09_adversarial_learning.ipynb

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    "### Creative machine learning - Adversarial learning\n",
    "\n",
    "### Author: Philippe Esling (esling@ircam.fr)\n",
    "\n",
    "Generative adversarial networks (GANs) are neural networks that generate material, such as images, music, speech, or text, that is similar to what humans produce.\n",
    "\n",
    "In this course we will cover\n",
    "1. The global principle of [adversarial learning](#adversarial)\n",
    "2. How to train your own GAN on a [toy dataset](#toy)\n",
    "3. A practical example on [MNIST](#mnist) using PyTorch"
   ]
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    "<a id=\"adversarial\"></a>\n",
    "## Adversarial learning\n",
    "\n",
    "This is the code used in first example of the article [Generative Adversarial Networks: Build Your First Models](https://realpython.com/generative-adversarial-networks/) on Real Python."
   ]
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   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\garam\\OneDrive\\Documentos\\Github Repo\\cml\\Lib\\site-packages\\tf_keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\garam\\OneDrive\\Documentos\\Github Repo\\cml\\Lib\\site-packages\\tensorflow_probability\\python\\internal\\backend\\numpy\\_utils.py:48: The name tf.logging.TaskLevelStatusMessage is deprecated. Please use tf.compat.v1.logging.TaskLevelStatusMessage instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\garam\\OneDrive\\Documentos\\Github Repo\\cml\\Lib\\site-packages\\tensorflow_probability\\python\\internal\\backend\\numpy\\_utils.py:48: The name tf.control_flow_v2_enabled is deprecated. Please use tf.compat.v1.control_flow_v2_enabled instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from cml.plot import cml_figure_matplotlib as figure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generative adversarial networks (GANs) are machine learning systems that can learn to mimic a given distribution of data. GANs consist of two neural networks, one trained to *generate* data and the other trained to *discriminate* fake data from real data (hence the “adversarial” nature of the model).\n",
    "\n",
    "GANs require to define two neural networks, one called the *generator* and the other called the *discriminator*. The role of the generator is to estimate the probability distribution of the real samples in order to provide generated samples resembling real data. The discriminator, in turn, is trained to estimate the probability that a given sample came from the real data rather than being provided by the generator.\n",
    "\n",
    "These two networks define a *zero-sum game* as they are trained to compete with each other: the generator tries to get better at fooling the discriminator, while the discriminator tries to get better at identifying generated samples."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training GANs on a toy dataset\n",
    "\n",
    "To understand how GAN training works, consider a toy example with a dataset composed of two-dimensional samples $(x_{1}, x_{2})$, with $x_{1}$ in the interval from $0$ to $2\\pi$ and $x_{2} = sin(x_{1})$, as illustrated in the following "
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      "C:\\Users\\garam\\AppData\\Local\\Temp\\ipykernel_10296\\2127077040.py:9: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\garam\\OneDrive\\Documentos\\Github Repo\\cml\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  func(*args, **kwargs)\n",
      "C:\\Users\\garam\\OneDrive\\Documentos\\Github Repo\\cml\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data_length = 128\n",
    "train_data = torch.zeros((train_data_length,2))\n",
    "train_data[:,0] = 2*math.pi*torch.rand(train_data_length)\n",
    "train_data[:,1] = torch.sin(train_data[:,0])\n",
    "train_labels = torch.zeros(train_data_length)\n",
    "trainset = [(train_data[i], train_labels[i]) for i in range(train_data_length)]\n",
    "figure(5,8)\n",
    "plt.plot(train_data[:, 0], train_data[:, 1], '.')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, we need to define a `DataLoader` that will provide us with the training data in different batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we define the generator, which is fed with random data from a latent space. Its role is to generate data resembling the real samples. In this example, you have a two-dimensional latent space, so that the generator is fed with random $(z_{1}, z_{2})$ pairs and is required to transform them so that they resemble the real samples.\n",
