Ex8.Regular.GenerativeAdversarialNetwork.ipynb

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    "# 8th exercise: <font color=\"#C70039\">Work with a regular Generative Adversarial Network (GAN)</font>\n",
    "* Course: AML\n",
    "* Lecturer: <a href=\"https://www.gernotheisenberg.de/\">Gernot Heisenberg</a>\n",
    "* Author of notebook: <a href=\"https://www.gernotheisenberg.de/\">Gernot Heisenberg</a>\n",
    "* Date:   08.08.2023\n",
    "\n",
    "<img src=\"https://www.researchgate.net/publication/338509383/figure/fig1/AS:845792709464064@1578664104008/Overview-of-generative-adversarial-network-GAN.ppm\" style=\"float: center;\" width=\"600\">\n",
    "\n",
    "---------------------------------\n",
    "**GENERAL NOTE 1**: \n",
    "Please make sure you are reading the entire notebook, since it contains a lot of information on your tasks (e.g. regarding the set of certain paramaters or a specific computational trick), and the written mark downs as well as comments contain a lot of information on how things work together as a whole. \n",
    "\n",
    "**GENERAL NOTE 2**: \n",
    "* Please, when commenting source code, just use English language only. \n",
    "* When describing an observation please use English language, too.\n",
    "* This applies to all exercises throughout this course.\n",
    "\n",
    "---------------------------------\n",
    "\n",
    "### <font color=\"ce33ff\">DESCRIPTION</font>:\n",
    "The code implements a regular GAN that generates images using a random latent vector as input. While it works great we do not know the mapping of latent vector to the generated image.\n",
    "\n",
    "Conditional GANs, however, can be used to supply a label during taining so the latent vector can be associated with a specific label - making the generation of images predictable. \n",
    "\n",
    "The below coded GAN is using the so-called cifar10 data set, which is standard to many machine learning applications (60,000 32x32 color images in 10 different classes: CIFAR10 classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck).\n",
    "Following the code trains and generates images based on that cifar10 data set, that is included in the Keras package. \n",
    "Further information on the data set can be found here: <a href=\"https://en.wikipedia.org/wiki/CIFAR-10\"> cifar10 data set (wiki)</a>\n",
    "\n",
    "The code is adapted from the code by Jason Brownlee from his blogs on <a href=\"https://machinelearningmastery.com/\">https://machinelearningmastery.com/</a>.\n",
    "I seriously urge everyone to follow his blogs and get inspired. Hence, original credit goes to Jason Brownlee. \n",
    "\n",
    "---------------------------------\n",
    "\n",
    "### <font color=\"FFC300\">TASKS</font>:\n",
    "The tasks that you need to work on within this notebook are always indicated below as bullet points. \n",
    "If a task is more challenging and consists of several steps, this is indicated as well. \n",
    "Make sure you have worked down the task list and commented your doings. \n",
    "This should be done by using markdown.<br> \n",
    "<font color=red>Make sure you don't forget to specify your name and your matriculation number in the notebook.</font>\n",
    "\n",
    "**YOUR TASKS in this exercise are as follows**:\n",
    "1. import the notebook to Google Colab or use your local machine.\n",
    "2. make sure you specified you name and your matriculation number in the header below my name and date. \n",
    "    * set the date too and remove mine.\n",
    "3. read the entire notebook carefully \n",
    "    * add comments whereever you feel it necessary for better understanding\n",
    "    * run the notebook for the first time. \n",
    "4. train the GAN with a higher dimension of the latent space and over more epochs. Are the results improving? \n",
    "5. What could be done to further improve the quality of the generated images? \n",
    "---------------------------------"
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    "### GAN\n",
    "#### Imports"
   ]
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    "import numpy as np\n",
    "from numpy import zeros\n",
    "from numpy import ones\n",
    "from numpy.random import randn\n",
    "from numpy.random import randint\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow.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 tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.datasets.cifar10 import load_data\n",
    "\n",
    "# this import is needed because there is a problem with the Secure Socket Layer (SSL) certificate, \n",
    "# It can be resolved using the below line of code\n",
    "import ssl\n",
    "ssl._create_default_https_context = ssl._create_unverified_context"
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   "source": [
    "### Build the GAN architeture\n",
    "\n",
    "Use the \"functional\" way of defining the model for the conditional gan but use \"sequential\" for descriminator and generator as they are straight forward."
