{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Networks (GANs)\n", "\n", "So far in CS231N, all the applications of neural networks that we have explored have been **discriminative models** that take an input and are trained to produce a labeled output. This has ranged from straightforward classification of image categories to sentence generation (which was still phrased as a classification problem, our labels were in vocabulary space and we’d learned a recurrence to capture multi-word labels). In this notebook, we will expand our repetoire, and build **generative models** using neural networks. Specifically, we will learn how to build models which generate novel images that resemble a set of training images.\n", "\n", "### What is a GAN?\n", "\n", "In 2014, [Goodfellow et al.](https://arxiv.org/abs/1406.2661) presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the **discriminator**. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the **generator**, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.\n", "\n", "We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game:\n", "$$\\underset{G}{\\text{minimize}}\\; \\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", "where $z \\sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In [Goodfellow et al.](https://arxiv.org/abs/1406.2661), they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.\n", "\n", "To optimize this minimax game, we will aternate between taking gradient *descent* steps on the objective for $G$, and gradient *ascent* steps on the objective for $D$:\n", "1. update the **generator** ($G$) to minimize the probability of the __discriminator making the correct choice__. \n", "2. update the **discriminator** ($D$) to maximize the probability of the __discriminator making the correct choice__.\n", "\n", "While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the **discriminator making the incorrect choice**. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from [Goodfellow et al.](https://arxiv.org/abs/1406.2661). \n", "\n", "In this assignment, we will alternate the following updates:\n", "1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data:\n", "$$\\underset{G}{\\text{maximize}}\\; \\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n", "2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data:\n", "$$\\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", "\n", "### What else is there?\n", "Since 2014, GANs have exploded into a huge research area, with massive [workshops](https://sites.google.com/site/nips2016adversarial/), and [hundreds of new papers](https://github.com/hindupuravinash/the-gan-zoo). Compared to other approaches for generative models, they often produce the highest quality samples but are some of the most difficult and finicky models to train (see [this github repo](https://github.com/soumith/ganhacks) that contains a set of 17 hacks that are useful for getting models working). Improving the stabiilty and robustness of GAN training is an open research question, with new papers coming out every day! For a more recent tutorial on GANs, see [here](https://arxiv.org/abs/1701.00160). There is also some even more recent exciting work that changes the objective function to Wasserstein distance and yields much more stable results across model architectures: [WGAN](https://arxiv.org/abs/1701.07875), [WGAN-GP](https://arxiv.org/abs/1704.00028).\n", "\n", "\n", "GANs are not the only way to train a generative model! For other approaches to generative modeling check out the [deep generative model chapter](http://www.deeplearningbook.org/contents/generative_models.html) of the Deep Learning [book](http://www.deeplearningbook.org). Another popular way of training neural networks as generative models is Variational Autoencoders (co-discovered [here](https://arxiv.org/abs/1312.6114) and [here](https://arxiv.org/abs/1401.4082)). Variatonal autoencoders combine neural networks with variationl inference to train deep generative models. These models tend to be far more stable and easier to train but currently don't produce samples that are as pretty as GANs.