    "\n",
    "We can define any type of network in the generator, given that it has sufficient complexity to generate the underlying type of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(2, 16),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(16, 32),\n",
    "            nn.ReLU(),\n",
    "            nn.Linear(32, 2),            \n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.model(x)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Generator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In turn, the discriminator is fed with either real samples from the training dataset or generated samples provided by the generator. Its role will be to estimate the probability that the input belongs to the real dataset or is a generated one. The training is performed so that the discriminator outputs 1 when it is fed a real sample and 0 when it is fed a generated sample. Again, we can choose any architecture of our liking for this network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(\n",
    "            nn.Linear(2, 256),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(256, 128),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(128, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.3),\n",
    "            nn.Linear(64, 1),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        output = self.model(x)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = Discriminator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now define our training hyper-parameters that will condition the learning procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.001\n",
    "num_epochs = 600\n",
    "loss_function = nn.BCELoss()\n",
    "optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=lr)\n",
    "optimizer_generator = torch.optim.Adam(generator.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, our goal is to understand and implement the training procedure for this simple type of GAN.\n",
    "\n",
    "***\n",
    "\n",
    "<div class=\"alert alert-success\" markdown=\"1\" style=\"color:white; background-color: #013220; border-color: #03C03C\">\n",
    "\n",
    "> ### Question 1.1 - GAN Training implementation\n",
    "\n",
    "> We divide the training procedure into several steps that will all be successively applied within one iteration loop.\n",
    "\n",
    "> 1. Generate data from random latent positions\n",
    "> 2. Prepare the labels for training\n",
    "> 3. Apply training and loss for the discriminator\n",
    "> 4. Apply training and loss for the generator\n",
    "    \n",
    "</div>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss_d = torch.zeros(int(num_epochs / 10))\n",
    "loss_g = torch.zeros(int(num_epochs / 10))\n",
    "for epoch in range(num_epochs):\n",
    "    for n, (real_samples, test) in enumerate(train_loader):\n",
    "        # 1. Generate data from random latent positions\n",
    "        # Gen 2 latent-space samples for each angle in batch\n",
    "        z = torch.randn(batch_size, 2)\n",
    "        fake_samples = generator(z)\n",
    "        # 2. Prepare the labels for training\n",
    "        # Collect real and gen examples\n",
    "        data = torch.cat((fake_samples, real_samples))\n",
    "        # Create their labels\n",
    "        fake_labels = torch.zeros(fake_samples.shape[0])\n",
    "        real_labels = torch.ones(real_samples.shape[0])\n",
    "        labels = torch.cat((fake_labels, real_labels))\n",
    "        # Shuffle both\n",
    "        indices = torch.randperm(labels.shape[0])\n",
    "        shuffled_data = data[indices, :]\n",
    "        shuffled_labels = labels[indices]\n",
    "        # 3. Apply training and loss for the discriminator\n",
    "        discriminator.zero_grad() # Reset gradients\n",
    "        guess = discriminator(shuffled_data)\n",
    "        loss_discriminator = loss_function(guess[:,0], shuffled_labels)\n",
    "        loss_discriminator.backward() # Calculate gradients\n",
    "        optimizer_discriminator.step() # Update params\n",