   ]
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    "#### Define the standalone discriminator model\n",
    "\n",
    "Given an input image, the Discriminator outputs the likelihood of the image being real.\n",
    "Binary classification - true or false (1 or 0). So use the sigmoid activation."
   ]
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     "text": [
      "Model: \"discriminator\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_3 (Conv2D)           (None, 16, 16, 128)       3584      \n",
      "                                                                 \n",
      " leaky_re_lu_5 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 8, 8, 128)         147584    \n",
      "                                                                 \n",
      " leaky_re_lu_6 (LeakyReLU)   (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 8192)              0         \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 8192)              0         \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 1)                 8193      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 159361 (622.50 KB)\n",
      "Trainable params: 159361 (622.50 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
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    "def define_discriminator(in_shape=(32,32,3)):\n",
    "    model = Sequential(name=\"discriminator\")\n",
    "\n",
    "    model.add(Conv2D(128, (3,3), strides=(2,2), padding='same', input_shape=in_shape)) #16x16x128\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "\n",
    "    model.add(Conv2D(128, (3,3), strides=(2,2), padding='same')) #8x8x128\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "\n",
    "    model.add(Flatten()) #shape of 8192\n",
    "    model.add(Dropout(0.4))\n",
    "    model.add(Dense(1, activation='sigmoid')) #shape of 1\n",
    "    \n",
    "    # Compile model since it is going to be trained directly and choose the optimizer\n",
    "    opt = Adam(learning_rate=0.0002, beta_1=0.5)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n",
    "    \n",
    "    return model\n",
    "\n",
    "test_discriminator = define_discriminator()\n",
    "print(test_discriminator.summary())"
   ]
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    "#### Define the standalone generator model\n",
    "Given the latent vector input, the Generator produces an image.(here: 32x32)\n",
    "The latent_dim, for example, can be 100, 1D array of size 100. \n",
    "\n",
    "Only Dense and conv2dlayers are being used. But the network can be more complicated based on the problem you are trying to solve. For example, you can use VGG as the basis for a super resolution GAN. "
   ]
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     "text": [
      "Model: \"generator\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense_1 (Dense)             (None, 8192)              827392    \n",
      "                                                                 \n",
      " leaky_re_lu_2 (LeakyReLU)   (None, 8192)              0         \n",
      "                                                                 \n",
      " reshape (Reshape)           (None, 8, 8, 128)         0         \n",
      "                                                                 \n",
      " conv2d_transpose (Conv2DTr  (None, 16, 16, 128)       262272    \n",
      " anspose)                                                        \n",
      "                                                                 \n",
      " leaky_re_lu_3 (LeakyReLU)   (None, 16, 16, 128)       0         \n",
      "                                                                 \n",
      " conv2d_transpose_1 (Conv2D  (None, 32, 32, 128)       262272    \n",
      " Transpose)                                                      \n",
      "                                                                 \n",
      " leaky_re_lu_4 (LeakyReLU)   (None, 32, 32, 128)       0         \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 32, 32, 3)         24579     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1376515 (5.25 MB)\n",
      "Trainable params: 1376515 (5.25 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n",
      "None\n"
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    "#latent_dim is the dimension of the latent vector (e.g., 100 - see below)\n",
    "def define_generator(latent_dim):    \n",
    "    \n",
    "    model = Sequential(name=\"generator\")\n",
    "\n",
    "    # Reshape the input latent vector into an 8x8 image as a starting point. \n",
