n",
      ">3, 9/234, d=0.705, g=0.730\n",
      ">3, 10/234, d=0.699, g=0.704\n",
      ">3, 11/234, d=0.699, g=0.712\n",
      ">3, 12/234, d=0.718, g=0.716\n",
      ">3, 13/234, d=0.692, g=0.720\n",
      ">3, 14/234, d=0.710, g=0.708\n",
      ">3, 15/234, d=0.686, g=0.714\n",
      ">3, 16/234, d=0.701, g=0.702\n",
      ">3, 17/234, d=0.705, g=0.702\n",
      ">3, 18/234, d=0.709, g=0.689\n",
      ">3, 19/234, d=0.682, g=0.697\n",
      ">3, 20/234, d=0.713, g=0.703\n",
      ">3, 21/234, d=0.706, g=0.699\n",
      ">3, 22/234, d=0.693, g=0.703\n",
      ">3, 23/234, d=0.678, g=0.712\n",
      ">3, 24/234, d=0.695, g=0.699\n",
      ">3, 25/234, d=0.701, g=0.690\n",
      ">3, 26/234, d=0.700, g=0.689\n",
      ">3, 27/234, d=0.703, g=0.679\n",
      ">3, 28/234, d=0.700, g=0.689\n",
      ">3, 29/234, d=0.704, g=0.684\n",
      ">3, 30/234, d=0.698, g=0.704\n",
      ">3, 31/234, d=0.712, g=0.684\n",
      ">3, 32/234, d=0.705, g=0.680\n",
      ">3, 33/234, d=0.704, g=0.682\n",
      ">3, 34/234, d=0.716, g=0.674\n",
      ">3, 35/234, d=0.691, g=0.661\n",
      ">3, 36/234, d=0.709, g=0.674\n",
      ">3, 37/234, d=0.700, g=0.669\n",
      ">3, 38/234, d=0.712, g=0.669\n",
      ">3, 39/234, d=0.702, g=0.680\n",
      ">3, 40/234, d=0.712, g=0.699\n",
      ">3, 41/234, d=0.700, g=0.689\n",
      ">3, 42/234, d=0.705, g=0.691\n",
      ">3, 43/234, d=0.721, g=0.684\n",
      ">3, 44/234, d=0.689, g=0.704\n",
      ">3, 45/234, d=0.714, g=0.691\n",
      ">3, 46/234, d=0.687, g=0.682\n",
      ">3, 47/234, d=0.688, g=0.680\n",
      ">3, 48/234, d=0.690, g=0.677\n",
      ">3, 49/234, d=0.707, g=0.682\n",
      ">3, 50/234, d=0.700, g=0.670\n",
      ">3, 51/234, d=0.694, g=0.669\n",
      ">3, 52/234, d=0.700, g=0.664\n",
      ">3, 53/234, d=0.701, g=0.677\n",
      ">3, 54/234, d=0.687, g=0.679\n",
      ">3, 55/234, d=0.697, g=0.699\n",
      ">3, 56/234, d=0.694, g=0.672\n",
      ">3, 57/234, d=0.702, g=0.692\n",
      ">3, 58/234, d=0.696, g=0.679\n",
      ">3, 59/234, d=0.702, g=0.687\n",
      ">3, 60/234, d=0.703, g=0.688\n",
      ">3, 61/234, d=0.704, g=0.682\n",
      ">3, 62/234, d=0.707, g=0.689\n",
      ">3, 63/234, d=0.696, g=0.680\n",
      ">3, 64/234, d=0.685, g=0.685\n",
      ">3, 65/234, d=0.708, g=0.697\n",
      ">3, 66/234, d=0.703, g=0.684\n",
      ">3, 67/234, d=0.703, g=0.703\n",
      ">3, 68/234, d=0.702, g=0.684\n",
      ">3, 69/234, d=0.693, g=0.685\n",
      ">3, 70/234, d=0.694, g=0.684\n",
      ">3, 71/234, d=0.697, g=0.693\n",
      ">3, 72/234, d=0.691, g=0.689\n",
      ">3, 73/234, d=0.687, g=0.702\n",
      ">3, 74/234, d=0.682, g=0.694\n",
      ">3, 75/234, d=0.694, g=0.706\n",
      ">3, 76/234, d=0.679, g=0.723\n",
      ">3, 77/234, d=0.676, g=0.711\n",
      ">3, 78/234, d=0.687, g=0.716\n",
      ">3, 79/234, d=0.681, g=0.723\n",
      ">3, 80/234, d=0.684, g=0.714\n",
      ">3, 81/234, d=0.692, g=0.733\n",
      ">3, 82/234, d=0.672, g=0.727\n",
      ">3, 83/234, d=0.673, g=0.713\n",
      ">3, 84/234, d=0.675, g=0.712\n",
      ">3, 85/234, d=0.675, g=0.709\n",
      ">3, 86/234, d=0.675, g=0.712\n",
      ">3, 87/234, d=0.668, g=0.719\n",
      ">3, 88/234, d=0.675, g=0.719\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">3, 89/234, d=0.657, g=0.710\n",