    "        # 4. Apply training and loss for the generator\n",
    "        generator.zero_grad() # Reset gradients\n",
    "        fake_samples = generator(z)\n",
    "        guess = discriminator(fake_samples)\n",
    "        loss_generator = loss_function(guess[:,0], real_labels)\n",
    "        loss_generator.backward() # Calculate gradients\n",
    "        optimizer_generator.step() # Update params\n",
    "    \n",
    "    # Show loss\n",
    "    if epoch % 10 == 0:\n",
    "        loss_d[int(epoch / 10)] = loss_discriminator\n",
    "        loss_g[int(epoch / 10)] = loss_generator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we look at the evolution of different losses, we can clearly see the competing nature of both networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f35f804e50>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(loss_d.detach())\n",
    "plt.plot(loss_g.detach())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can visualize the samples that can be generated by drawing some random latent positions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\garam\\AppData\\Local\\Temp\\ipykernel_10296\\3292409582.py:6: UserWarning: Glyph 8722 (\\N{MINUS SIGN}) missing from current font.\n",
      "  plt.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latent_space_samples = torch.randn(100, 2)\n",
    "generated_samples = generator(latent_space_samples)\n",
    "generated_samples = generated_samples.detach()\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(generated_samples[:,0], generated_samples[:,1], '.')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, the randomly generated points closely follow the original distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Handwritten digits generator with GAN\n",
    "\n",
    "Generative adversarial networks can also generate high-dimensional samples such as images. In this example, we are going to define a GAN to generate images of handwritten digits. For that, we will train the models using the infamous MNIST dataset. \n",
    "\n",
    "Again, we are going to make use of `torch` and `torchvision` . Notably, we will use `transforms` to obtain the training data and perform image conversions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn, optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since this example uses images in the training set, the models need to be more complex, with a larger number of parameters. This makes the training process slower, taking about two minutes per epoch when running on CPU. To reduce the training time, you can use a GPU to train the model if you have one available. The following piece of code allows to automatically select the best device available on your system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = ''\n",
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the training data\n",
    "\n",
    "The MNIST dataset consists of 28 × 28 pixel grayscale images of handwritten digits from 0 to 9. To use them with PyTorch, we need to convert them to Tensors. Also, in order to ensure adequate learning, it is always best to normalize the data. This is were we make use of `torchvision.transforms` in order to perfom thesse automatically while loading the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "transform = transforms.Compose(\n",
    "    [transforms.ToTensor(),\n",
    "     transforms.Normalize((0.5,), (0.5,))])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, as usual, we can load both the *training* and *testing* data with the datasets objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "# Load the training set\n",
    "train_set = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "# Create a batched data loader\n",
    "train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we plot some examples of the train set, to better understand the underlying data that we are working with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x500 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(20,5))\n",
    "real_samples, mnist_labels = next(iter(train_loader))\n",
    "for i in range(16):\n",
    "    ax = plt.subplot(1, 16, i+1)\n",
    "    plt.imshow(real_samples[i].reshape(28, 28), cmap='gray_r')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.tight_layout()    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementing the discriminator and generator\n",