\n", "\n", "Here's an example of what your outputs from the 3 different models you're going to train should look like... note that GANs are sometimes finicky, so your outputs might not look exactly like this... this is just meant to be a *rough* guideline of the kind of quality you can expect:\n", "\n", "![caption](gan_outputs_pytorch.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 68, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import torch.nn as nn\n", "from torch.nn import init\n", "from torch.autograd import Variable\n", "import torchvision\n", "import torchvision.transforms as T\n", "import torch.optim as optim\n", "from torch.utils.data import DataLoader\n", "from torch.utils.data import sampler\n", "import torchvision.datasets as dset\n", "\n", "import numpy as np\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "\n", "%matplotlib inline\n", "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n", "plt.rcParams['image.interpolation'] = 'nearest'\n", "plt.rcParams['image.cmap'] = 'gray'\n", "\n", "def show_images(images):\n", " images = np.reshape(images, [images.shape[0], -1]) # images reshape to (batch_size, D)\n", " sqrtn = int(np.ceil(np.sqrt(images.shape[0])))\n", " sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))\n", "\n", " fig = plt.figure(figsize=(sqrtn, sqrtn))\n", " gs = gridspec.GridSpec(sqrtn, sqrtn)\n", " gs.update(wspace=0.05, hspace=0.05)\n", "\n", " for i, img in enumerate(images):\n", " ax = plt.subplot(gs[i])\n", " plt.axis('off')\n", " ax.set_xticklabels([])\n", " ax.set_yticklabels([])\n", " ax.set_aspect('equal')\n", " plt.imshow(img.reshape([sqrtimg,sqrtimg]))\n", " return \n", "\n", "def preprocess_img(x):\n", " return 2 * x - 1.0\n", "\n", "def deprocess_img(x):\n", " return (x + 1.0) / 2.0\n", "\n", "def rel_error(x,y):\n", " return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n", "\n", "def count_params(model):\n", " \"\"\"Count the number of parameters in the current TensorFlow graph \"\"\"\n", " param_count = np.sum([np.prod(p.size()) for p in model.parameters()])\n", " return param_count\n", "\n", "answers = np.load('gan-checks-tf.npz')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Dataset\n", " GANs are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable without a GPU, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n", "\n", "To simplify our code here, we will use the PyTorch MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`. " ] }, { "cell_type": "code", "execution_count": 69, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "data": { "image/png": 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ZrcxptUrJCprTihUraNCgAeCU2OJ466OOvp+p9kFd6+j8FwSmxBqGYRiGYRix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\nTkA333wzULDZgXdpfcmSJc6Frw5W//33XyDjLQ4qXpL5XkvLigHSiVB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L2KDZ0nYCm1+8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HnRMXPp0qU88MADAYwyN+jc8fDDDwPuCl06fvvtNz744APATerZdtttfRxhbpHyquPKTz/9BLjn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\n7rvvvpyc50yJNQzDMAzDMCJH1jyxfsqWLevlU6rVpzLFDjzwQ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "class ChunkSampler(sampler.Sampler):\n", " \"\"\"Samples elements sequentially from some offset. \n", " Arguments:\n", " num_samples: # of desired datapoints\n", " start: offset where we should start selecting from\n", " \"\"\"\n", " def __init__(self, num_samples, start=0):\n", " self.num_samples = num_samples\n", " self.start = start\n", "\n", " def __iter__(self):\n", " return iter(range(self.start, self.start + self.num_samples))\n", "\n", " def __len__(self):\n", " return self.num_samples\n", "\n", "NUM_TRAIN = 50000\n", "NUM_VAL = 5000\n", "\n", "NOISE_DIM = 96\n", "batch_size = 128\n", "\n", "mnist_train = dset.MNIST('./cs231n/datasets/MNIST_data', train=True, download=True,\n", " transform=T.ToTensor())\n", "loader_train = DataLoader(mnist_train, batch_size=batch_size,\n", " sampler=ChunkSampler(NUM_TRAIN, 0))\n", "\n", "mnist_val = dset.MNIST('./cs231n/datasets/MNIST_data', train=True, download=True,\n", " transform=T.ToTensor())\n", "loader_val = DataLoader(mnist_val, batch_size=batch_size,\n", " sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))\n", "\n", "\n", "imgs = loader_train.