AC-GAN.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Model: \"sequential_17\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_57 (Conv2D)           (None, 24, 24, 16)        160       \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_33 (LeakyReLU)   (None, 24, 24, 16)        0         \n",
      "_________________________________________________________________\n",
      "dropout_33 (Dropout)         (None, 24, 24, 16)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_58 (Conv2D)           (None, 12, 12, 32)        4640      \n",
      "_________________________________________________________________\n",
      "zero_padding2d_9 (ZeroPaddin (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_34 (LeakyReLU)   (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_34 (Dropout)         (None, 13, 13, 32)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_41 (Batc (None, 13, 13, 32)        128       \n",
      "_________________________________________________________________\n",
      "conv2d_59 (Conv2D)           (None, 7, 7, 64)          18496     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_35 (LeakyReLU)   (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_35 (Dropout)         (None, 7, 7, 64)          0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_42 (Batc (None, 7, 7, 64)          256       \n",
      "_________________________________________________________________\n",
      "conv2d_60 (Conv2D)           (None, 7, 7, 128)         73856     \n",
      "_________________________________________________________________\n",
      "leaky_re_lu_36 (LeakyReLU)   (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "dropout_36 (Dropout)         (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "flatten_17 (Flatten)         (None, 6272)              0         \n",
      "=================================================================\n",
      "Total params: 97,536\n",
      "Trainable params: 97,344\n",
      "Non-trainable params: 192\n",
      "_________________________________________________________________\n",
      "Model: \"sequential_18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "dense_27 (Dense)             (None, 6272)              633472    \n",
      "_________________________________________________________________\n",
      "reshape_9 (Reshape)          (None, 7, 7, 128)         0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_43 (Batc (None, 7, 7, 128)         512       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_17 (UpSampling (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_61 (Conv2D)           (None, 14, 14, 128)       147584    \n",
      "_________________________________________________________________\n",
      "activation_25 (Activation)   (None, 14, 14, 128)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_44 (Batc (None, 14, 14, 128)       512       \n",
      "_________________________________________________________________\n",
      "up_sampling2d_18 (UpSampling (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_62 (Conv2D)           (None, 28, 28, 64)        73792     \n",
      "_________________________________________________________________\n",
      "activation_26 (Activation)   (None, 28, 28, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_45 (Batc (None, 28, 28, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_63 (Conv2D)           (None, 28, 28, 1)         577       \n",
      "_________________________________________________________________\n",
      "activation_27 (Activation)   (None, 28, 28, 1)         0         \n",
      "=================================================================\n",
      "Total params: 856,705\n",
      "Trainable params: 856,065\n",
      "Non-trainable params: 640\n",
      "_________________________________________________________________\n",
      "Data loaded from .npy files\n",
      "28709 0\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Error when checking input: expected input_41 to have shape (48, 48, 1) but got array with shape (28, 28, 1)",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-7e14254717fb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m    232\u001b[0m \u001b[1;31m#  Train All\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    233\u001b[0m \u001b[1;31m# ---------------------\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 234\u001b[1;33m \u001b[0macgan\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m14000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m32\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_interval\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-9-7e14254717fb>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, epochs, batch_size, sample_interval)\u001b[0m\n\u001b[0;32m    176\u001b[0m             \u001b[1;31m# Train the discriminator\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    177\u001b[0m             \u001b[0md_loss_real\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mimgs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mvalid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mimg_labels\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 178\u001b[1;33m             \u001b[0md_loss_fake\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdiscriminator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain_on_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgen_imgs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mfake\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msampled_labels\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    179\u001b[0m             \u001b[0md_loss\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0.5\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md_loss_real\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0md_loss_fake\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\python36gpu\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36mtrain_on_batch\u001b[1;34m(self, x, y, sample_weight, class_weight, reset_metrics)\u001b[0m\n\u001b[0;32m   1506\u001b[0m             \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1507\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1508\u001b[1;33m             class_weight=class_weight)\n\u001b[0m\u001b[0;32m   1509\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_uses_dynamic_learning_phase\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1510\u001b[0m             \u001b[0mins\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0msample_weights\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\python36gpu\\lib\\site-packages\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m_standardize_user_data\u001b[1;34m(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)\u001b[0m\n\u001b[0;32m    577\u001b[0m             \u001b[0mfeed_input_shapes\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    578\u001b[0m             \u001b[0mcheck_batch_axis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m  \u001b[1;31m# Don't enforce the batch size.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 579\u001b[1;33m             exception_prefix='input')\n\u001b[0m\u001b[0;32m    580\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    581\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0my\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mE:\\Anaconda3\\envs\\python36gpu\\lib\\site-packages\\keras\\engine\\training_utils.py\u001b[0m in \u001b[0;36mstandardize_input_data\u001b[1;34m(data, names, shapes, check_batch_axis, exception_prefix)\u001b[0m\n\u001b[0;32m    143\u001b[0m                             \u001b[1;34m': expected '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' to have shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m                             \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m' but got array with shape '\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m                             str(data_shape))\n\u001b[0m\u001b[0;32m    146\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Error when checking input: expected input_41 to have shape (48, 48, 1) but got array with shape (28, 28, 1)"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, division\n",
    "\n",
    "from keras.datasets import mnist\n",
    "from keras.layers import Input, Dense, Reshape, Flatten, Dropout, multiply\n",
    "from keras.layers import BatchNormalization, Activation, Embedding, ZeroPadding2D\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers.convolutional import UpSampling2D, Conv2D\n",
    "from keras.models import Sequential, Model\n",
    "from keras.optimizers import Adam\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "class ACGAN():\n",
    "    def __init__(self):\n",
    "        # Input shape\n",
    "        self.img_rows = 48\n",
    "        self.img_cols = 48\n",
    "        self.channels = 1\n",
    "        self.img_shape = (self.img_rows, self.img_cols, self.channels)\n",
    "        self.num_classes = 7\n",
    "        self.latent_dim = 100\n",
    "\n",
    "        optimizer = Adam(0.0002, 0.5)\n",
    "        losses = ['binary_crossentropy', 'sparse_categorical_crossentropy']\n",
    "\n",
    "        # Build and compile the discriminator\n",
    "        self.discriminator = self.build_discriminator()\n",
    "        self.discriminator.compile(loss=losses,\n",
    "            optimizer=optimizer,\n",
    "            metrics=['accuracy'])\n",
    "\n",
    "        # Build the generator\n",
    "        self.generator = self.build_generator()\n",
    "\n",
    "        # The generator takes noise and the target label as input\n",
    "        # and generates the corresponding digit of that label\n",
    "        noise = Input(shape=(self.latent_dim,))\n",
    "        label = Input(shape=(1,))\n",
    "        img = self.generator([noise, label])\n",
    "\n",
    "        # For the combined model we will only train the generator\n",