    "    # Hence n_nodes for the Dense layer are e.g. 128x8x8, so when reshaping the output \n",
    "    # it would be 8x8x128 and that can be slowly upscaled to a 32x32 image as the output.\n",
    "    n_nodes = 128 * 8 * 8  #8192 nodes\n",
    "    model.add(Dense(n_nodes, input_dim=latent_dim)) #Dense layer so we can work with a 1D latent vector\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    model.add(Reshape((8, 8, 128)))  #8x8x128 data set from the latent vector. \n",
    "    # upsample to 16x16\n",
    "    model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')) #16x16x128\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    # upsample to 32x32\n",
    "    model.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same')) #32x32x128\n",
    "    model.add(LeakyReLU(alpha=0.2))\n",
    "    # generate\n",
    "    model.add(Conv2D(3, (8,8), activation='tanh', padding='same')) #32x32x3\n",
    "    return model  # the generator model not compiled as it is not directly trained as the discriminator.\n",
    "                  # the generator is being trained via a GAN combined model (see below)\n",
    "\n",
    "test_generator = define_generator(latent_dim=100)\n",
    "print(test_generator.summary())"
   ]
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    "Now, define the combined generator and discriminator model, for updating the generator.\n",
    "The discriminator is trained separately so here only the generator will be trained by keeping the discriminator constant. "
   ]
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  {
   "cell_type": "code",
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   "id": "c58faac1",
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   "source": [
    "def define_gan(generator, discriminator):\n",
    "    # The discriminator is trained separately (see above). Hence, set it to not trainable.\n",
    "    discriminator.trainable = False  \n",
    "    \n",
    "    # Now, connect the generator and the discriminator\n",
    "    model = Sequential()\n",
    "    model.add(generator)\n",
    "    model.add(discriminator)\n",
    "    \n",
    "    # Compile the combined model and Choose an optimizer\n",
    "    opt = Adam(learning_rate=0.0002, beta_1=0.5)\n",
    "    model.compile(loss='binary_crossentropy', optimizer=opt)\n",
    "    \n",
    "    return model"
   ]
  },
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   "cell_type": "markdown",
   "id": "cbe4977b",
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   "source": [
    "### Load data\n",
    "Load the data set and plot parts of it in order to get a quick understanding.\n",
    "\n",
    "CIFAR10 classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4de710b9",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(trainX, trainy), (testX, testy) = load_data()\n",
    "\n",
    "# plot 25=5x5 example images\n",
    "for i in range(25):\n",
    "    plt.subplot(5, 5, 1 + i)\n",
    "    plt.axis('off')\n",
    "    plt.imshow(trainX[i])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "47265cf3",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load the cifar images and build a training data set\n",
    "def load_real_samples():\n",
    "    (trainX, _), (_, _) = load_data()\n",
    "    # convert to float and scale.\n",
    "    X = trainX.astype('float32')\n",
    "    # the generator uses tanh as activation function (see above), \n",
    "    # so we need to rescale from [0,255] to [-1,1]\n",
    "    # original images to -1 to 1 to match the output of generator.\n",
    "    X = (X - 127.5) / 127.5  \n",
    "    \n",
    "    return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "046cbd1a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Pick a batch of random real samples to train the GAN\n",
    "# In fact, train the GAN on a half batch of real images and another half batch of fake images. \n",
    "# For each real image assign the label=1 and for fake assign the label=0. \n",
    "def generate_real_samples(dataset, n_samples):\n",
    "    # choose random images\n",
    "    ix = randint(0, dataset.shape[0], n_samples)\n",
    "    # select the random images and assign it to X\n",
    "    x = dataset[ix]\n",
    "    # generate class labels and assign to y\n",
    "    y = ones((n_samples, 1)) # label=1 indicates that the image is a real one\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6698be62",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Generate n_samples number of latent vectors as input for the generator\n",