__iter__().next()[0].view(batch_size, 784).numpy().squeeze()\n", "show_images(imgs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Random Noise\n", "Generate uniform noise from -1 to 1 with shape `[batch_size, dim]`.\n", "\n", "Hint: use `torch.rand`." ] }, { "cell_type": "code", "execution_count": 70, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def sample_noise(batch_size, dim):\n", " \"\"\"\n", " Generate a PyTorch Tensor of uniform random noise.\n", "\n", " Input:\n", " - batch_size: Integer giving the batch size of noise to generate.\n", " - dim: Integer giving the dimension of noise to generate.\n", " \n", " Output:\n", " - A PyTorch Tensor of shape (batch_size, dim) containing uniform\n", " random noise in the range (-1, 1).\n", " \"\"\"\n", " return torch.Tensor(batch_size, dim).uniform_(-1, 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Make sure noise is the correct shape and type:" ] }, { "cell_type": "code", "execution_count": 71, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "All tests passed!\n" ] } ], "source": [ "def test_sample_noise():\n", " batch_size = 3\n", " dim = 4\n", " torch.manual_seed(231)\n", " z = sample_noise(batch_size, dim)\n", " np_z = z.cpu().numpy()\n", " assert np_z.shape == (batch_size, dim)\n", " assert torch.is_tensor(z)\n", " assert np.all(np_z >= -1.0) and np.all(np_z <= 1.0)\n", " assert np.any(np_z < 0.0) and np.any(np_z > 0.0)\n", " print('All tests passed!')\n", " \n", "test_sample_noise()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Flatten\n", "\n", "Recall our Flatten operation from previous notebooks... this time we also provide an Unflatten, which you might want to use when implementing the convolutional generator. We also provide a weight initializer (and call it for you) that uses Xavier initialization instead of PyTorch's uniform default." ] }, { "cell_type": "code", "execution_count": 72, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Flatten(nn.Module):\n", " def forward(self, x):\n", " N, C, H, W = x.size() # read in N, C, H, W\n", " return x.view(N, -1) # \"flatten\" the C * H * W values into a single vector per image\n", " \n", "class Unflatten(nn.Module):\n", " \"\"\"\n", " An Unflatten module receives an input of shape (N, C*H*W) and reshapes it\n", " to produce an output of shape (N, C, H, W).\n", " \"\"\"\n", " def __init__(self, N=-1, C=128, H=7, W=7):\n", " super(Unflatten, self).__init__()\n", " self.N = N\n", " self.C = C\n", " self.H = H\n", " self.W = W\n", " def forward(self, x):\n", " return x.view(self.N, self.C, self.H, self.W)\n", "\n", "def initialize_weights(m):\n", " if isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):\n", " init.xavier_uniform(m.weight.data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CPU / GPU\n", "By default all code will run on CPU. GPUs are not needed for this assignment, but will help you to train your models faster. If you do want to run the code on a GPU, then change the `dtype` variable in the following cell." ] }, { "cell_type": "code", "execution_count": 73, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#dtype = torch.FloatTensor\n", "dtype = torch.cuda.FloatTensor ## UNCOMMENT THIS LINE IF YOU'RE ON A GPU!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Discriminator\n", "Our first step is to build a discriminator. Fill in the architecture as part of the `nn.Sequential` constructor in the function below. All fully connected layers should include bias terms. The architecture is:\n", " * Fully connected layer from size 784 to 256\n", " * LeakyReLU with alpha 0.01\n", " * Fully connected layer from 256 to 256\n", " * LeakyReLU with alpha 0.01\n", " * Fully connected layer from 256 to 1\n", " \n", "Recall that the Leaky ReLU nonlinearity computes $f(x) = \\max(\\alpha x, x)$ for some fixed constant $\\alpha$; for the LeakyReLU nonlinearities in the architecture above we set $\\alpha=0.01$.\n", " \n", "The output of the discriminator should have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def discriminator():\n", " \"\"\"\n", " Build and return a PyTorch model implementing the architecture above.