    "        self.discriminator.trainable = False\n",
    "\n",
    "        # The discriminator takes generated image as input and determines validity\n",
    "        # and the label of that image\n",
    "        valid, target_label = self.discriminator(img)\n",
    "\n",
    "        # The combined model  (stacked generator and discriminator)\n",
    "        # Trains the generator to fool the discriminator\n",
    "        self.combined = Model([noise, label], [valid, target_label])\n",
    "        self.combined.compile(loss=losses,\n",
    "            optimizer=optimizer)\n",
    "\n",
    "    def build_generator(self):\n",
    "\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(Dense(128 * 7 * 7, activation=\"relu\", input_dim=self.latent_dim))\n",
    "        model.add(Reshape((7, 7, 128)))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(UpSampling2D())\n",
    "        model.add(Conv2D(128, kernel_size=3, padding=\"same\"))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(UpSampling2D())\n",
    "        model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n",
    "        model.add(Activation(\"relu\"))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(Conv2D(self.channels, kernel_size=3, padding='same'))\n",
    "        model.add(Activation(\"tanh\"))\n",
    "\n",
    "        model.summary()\n",
    "\n",
    "        noise = Input(shape=(self.latent_dim,))\n",
    "        label = Input(shape=(1,), dtype='int32')\n",
    "        label_embedding = Flatten()(Embedding(self.num_classes, self.latent_dim)(label))\n",
    "\n",
    "        model_input = multiply([noise, label_embedding])\n",
    "        img = model(model_input)\n",
    "\n",
    "        return Model([noise, label], img)\n",
    "\n",
    "    def build_discriminator(self):\n",
    "\n",
    "        model = Sequential()\n",
    "\n",
    "        model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding=\"same\"))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(Conv2D(32, kernel_size=3, strides=2, padding=\"same\"))\n",
    "        model.add(ZeroPadding2D(padding=((0,1),(0,1))))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "        model.add(BatchNormalization(momentum=0.8))\n",
    "        model.add(Conv2D(128, kernel_size=3, strides=1, padding=\"same\"))\n",
    "        model.add(LeakyReLU(alpha=0.2))\n",
    "        model.add(Dropout(0.25))\n",
    "\n",
    "        model.add(Flatten())\n",
    "        model.summary()\n",
    "\n",
    "        img = Input(shape=self.img_shape)\n",
    "\n",
    "        # Extract feature representation\n",
    "        features = model(img)\n",
    "\n",
    "        # Determine validity and label of the image\n",
    "        validity = Dense(1, activation=\"sigmoid\")(features)\n",
    "        label = Dense(self.num_classes, activation=\"softmax\")(features)\n",
    "\n",
    "        return Model(img, [validity, label])\n",
    "\n",
    "    def train(self, epochs, batch_size=128, sample_interval=50):\n",
    "\n",
    "        # # Load the dataset\n",
    "        # (X_train, y_train), (_, _) = mnist.load_data()\n",
    "\n",
    "        # # Configure inputs\n",
    "        # X_train = (X_train.astype(np.float32) - 127.5) / 127.5\n",
    "        # X_train = np.expand_dims(X_train, axis=3)\n",
    "        # y_train = y_train.reshape(-1, 1)\n",
    "\n",
    "        # print(len(y_train), y_train[0])\n",
    "\n",
    "        X = np.load('./Datasets/FER-2013/fdata.npy')\n",
    "        y = np.load('./Datasets/FER-2013/flabels.npy')\n",
    "        y = (np.argmax(y, axis=1))\n",
    "        tmask = np.load('./Datasets/FER-2013/ftraintestmask.npy')\n",
    "        print(\"Data loaded from .npy files\")\n",
    "\n",
    "        # Make image VGG compatible (3 channels instead of 1 channel gray scale, BGR, etc.)\n",
    "        # X_new = np.empty((len(X), width, height, channels))\n",
    "        # for i in range(len(X)):\n",
    "        #     X_new[i] = preprocess_input(np.dstack((X[i], X[i], X[i])), mode='caffe')\n",
    "        # X = X_new\n",
    "        # del X_new        \n",
    "\n",
    "        X_train, y_train = X[tmask], y[tmask]\n",
    "        X_test, y_test = X[~tmask], y[~tmask]\n",
    "\n",
    "        print(len(y_train), y_train[0])\n",
    "\n",
    "        # Adversarial ground truths\n",
    "        valid = np.ones((batch_size, 1))\n",
    "        fake = np.zeros((batch_size, 1))\n",
    "\n",
    "        for epoch in range(epochs):\n",
    "\n",
    "            # ---------------------\n",
    "            #  Train Discriminator\n",
    "            # ---------------------\n",
    "\n",
    "            # Select a random batch of images\n",
    "            idx = np.random.randint(0, X_train.shape[0], batch_size)\n",
    "            imgs = X_train[idx]\n",
    "\n",
    "            # Sample noise as generator input\n",
    "            noise = np.random.normal(0, 1, (batch_size, self.latent_dim))\n",