    "\n",
    "Here, we are going to first define our *discriminator*. In our case, we will use an MLP, that will be responsible for receiving the images and trying to find which images are fake. Here, we propose the converse exercise of the previous one, as you need to implement your own architecture before training it.\n",
    "\n",
    "***\n",
    "\n",
    "<div class=\"alert alert-success\" markdown=\"1\" style=\"color:white; background-color: #013220; border-color: #03C03C\">\n",
    "\n",
    "> ### Question 1.2 - Discriminator architecture\n",
    "\n",
    "> 1. Define an architecture of your choice for the discriminator\n",
    "> 2. (Optional) Compare the use of feed-forward and convolutional architectures.\n",
    "    \n",
    "</div>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(\n",
    "        # input layer\n",
    "        nn.Linear(784, 512),\n",
    "        nn.LeakyReLU(0.2),\n",
    "        # Hidden layer 1\n",
    "        nn.Linear(512, 256),\n",
    "        nn.LeakyReLU(0.2),\n",
    "        # Hidden layer 2\n",
    "        nn.Linear(256, 128),\n",
    "        nn.LeakyReLU(0.2),\n",
    "        # Output layer\n",
    "        nn.Linear(128, 1),\n",
    "        nn.Dropout(p=0.3, inplace=False),\n",
    "        nn.Sigmoid()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 784)\n",
    "        output = self.model(x)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To input the image coefficients into the MLP neural network, you vectorize them so that the neural network receives vectors with 784 coefficients. The vectorization occurs in the first line of `.forward()`, as the call to `x.view()` converts the shape of the input tensor. In this case, the original shape of the input x is 32 × 1 × 28 × 28, where 32 is the batch size you’ve set up. After the conversion, the shape of x becomes 32 × 784, with each line representing the coefficients of an image of the training set.\n",
    "\n",
    "To run the discriminator model using the GPU, you have to instantiate it and send it to the GPU with .to(). To use a GPU when there’s one available, you can send the model to the device object you created earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "discriminator = Discriminator().to(device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since the generator is going to generate more complex data, it’s necessary to increase the dimensions of the input from the latent space. In this case, the generator is going to be fed a 100-dimensional input and will provide an output with 784 coefficients, which will be organized in a 28 × 28 tensor representing an image. \n",
    "\n",
    "***\n",
    "\n",
    "<div class=\"alert alert-success\" markdown=\"1\" style=\"color:white; background-color: #013220; border-color: #03C03C\">\n",
    "\n",
    "> ### Question 1.3 - Generator architecture\n",
    "\n",
    "> 1. Define an architecture of your choice for the generator\n",
    "> 2. (Optional) Compare the use of feed-forward and convolutional architectures.\n",
    "    \n",
    "</div>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Generator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.model = nn.Sequential(\n",
    "        # input layer\n",
    "        nn.Linear(100, 32),\n",
    "        nn.ReLU(),\n",
    "        # Hidden layer 1\n",
    "        nn.Linear(32, 128),\n",
    "        nn.ReLU(),\n",
    "        # Hidden layer 2\n",
    "        nn.Linear(128, 64),\n",
    "        nn.ReLU(),\n",
    "        # Output layer\n",
    "        nn.Linear(64, 784),\n",
    "        nn.Dropout(p=0.3, inplace=False),\n",
    "        nn.Tanh()\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        #x = x.view(x.size(0), 100)\n",
    "        output = self.model(x)\n",
    "        output = output.view(x.size(0), 1, 28, 28)\n",
    "        return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "generator = Generator().to(device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As usual, we define the training hyper-parameters that will condition our learning procedure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.0001\n",
    "num_epochs = 50\n",
    "loss_function = nn.BCELoss()\n",
    "optimizer_discriminator = torch.optim.Adam(discriminator.parameters(), lr=lr)\n",