      ">3, 90/234, d=0.673, g=0.717\n",
      ">3, 91/234, d=0.677, g=0.724\n",
      ">3, 92/234, d=0.678, g=0.740\n",
      ">3, 93/234, d=0.673, g=0.733\n",
      ">3, 94/234, d=0.666, g=0.736\n",
      ">3, 95/234, d=0.673, g=0.717\n",
      ">3, 96/234, d=0.678, g=0.727\n",
      ">3, 97/234, d=0.688, g=0.716\n",
      ">3, 98/234, d=0.683, g=0.721\n",
      ">3, 99/234, d=0.682, g=0.726\n",
      ">3, 100/234, d=0.683, g=0.722\n",
      ">3, 101/234, d=0.691, g=0.727\n",
      ">3, 102/234, d=0.691, g=0.729\n",
      ">3, 103/234, d=0.697, g=0.725\n",
      ">3, 104/234, d=0.689, g=0.715\n",
      ">3, 105/234, d=0.690, g=0.725\n",
      ">3, 106/234, d=0.690, g=0.714\n",
      ">3, 107/234, d=0.696, g=0.718\n",
      ">3, 108/234, d=0.686, g=0.731\n",
      ">3, 109/234, d=0.702, g=0.733\n",
      ">3, 110/234, d=0.700, g=0.736\n",
      ">3, 111/234, d=0.686, g=0.746\n",
      ">3, 112/234, d=0.708, g=0.745\n",
      ">3, 113/234, d=0.693, g=0.744\n",
      ">3, 114/234, d=0.691, g=0.732\n",
      ">3, 115/234, d=0.705, g=0.723\n",
      ">3, 116/234, d=0.695, g=0.727\n",
      ">3, 117/234, d=0.698, g=0.711\n",
      ">3, 118/234, d=0.687, g=0.712\n",
      ">3, 119/234, d=0.703, g=0.717\n",
      ">3, 120/234, d=0.706, g=0.711\n",
      ">3, 121/234, d=0.710, g=0.701\n",
      ">3, 122/234, d=0.690, g=0.700\n",
      ">3, 123/234, d=0.697, g=0.703\n",
      ">3, 124/234, d=0.696, g=0.702\n",
      ">3, 125/234, d=0.696, g=0.726\n",
      ">3, 126/234, d=0.704, g=0.714\n",
      ">3, 127/234, d=0.696, g=0.699\n",
      ">3, 128/234, d=0.702, g=0.728\n",
      ">3, 129/234, d=0.705, g=0.714\n",
      ">3, 130/234, d=0.697, g=0.693\n",
      ">3, 131/234, d=0.706, g=0.714\n",
      ">3, 132/234, d=0.713, g=0.709\n",
      ">3, 133/234, d=0.699, g=0.712\n",
      ">3, 134/234, d=0.710, g=0.697\n",
      ">3, 135/234, d=0.710, g=0.703\n",
      ">3, 136/234, d=0.694, g=0.698\n",
      ">3, 137/234, d=0.706, g=0.697\n",
      ">3, 138/234, d=0.718, g=0.709\n",
      ">3, 139/234, d=0.717, g=0.698\n",
      ">3, 140/234, d=0.702, g=0.705\n",
      ">3, 141/234, d=0.706, g=0.701\n",
      ">3, 142/234, d=0.716, g=0.699\n",
      ">3, 143/234, d=0.716, g=0.694\n",
      ">3, 144/234, d=0.715, g=0.695\n",
      ">3, 145/234, d=0.711, g=0.711\n",
      ">3, 146/234, d=0.714, g=0.687\n",
      ">3, 147/234, d=0.716, g=0.698\n",
      ">3, 148/234, d=0.720, g=0.706\n",
      ">3, 149/234, d=0.711, g=0.704\n",
      ">3, 150/234, d=0.710, g=0.713\n",
      ">3, 151/234, d=0.713, g=0.716\n",
      ">3, 152/234, d=0.697, g=0.714\n",
      ">3, 153/234, d=0.712, g=0.717\n",
      ">3, 154/234, d=0.706, g=0.723\n",
      ">3, 155/234, d=0.693, g=0.732\n",
      ">3, 156/234, d=0.693, g=0.731\n",
      ">3, 157/234, d=0.696, g=0.729\n",
      ">3, 158/234, d=0.693, g=0.723\n",
      ">3, 159/234, d=0.686, g=0.737\n",
      ">3, 160/234, d=0.680, g=0.734\n",
      ">3, 161/234, d=0.683, g=0.736\n",
      ">3, 162/234, d=0.685, g=0.749\n",
      ">3, 163/234, d=0.677, g=0.759\n",
      ">3, 164/234, d=0.665, g=0.766\n",
      ">3, 165/234, d=0.671, g=0.763\n",
      ">3, 166/234, d=0.680, g=0.774\n",
      ">3, 167/234, d=0.666, g=0.774\n",