    "def generate_latent_points(latent_dim, n_samples):\n",
    "    # generate points in the latent space\n",
    "    x_input = randn(latent_dim * n_samples)\n",
    "    # reshape into a batch of inputs for the network\n",
    "    x_input = x_input.reshape(n_samples, latent_dim)\n",
    "    return x_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "39c08c43",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Use the generator to generate n fake examples, with class labels\n",
    "# Supply the generator, latent_dim and number of samples as input.\n",
    "# Use the above latent point generator to generate latent points. \n",
    "def generate_fake_samples(generator, latent_dim, n_samples):\n",
    "    # generate points in latent space\n",
    "    x_input = generate_latent_points(latent_dim, n_samples)\n",
    "    # predict using generator to generate fake samples. \n",
    "    x = generator.predict(x_input)\n",
    "    # Class labels will be 0 as these samples are fake. \n",
    "    y = zeros((n_samples, 1))  # label=0 indicates that the image is a fake one\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a2c93f",
   "metadata": {},
   "source": [
    "### Training function for the generator and discriminator\n",
    "Loop through a number of epochs to train the discriminator by first selecting a random batch of images from the true/real data set. Then, generate a set of images using the generator. Feed both sets of images into the discriminator. Finally, set the loss parameters for both the real and fake images, as well as the combined loss. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1d406b96",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train(g_model, d_model, gan_model, filename, dataset, latent_dim, n_epochs=100, n_batch=128):\n",
    "    bat_per_epo = int(dataset.shape[0] / n_batch)\n",
    "    half_batch = int(n_batch / 2)  #the discriminator model is updated for a half batch of real samples \n",
    "                            #and a half batch of fake samples, combined a single batch. \n",
    "    # manually enumerate epochs and bacthes. \n",
    "    for i in range(n_epochs):\n",
    "        # enumerate batches over the training set\n",
    "        for j in range(bat_per_epo):\n",
    "            \n",
    "            # Train the discriminator on real and fake images, separately (half batch each)\n",
    "            #Research showed that separate training is more effective. \n",
    "            # get randomly selected 'real' samples\n",
    "            X_real, y_real = generate_real_samples(dataset, half_batch)\n",
    "            # update discriminator model weights\n",
    "            ##train_on_batch allows you to update weights based on a collection \n",
    "            #of samples you provide\n",
    "            #Let us just capture loss and ignore accuracy value (2nd output below)\n",
    "            d_loss_real, _ = d_model.train_on_batch(X_real, y_real) \n",
    "            \n",
    "            # generate 'fake' examples\n",
    "            X_fake, y_fake = generate_fake_samples(g_model, latent_dim, half_batch)\n",
    "            # update discriminator model weights\n",
    "            d_loss_fake, _ = d_model.train_on_batch(X_fake, y_fake)\n",
    "            \n",
    "            #d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) #Average loss if you want to report single..\n",
    "            \n",
    "            # prepare points in latent space as input for the generator\n",
    "            X_gan = generate_latent_points(latent_dim, n_batch)\n",
    "             \n",
    "            # The generator wants the discriminator to label the generated samples as valid ones.\n",
    "            # This is where the generator is trying to trick discriminator into believing that \n",
    "            # the generated image is true (hence value of 1 for y)\n",
    "            y_gan = ones((n_batch, 1))\n",
    "            \n",
    "            # The generator is part of the combined model where it got directly linked with the discriminator\n",
    "            # Train the generator with latent_dim as x and 1 as y. \n",
    "            # Again, 1 as the output as it is adversarial and if the generator did a great\n",
    "            # job of fooling the discriminator, then the output would be 1 (true).\n",
    "            # Update the generator via the discriminator's error\n",
    "            g_loss = gan_model.train_on_batch(X_gan, y_gan)\n",
    "            \n",
    "            # Print losses on this batch\n",
    "            print('Epoch>%d, Batch %d/%d, d1=%.3f, d2=%.3f g=%.3f' %\n",