\n", " \"\"\"\n", " model = nn.Sequential(Flatten(),\n", " nn.Linear(784, 256),\n", " nn.LeakyReLU(negative_slope=0.01, inplace=True),\n", " nn.Linear(256, 256),\n", " nn.LeakyReLU(negative_slope=0.01, inplace=True),\n", " nn.Linear(256, 1))\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test to make sure the number of parameters in the discriminator is correct:" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in discriminator.\n" ] } ], "source": [ "def test_discriminator(true_count=267009):\n", " model = discriminator()\n", " cur_count = count_params(model)\n", " if cur_count != true_count:\n", " print('Incorrect number of parameters in discriminator. Check your achitecture.')\n", " else:\n", " print('Correct number of parameters in discriminator.') \n", "\n", "test_discriminator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Generator\n", "Now to build the generator network:\n", " * Fully connected layer from noise_dim to 1024\n", " * ReLU\n", " * Fully connected layer with size 1024 \n", " * ReLU\n", " * Fully connected layer with size 784\n", " * TanH\n", " * To clip the image to be [-1,1]" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def generator(noise_dim=NOISE_DIM):\n", " \"\"\"\n", " Build and return a PyTorch model implementing the architecture above.\n", " \"\"\"\n", " model = nn.Sequential(nn.Linear(noise_dim, 1024),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(1024, 1024),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(1024, 784),\n", " nn.Tanh())\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test to make sure the number of parameters in the generator is correct:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in generator.\n" ] } ], "source": [ "def test_generator(true_count=1858320):\n", " model = generator(4)\n", " cur_count = count_params(model)\n", " if cur_count != true_count:\n", " print('Incorrect number of parameters in generator. Check your achitecture.')\n", " else:\n", " print('Correct number of parameters in generator.')\n", "\n", "test_generator()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# GAN Loss\n", "\n", "Compute the generator and discriminator loss. The generator loss is:\n", "$$\\ell_G = -\\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n", "and the discriminator loss is:\n", "$$ \\ell_D = -\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] - \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n", "Note that these are negated from the equations presented earlier as we will be *minimizing* these losses.\n", "\n", "**HINTS**: You should use the `bce_loss` function defined below to compute the binary cross entropy loss which is needed to compute the log probability of the true label given the logits output from the discriminator. Given a score $s\\in\\mathbb{R}$ and a label $y\\in\\{0, 1\\}$, the binary cross entropy loss is\n", "\n", "$$ bce(s, y) = y * \\log(s) + (1 - y) * \\log(1 - s) $$\n", "\n", "A naive implementation of this formula can be numerically unstable, so we have provided a numerically stable implementation for you below.\n", "\n", "You will also need to compute labels corresponding to real or fake and use the logit arguments to determine their size. Make sure you cast these labels to the correct data type using the global `dtype` variable, for example:\n", "\n", "\n", "`true_labels = Variable(torch.ones(size)).type(dtype)`\n", "\n", "Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing." ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def bce_loss(input, target):\n", " \"\"\"\n", " Numerically stable version of the binary cross-entropy loss function.\n", "\n", " As per https://github.com/pytorch/pytorch/issues/751\n", " See the TensorFlow docs for a derivation of this formula:\n", " https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits\n", "\n", " Inputs:\n", " - input: PyTorch Variable of shape (N, ) giving scores.\n", " - target: PyTorch Variable of shape (N,) containing 0 and 1 giving targets.\n", "\n", " Returns:\n", " - A PyTorch Variable containing the mean BCE loss over the minibatch of input data.\n", " \"\"\"\n", " neg_abs = - input.abs()\n", " loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()\n", " return loss.mean()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def discriminator_loss(logits_real, logits_fake):\n", " \"\"\"\n", " Computes the discriminator loss described above.