    "optimizer_generator = torch.optim.Adam(generator.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "latent_space_samples_plot = torch.randn((16, 100)).to(device=device)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<div class=\"alert alert-success\" markdown=\"1\" style=\"color:white; background-color: #013220; border-color: #03C03C\">\n",
    "\n",
    "> ### Question 1.4 - Training procedure\n",
    "\n",
    "> 1. Implement the training procedure for the whole GAN\n",
    "\n",
    "**Note:** This implementation should be extremely similar to question 1.1\n",
    "    \n",
    "</div>\n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0 Loss D.: 0.4571472704410553\n",
      "Epoch: 0 Loss G.: 0.834632933139801\n",
      "Epoch: 1 Loss D.: 0.14890329539775848\n",
      "Epoch: 1 Loss G.: 6.042722702026367\n",
      "Epoch: 2 Loss D.: 0.22990629076957703\n",
      "Epoch: 2 Loss G.: 6.8441877365112305\n",
      "Epoch: 3 Loss D.: 0.2168508917093277\n",
      "Epoch: 3 Loss G.: 7.451517105102539\n",
      "Epoch: 4 Loss D.: 0.1954040825366974\n",
      "Epoch: 4 Loss G.: 8.285069465637207\n",
      "Epoch: 5 Loss D.: 0.29245853424072266\n",
      "Epoch: 5 Loss G.: 8.23978328704834\n",
      "Epoch: 6 Loss D.: 0.21662285923957825\n",
      "Epoch: 6 Loss G.: 11.322286605834961\n",
      "Epoch: 7 Loss D.: 0.29673606157302856\n",
      "Epoch: 7 Loss G.: 18.690820693969727\n",
      "Epoch: 8 Loss D.: 0.24276705086231232\n",
      "Epoch: 8 Loss G.: 8.304178237915039\n",
      "Epoch: 9 Loss D.: 0.17342311143875122\n",
      "Epoch: 9 Loss G.: 12.658289909362793\n",
      "Epoch: 10 Loss D.: 0.2385229766368866\n",
      "Epoch: 10 Loss G.: 9.755794525146484\n",
      "Epoch: 11 Loss D.: 0.28159165382385254\n",
      "Epoch: 11 Loss G.: 10.449706077575684\n",
      "Epoch: 12 Loss D.: 0.17382869124412537\n",
      "Epoch: 12 Loss G.: 18.90707778930664\n",
      "Epoch: 13 Loss D.: 0.15164126455783844\n",
      "Epoch: 13 Loss G.: 10.083651542663574\n",
      "Epoch: 14 Loss D.: 0.21661806106567383\n",
      "Epoch: 14 Loss G.: 27.377967834472656\n",
      "Epoch: 15 Loss D.: 0.17336297035217285\n",
      "Epoch: 15 Loss G.: 11.410223007202148\n",
      "Epoch: 16 Loss D.: 0.18424658477306366\n",
      "Epoch: 16 Loss G.: 4.9944257736206055\n",
      "Epoch: 17 Loss D.: 0.23826934397220612\n",
      "Epoch: 17 Loss G.: 15.693863868713379\n",
      "Epoch: 18 Loss D.: 0.2057780921459198\n",
      "Epoch: 18 Loss G.: 18.703989028930664\n",
      "Epoch: 19 Loss D.: 0.21660850942134857\n",
      "Epoch: 19 Loss G.: 11.569514274597168\n",
      "Epoch: 20 Loss D.: 0.26000654697418213\n",
      "Epoch: 20 Loss G.: 6.9429612159729\n",
      "Epoch: 21 Loss D.: 0.25993019342422485\n",
      "Epoch: 21 Loss G.: 14.357061386108398\n",
      "Epoch: 22 Loss D.: 0.20578579604625702\n",
      "Epoch: 22 Loss G.: 11.0562162399292\n",
      "Epoch: 23 Loss D.: 0.1435428261756897\n",
      "Epoch: 23 Loss G.: 6.903419494628906\n",
      "Epoch: 24 Loss D.: 0.2166646122932434\n",
      "Epoch: 24 Loss G.: 7.250720024108887\n",
      "Epoch: 25 Loss D.: 0.2815919518470764\n",
      "Epoch: 25 Loss G.: 10.94458293914795\n",
      "Epoch: 26 Loss D.: 0.18412868678569794\n",
      "Epoch: 26 Loss G.: 9.351357460021973\n",
      "Epoch: 27 Loss D.: 0.2012375295162201\n",
      "Epoch: 27 Loss G.: 4.194206237792969\n",
      "Epoch: 28 Loss D.: 0.22744297981262207\n",
      "Epoch: 28 Loss G.: 10.089420318603516\n",
      "Epoch: 29 Loss D.: 0.1732867956161499\n",
      "Epoch: 29 Loss G.: 35.97834014892578\n",
      "Epoch: 30 Loss D.: 0.23826932907104492\n",
      "Epoch: 30 Loss G.: 35.88373565673828\n",
      "Epoch: 31 Loss D.: 0.2057780772447586\n",
      "Epoch: 31 Loss G.: 36.789093017578125\n",
      "Epoch: 32 Loss D.: 0.22743892669677734\n",
      "Epoch: 32 Loss G.: 39.22635269165039\n",
      "Epoch: 33 Loss D.: 0.2057780623435974\n",
      "Epoch: 33 Loss G.: 32.08342361450195\n",
      "Epoch: 34 Loss D.: 0.29242146015167236\n",
      "Epoch: 34 Loss G.: 20.359113693237305\n",
      "Epoch: 35 Loss D.: 0.25993022322654724\n",
      "Epoch: 35 Loss G.: 30.279314041137695\n",
      "Epoch: 36 Loss D.: 0.18411986529827118\n",
      "Epoch: 36 Loss G.: 10.456869125366211\n",
      "Epoch: 37 Loss D.: 0.19496648013591766\n",
      "Epoch: 37 Loss G.: 7.480528354644775\n",