    "                 (i+1, j+1, bat_per_epo, d_loss_real, d_loss_fake, g_loss))\n",
    "    # save the generator model\n",
    "    g_model.save(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3638625",
   "metadata": {},
   "source": [
    "### Train the GAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca4213e0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'define_discriminator' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mg:\\Meine Ablage\\TH Köln\\AML\\Ex8.Regular.GenerativeAdversarialNetwork.ipynb Cell 21\u001b[0m line \u001b[0;36m4\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/Meine%20Ablage/TH%20K%C3%B6ln/AML/Ex8.Regular.GenerativeAdversarialNetwork.ipynb#X26sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m latent_dim \u001b[39m=\u001b[39m \u001b[39m100\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/Meine%20Ablage/TH%20K%C3%B6ln/AML/Ex8.Regular.GenerativeAdversarialNetwork.ipynb#X26sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m# create the discriminator\u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/g%3A/Meine%20Ablage/TH%20K%C3%B6ln/AML/Ex8.Regular.GenerativeAdversarialNetwork.ipynb#X26sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m discriminator \u001b[39m=\u001b[39m define_discriminator()\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/Meine%20Ablage/TH%20K%C3%B6ln/AML/Ex8.Regular.GenerativeAdversarialNetwork.ipynb#X26sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m# create the generator\u001b[39;00m\n\u001b[0;32m      <a href='vscode-notebook-cell:/g%3A/Meine%20Ablage/TH%20K%C3%B6ln/AML/Ex8.Regular.GenerativeAdversarialNetwork.ipynb#X26sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m generator \u001b[39m=\u001b[39m define_generator(latent_dim)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'define_discriminator' is not defined"
     ]
    }
   ],
   "source": [
    "# size of the latent space\n",
    "latent_dim = 100\n",
    "# create the discriminator\n",
    "discriminator = define_discriminator()\n",
    "# create the generator\n",
    "generator = define_generator(latent_dim)\n",
    "# create the gan\n",
    "gan_model = define_gan(generator, discriminator)\n",
    "# load image data\n",
    "dataset = load_real_samples()\n",
    "# train model\n",
    "n_epochs=5 #50\n",
    "model_filename = \"cifar_GAN.\" + str(n_epochs) + \"_epochs.model\"\n",
    "train(generator, discriminator, gan_model, model_filename, dataset, latent_dim, n_epochs)\n",
    "\n",
    "\n",
    "\n",
    "# Now, load the generator model and generate images\n",
    "\n",
    "# Plot generated images function\n",
    "def show_plot(examples, n):\n",
    "    for i in range(n * n):\n",
    "        plt.subplot(n, n, 1 + i)\n",
    "        plt.axis('off')\n",
    "        plt.imshow(examples[i, :, :, :])\n",
    "    plt.show()\n",
    "\n",
    "# load model\n",
    "model = load_model(model_filename) #Model trained for n epochs\n",
    "\n",
    "# generate the latent vector \n",
    "latent_points = generate_latent_points(100, 25)  #Latent dim and n_samples\n",
    "\n",
    "# generate images from the latent vector \n",
    "X = model.predict(latent_points)\n",
    "\n",
    "# scale from [-1,1] to [0,1]\n",
    "X = (X + 1) / 2.0\n",
    "X = (X*255).astype(np.uint8)\n",
    "\n",
    "# plot the generated images\n",
    "# Note: CIFAR10 classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck\n",
    "show_plot(X, 5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a000f625",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Now, load the generator model and generate images\n",
    "\n",
    "# Plot generated images function\n",
    "def show_plot(examples, n):\n",
    "    for i in range(n * n):\n",
    "        plt.subplot(n, n, 1 + i)\n",
    "        plt.axis('off')\n",
    "        plt.imshow(examples[i, :, :, :])\n",
    "    plt.show()\n",
    "\n",
    "# load model\n",
    "model = load_model(model_filename) #Model trained for n epochs\n",
    "\n",
    "# generate the latent vector \n",
    "latent_points = generate_latent_points(100, 25)  #Latent dim and n_samples\n",
    "\n",
    "# generate images from the latent vector \n",
    "X = model.predict(latent_points)\n",
    "\n",
    "# scale from [-1,1] to [0,1]\n",
    "X = (X + 1) / 2.0\n",
    "X = (X*255).astype(np.uint8)\n",
    "\n",
    "# plot the generated images\n",
    "# Note: CIFAR10 classes are: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck\n",
    "show_plot(X, 5)"
   ]
  }
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