\n", " \n", " Inputs:\n", " - logits_real: PyTorch Variable of shape (N,) giving scores for the real data.\n", " - logits_fake: PyTorch Variable of shape (N,) giving scores for the fake data.\n", " \n", " Returns:\n", " - loss: PyTorch Variable containing (scalar) the loss for the discriminator.\n", " \"\"\"\n", " N, _ = logits_real.size()\n", " loss = (bce_loss(logits_real, Variable(torch.ones(N)).type(dtype)) +\n", " bce_loss(logits_fake, Variable(torch.zeros(N)).type(dtype)))\n", " return loss\n", "\n", "def generator_loss(logits_fake):\n", " \"\"\"\n", " Computes the generator loss described above.\n", "\n", " Inputs:\n", " - logits_fake: PyTorch Variable of shape (N,) giving scores for the fake data.\n", " \n", " Returns:\n", " - loss: PyTorch Variable containing the (scalar) loss for the generator.\n", " \"\"\"\n", " N, _ = logits_fake.size()\n", " loss = bce_loss(logits_fake, Variable(torch.ones(N)).type(dtype))\n", " return loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Test your generator and discriminator loss. You should see errors < 1e-7." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum error in d_loss: 0\n" ] } ], "source": [ "def test_discriminator_loss(logits_real, logits_fake, d_loss_true):\n", " d_loss = discriminator_loss(Variable(torch.Tensor(logits_real)).type(dtype),\n", " Variable(torch.Tensor(logits_fake)).type(dtype)).data.cpu().numpy()\n", " print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n", "\n", "test_discriminator_loss(answers['logits_real'], answers['logits_fake'],\n", " answers['d_loss_true'])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum error in g_loss: 3.86398e-08\n" ] } ], "source": [ "def test_generator_loss(logits_fake, g_loss_true):\n", " g_loss = generator_loss(Variable(torch.Tensor(logits_fake)).type(dtype)).data.cpu().numpy()\n", " print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n", "\n", "test_generator_loss(answers['logits_fake'], answers['g_loss_true'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimizing our loss\n", "Make a function that returns an `optim.Adam` optimizer for the given model with a 1e-3 learning rate, beta1=0.5, beta2=0.999. You'll use this to construct optimizers for the generators and discriminators for the rest of the notebook." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_optimizer(model):\n", " \"\"\"\n", " Construct and return an Adam optimizer for the model with learning rate 1e-3,\n", " beta1=0.5, and beta2=0.999.\n", " \n", " Input:\n", " - model: A PyTorch model that we want to optimize.\n", " \n", " Returns:\n", " - An Adam optimizer for the model with the desired hyperparameters.\n", " \"\"\"\n", " optimizer = optim.Adam(model.parameters(), lr=1e-3, betas=(0., 0.999))\n", " return optimizer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Training a GAN!\n", "\n", "We provide you the main training loop... you won't need to change this function, but we encourage you to read through and understand it. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, show_every=250, \n", " batch_size=128, noise_size=96, num_epochs=10):\n", " \"\"\"\n", " Train a GAN!\n", " \n", " Inputs:\n", " - D, G: PyTorch models for the discriminator and generator\n", " - D_solver, G_solver: torch.optim Optimizers to use for training the\n", " discriminator and generator.\n", " - discriminator_loss, generator_loss: Functions to use for computing the generator and\n", " discriminator loss, respectively.\n", " - show_every: Show samples after every show_every iterations.\n", " - batch_size: Batch size to use for training.\n", " - noise_size: Dimension of the noise to use as input to the generator.\n", " - num_epochs: Number of epochs over the training dataset to use for training.