      ">3, 168/234, d=0.663, g=0.781\n",
      ">3, 169/234, d=0.658, g=0.793\n",
      ">3, 170/234, d=0.653, g=0.795\n",
      ">3, 171/234, d=0.660, g=0.784\n",
      ">3, 172/234, d=0.644, g=0.796\n",
      ">3, 173/234, d=0.651, g=0.777\n",
      ">3, 174/234, d=0.640, g=0.771\n",
      ">3, 175/234, d=0.655, g=0.767\n",
      ">3, 176/234, d=0.649, g=0.764\n",
      ">3, 177/234, d=0.642, g=0.758\n",
      ">3, 178/234, d=0.653, g=0.742\n",
      ">3, 179/234, d=0.658, g=0.741\n",
      ">3, 180/234, d=0.652, g=0.738\n",
      ">3, 181/234, d=0.653, g=0.735\n",
      ">3, 182/234, d=0.660, g=0.729\n",
      ">3, 183/234, d=0.652, g=0.716\n",
      ">3, 184/234, d=0.651, g=0.711\n",
      ">3, 185/234, d=0.650, g=0.719\n",
      ">3, 186/234, d=0.665, g=0.701\n",
      ">3, 187/234, d=0.670, g=0.709\n",
      ">3, 188/234, d=0.667, g=0.707\n",
      ">3, 189/234, d=0.675, g=0.700\n",
      ">3, 190/234, d=0.673, g=0.701\n",
      ">3, 191/234, d=0.685, g=0.692\n",
      ">3, 192/234, d=0.667, g=0.707\n",
      ">3, 193/234, d=0.662, g=0.698\n",
      ">3, 194/234, d=0.685, g=0.703\n",
      ">3, 195/234, d=0.682, g=0.694\n",
      ">3, 196/234, d=0.674, g=0.684\n",
      ">3, 197/234, d=0.697, g=0.686\n",
      ">3, 198/234, d=0.688, g=0.699\n",
      ">3, 199/234, d=0.683, g=0.677\n",
      ">3, 200/234, d=0.679, g=0.692\n",
      ">3, 201/234, d=0.698, g=0.689\n",
      ">3, 202/234, d=0.704, g=0.691\n",
      ">3, 203/234, d=0.695, g=0.676\n",
      ">3, 204/234, d=0.692, g=0.681\n",
      ">3, 205/234, d=0.699, g=0.678\n",
      ">3, 206/234, d=0.694, g=0.690\n",
      ">3, 207/234, d=0.680, g=0.695\n",
      ">3, 208/234, d=0.708, g=0.697\n",
      ">3, 209/234, d=0.691, g=0.699\n",
      ">3, 210/234, d=0.684, g=0.721\n",
      ">3, 211/234, d=0.692, g=0.711\n",
      ">3, 212/234, d=0.695, g=0.739\n",
      ">3, 213/234, d=0.695, g=0.732\n",
      ">3, 214/234, d=0.681, g=0.737\n",
      ">3, 215/234, d=0.658, g=0.736\n",
      ">3, 216/234, d=0.682, g=0.760\n",
      ">3, 217/234, d=0.671, g=0.753\n",
      ">3, 218/234, d=0.699, g=0.751\n",
      ">3, 219/234, d=0.669, g=0.757\n",
      ">3, 220/234, d=0.671, g=0.769\n",
      ">3, 221/234, d=0.668, g=0.766\n",
      ">3, 222/234, d=0.665, g=0.773\n",
      ">3, 223/234, d=0.661, g=0.778\n",
      ">3, 224/234, d=0.669, g=0.789\n",
      ">3, 225/234, d=0.656, g=0.781\n",
      ">3, 226/234, d=0.660, g=0.781\n",
      ">3, 227/234, d=0.652, g=0.758\n",
      ">3, 228/234, d=0.659, g=0.764\n",
      ">3, 229/234, d=0.658, g=0.755\n",
      ">3, 230/234, d=0.646, g=0.760\n",
      ">3, 231/234, d=0.666, g=0.771\n",
      ">3, 232/234, d=0.674, g=0.762\n",
      ">3, 233/234, d=0.670, g=0.763\n",
      ">3, 234/234, d=0.669, g=0.785\n",
      ">Accuracy real: 49%, fake: 82%\n"
     ]
    }
   ],
   "source": [
    "# load image data\n",
    "dataset = load_real_samples()\n",
    "# train model\n",
    "train(g_model, d_model, gan_model, dataset, latent_dim,n_epochs=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Really well done on this checkpoint -- you clearly engaged a lot with this "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TOTAL:  10/10"
   ]
  }
 ],
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