      "Epoch: 38 Loss D.: 0.24923278391361237\n",
      "Epoch: 38 Loss G.: 8.873055458068848\n",
      "Epoch: 39 Loss D.: 0.1733059138059616\n",
      "Epoch: 39 Loss G.: 14.699638366699219\n",
      "Epoch: 40 Loss D.: 0.17329421639442444\n",
      "Epoch: 40 Loss G.: 10.143980026245117\n",
      "Epoch: 41 Loss D.: 0.20608863234519958\n",
      "Epoch: 41 Loss G.: 9.39126968383789\n",
      "Epoch: 42 Loss D.: 0.20579057931900024\n",
      "Epoch: 42 Loss G.: 10.222749710083008\n",
      "Epoch: 43 Loss D.: 0.20578710734844208\n",
      "Epoch: 43 Loss G.: 10.523180961608887\n",
      "Epoch: 44 Loss D.: 0.2166294902563095\n",
      "Epoch: 44 Loss G.: 10.65544319152832\n",
      "Epoch: 45 Loss D.: 0.2274390161037445\n",
      "Epoch: 45 Loss G.: 11.663222312927246\n",
      "Epoch: 46 Loss D.: 0.19508467614650726\n",
      "Epoch: 46 Loss G.: 10.493858337402344\n",
      "Epoch: 47 Loss D.: 0.21660901606082916\n",
      "Epoch: 47 Loss G.: 11.026662826538086\n",
      "Epoch: 48 Loss D.: 0.17333127558231354\n",
      "Epoch: 48 Loss G.: 8.779873847961426\n",
      "Epoch: 49 Loss D.: 0.2057793289422989\n",
      "Epoch: 49 Loss G.: 14.336033821105957\n"
     ]
    }
   ],
   "source": [
    "# Load trained NN when it exists, or train a new NN\n",
    "if os.path.isfile('discriminator.pt') and os.path.isfile('generator.pt'):\n",
    "    discriminator.load_state_dict(torch.load('./discriminator.pt'))\n",
    "    generator.load_state_dict(torch.load('./generator.pt'))   \n",
    "else:\n",
    "    for epoch in range(num_epochs):\n",
    "        for n, (real_samples, _) in enumerate(train_loader):\n",
    "            # 1. Generate data from random latent positions\n",
    "            z = torch.randn(batch_size, 100)\n",
    "            fake_samples = generator(z)\n",
    "            # 2. Prepare the labels for training\n",
    "            # Collect real and gen examples\n",
    "            data = torch.cat((fake_samples, real_samples))\n",
    "            # Create their labels\n",
    "            fake_labels = torch.zeros(fake_samples.shape[0])\n",
    "            real_labels = torch.ones(real_samples.shape[0])\n",
    "            labels = torch.cat((fake_labels, real_labels))\n",
    "            # Shuffle both\n",
    "            indices = torch.randperm(labels.shape[0])\n",
    "            shuffled_data = data[indices, :]\n",
    "            shuffled_labels = labels[indices]\n",
    "            # 3. Apply training and loss for the discriminator\n",
    "            discriminator.zero_grad() # Reset gradients\n",
    "            guess = discriminator(shuffled_data)\n",
    "            loss_discriminator = loss_function(guess[:,0], shuffled_labels)\n",
    "            loss_discriminator.backward() # Calculate gradients\n",
    "            optimizer_discriminator.step() # Update params\n",
    "            # 4. Apply training and loss for the generator\n",
    "            generator.zero_grad() # Reset gradients\n",
    "            fake_samples = generator(z)\n",
    "            guess = discriminator(fake_samples)\n",
    "            loss_generator = loss_function(guess[:,0], real_labels)\n",
    "            loss_generator.backward() # Calculate gradients\n",
    "            optimizer_generator.step() # Update params\n",
    "\n",
    "            # Show loss\n",
    "            if n == batch_size - 1:\n",
    "                print(f\"Epoch: {epoch} Loss D.: {loss_discriminator}\")\n",
    "                print(f\"Epoch: {epoch} Loss G.: {loss_generator}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latent_space_samples = torch.randn(batch_size, 100).to(device=device)\n",
    "\n",
    "generated_samples = generator(latent_space_samples)\n",
    "generated_samples = generated_samples.cpu().detach()\n",
    "\n",
    "plt.figure(dpi=150)\n",
    "for i in range(16):\n",
    "    ax = plt.subplot(4, 4, i+1)\n",
    "    plt.imshow(generated_samples[i].reshape(28, 28), cmap='gray_r')\n",
    "    plt.xticks([])\n",
    "    plt.yticks([])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save trained NN parameters\n",
    "torch.save(generator.state_dict(), 'generator.pt')\n",
    "torch.save(discriminator.state_dict(), 'discriminator.pt')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
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