    "\n",
    "            # The labels of the digits that the generator tries to create an\n",
    "            # image representation of\n",
    "            sampled_labels = np.random.randint(0, 10, (batch_size, 1))\n",
    "\n",
    "            # Generate a half batch of new images\n",
    "            gen_imgs = self.generator.predict([noise, sampled_labels])\n",
    "\n",
    "            # Image labels. 0-9 \n",
    "            img_labels = y_train[idx]\n",
    "\n",
    "            # Train the discriminator\n",
    "            d_loss_real = self.discriminator.train_on_batch(imgs, [valid, img_labels])\n",
    "            d_loss_fake = self.discriminator.train_on_batch(gen_imgs, [fake, sampled_labels])\n",
    "            d_loss = 0.5 * np.add(d_loss_real, d_loss_fake)\n",
    "\n",
    "            # ---------------------\n",
    "            #  Train Generator\n",
    "            # ---------------------\n",
    "\n",
    "            # Train the generator\n",
    "            g_loss = self.combined.train_on_batch([noise, sampled_labels], [valid, sampled_labels])\n",
    "\n",
    "            # Plot the progress\n",
    "            print (\"%d [D loss: %f, acc.: %.2f%%, op_acc: %.2f%%] [G loss: %f]\" % (epoch, d_loss[0], 100*d_loss[3], 100*d_loss[4], g_loss[0]))\n",
    "\n",
    "            # If at save interval => save generated image samples\n",
    "            if epoch % sample_interval == 0:\n",
    "                self.save_model()\n",
    "                self.sample_images(epoch)\n",
    "\n",
    "    def sample_images(self, epoch):\n",
    "        r, c = 10, 10\n",
    "        noise = np.random.normal(0, 1, (r * c, self.latent_dim))\n",
    "        sampled_labels = np.array([num for _ in range(r) for num in range(c)])\n",
    "        gen_imgs = self.generator.predict([noise, sampled_labels])\n",
    "        # Rescale images 0 - 1\n",
    "        gen_imgs = 0.5 * gen_imgs + 0.5\n",
    "\n",
    "        fig, axs = plt.subplots(r, c)\n",
    "        cnt = 0\n",
    "        for i in range(r):\n",
    "            for j in range(c):\n",
    "                axs[i,j].imshow(gen_imgs[cnt,:,:,0], cmap='gray')\n",
    "                axs[i,j].axis('off')\n",
    "                cnt += 1\n",
    "        fig.savefig(\"Results/GAN/%d.png\" % epoch)\n",
    "        plt.close()\n",
    "\n",
    "    def save_model(self):\n",
    "\n",
    "        def save(model, model_name):\n",
    "            model_path = \"Saved Models/AC-GAN_%s_model.json\" % model_name\n",
    "            weights_path = \"Saved Models/AC-GAN_%s_weights.hdf5\" % model_name\n",
    "            options = {\"file_arch\": model_path,\n",
    "                        \"file_weight\": weights_path}\n",
    "            #json_string = model.to_json()\n",
    "            #open(options['file_arch'], 'w').write(json_string)\n",
    "            model.save_weights(options['file_weight'])\n",
    "\n",
    "        save(self.generator, \"generator\")\n",
    "        save(self.discriminator, \"discriminator\")\n",
    "\n",
    "\n",
    "acgan = ACGAN()\n",
    "\n",
    "# ---------------------\n",
    "#  Train All\n",
    "# ---------------------\n",
    "acgan.train(epochs=14000, batch_size=32, sample_interval=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "source": [
    "# ---------------------\n",
    "#  Test Discriminator\n",
    "# ---------------------\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n",
    "# Configure inputs\n",
    "X_test = (X_test.astype(np.float32) - 127.5) / 127.5\n",
    "X_test = np.expand_dims(X_test, axis=3)\n",
    "y_test = y_test.reshape(-1, 1)\n",
    "\n",
    "model = acgan.discriminator\n",
    "\n",
    "# for i in range(len(model.metrics_names)):\n",
    "#    print(\"Metric - {}: {:.5f}\".format(model.metrics_names[i], results[i]*100))\n",
    "  \n",
    "predictions = np.argmax(model.predict(X_test)[1], axis=1)  # For some reason returns 2 predictions, the label is the 2nd one\n",
    "acc = accuracy_score(y_test[:,0], predictions)\n",
    "cm = confusion_matrix(y_test[:,0], predictions)\n",
    "print()\n",
    "print(acc)\n",
    "print(cm)"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 73,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n0.2144\n[[  56    0    0    0    0    0    0    0  788  136]\n [   0    0    0    0    0    0    0    0 1135    0]\n [   0    0    0    0    0    0    0    0 1025    7]\n [   0    0    0    0    0    0    0    0  968   42]\n [   0    0    0    0    7    0    0    0  612  363]\n [   0    0    0    0    0    0    0    0  827   65]\n [   0    0    0    0    1    0  192    0  752   13]\n [   0    0    0    0    0    0    0    0  239  789]\n [   0    0    0    0    0    0    0    0  974    0]\n [   0    0    0    0    0    0    0    0   94  915]]\n"
     ]
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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