\n", " \"\"\"\n", " iter_count = 0\n", " for epoch in range(num_epochs):\n", " for x, _ in loader_train:\n", " if len(x) != batch_size:\n", " continue\n", " D_solver.zero_grad()\n", " real_data = Variable(x).type(dtype)\n", " logits_real = D(2* (real_data - 0.5)).type(dtype)\n", "\n", " g_fake_seed = Variable(sample_noise(batch_size, noise_size)).type(dtype)\n", " fake_images = G(g_fake_seed).detach()\n", " logits_fake = D(fake_images.view(batch_size, 1, 28, 28))\n", "\n", " d_total_error = discriminator_loss(logits_real, logits_fake)\n", " d_total_error.backward() \n", " D_solver.step()\n", "\n", " G_solver.zero_grad()\n", " g_fake_seed = Variable(sample_noise(batch_size, noise_size)).type(dtype)\n", " fake_images = G(g_fake_seed)\n", "\n", " gen_logits_fake = D(fake_images.view(batch_size, 1, 28, 28))\n", " g_error = generator_loss(gen_logits_fake)\n", " g_error.backward()\n", " G_solver.step()\n", "\n", " if (iter_count % show_every == 0):\n", " print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count,d_total_error.data[0],g_error.data[0]))\n", " imgs_numpy = fake_images.data.cpu().numpy()\n", " show_images(imgs_numpy[0:16])\n", " plt.show()\n", " print()\n", " iter_count += 1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well that wasn't so hard, was it? In the iterations in the low 100s you should see black backgrounds, fuzzy shapes as you approach iteration 1000, and decent shapes, about half of which will be sharp and clearly recognizable as we pass 3000." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter: 0, D: 1.378, G:0.6873\n" ] }, { "data": { "image/png": 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UoHBn+PDhKby5jzmt389cW21fUmsRMvPIqCrPxhtOmTIl0UUxg4AfAhN5eB0o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\nSrsKCZ966qlcD1gqYa56OusXWY3MtdXWSqxFAthWW23ViGj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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 250, D: 1.376, G:0.7614\n" ] }, { "data": { "image/png": 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LqOrkqVP5ZzU3crgZy7N582aXG47mxU8aVsaRrhERfQwFYWZyS8nICmElcT6M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\nyXu5aZMceum5DpsjELTSijZzRERfwjZjZrKGYDALYq+//e1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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 500, D: 1.332, G:0.793\n" ] }, { "data": { "image/png": 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1uu222yR5Ox+mIluM30ML4fXhw4dn7fHKK690MVwaBlI2yWjfSgG2JCuRbD7O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\nvBIbMymLi/2ydo6ZphZ4SYx0DbWeUj3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 750, D: 1.314, G:1.014\n" ] }, { "data": { "image/png": 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4O75gmt7df//9kvy829vb9bOf/UxSV5M8yeuRBx10kCS/y8OuzI/0QKzH5Vhz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\nKUmuBzdSAM8BrYjof1wOah0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wZ8KArNEyyywjyc8HieP888+XJE2cONGdv1iqYBLsrm/9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\nEVEn+P/Ki5u7902VtgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1000, D: 1.275, G:0.7455\n" ] }, { "data": { "image/png": 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actYIFxIjwFcmgk5+l5+Tnt8n55KTLNvECTtwj4iiu8L+Admegt+7An3cE6rJ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\n5RRJ1u/dZpttJEmHHXaYJOvrNzc3m4q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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1250, D: 1.255, G:0.7701\n" ] }, { "data": { "image/png": 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Zs2YVTYpvBrqSmbsLfU26KobEzAkJvQQN1ZkHDBhQ1sxeygDVKFRTmrWv7erO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\nc/vttzf9HPk/XT2/TTfdNPu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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1500, D: 1.295, G:0.7588\n" ] }, { "data": { "image/png": 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v0tyXLJVGwliaJvDvvfde7HWbE6Wu4fjx4yXJy6bCX44ezLPL50ESCJ+NGzdO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\nCF8suuKoUaM8vdX+DmWBsHb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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1750, D: 1.344, G:0.7526\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2000, D: 1.31, G:0.7399\n" ] }, { "data": { "image/png": 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STJo0KSv4zrsv/1WIUKvcrL+mQPYX4lV95MiR3ZCP8VqB66+/HgjFM6YdDfaZ\nqjzooIMAOOGEE4DGZ1APGTKk+8vf1f1entlRldDV1ZX7+dZUNujWCBIzJyR0GAoxc5FVL4YN71rR\nAzqvNK40ANaqVrtfNQwUn7lk/0Bvn7IZdNo9rIbEzAkJgwQt9ZmHDh1aqBSwEvKkk4qgkigl+ntH\nreqD/RyrnZ8zrJxpNpBQb754YuaEhA5D0Va7CQkJAxSJmRMSBgnSy5yQMEhQaNbUVyV44uBr5wDV\nwmALgMVdX/IGwGqJfRqp5lKTXaSTa97j6XThTzX0mza7ERQdwZkH/fkgxJF29b7xfOom91H2INTT\nCrQ6mxDD0kYVaM0qBr/87oB5meNmHK1AimYnJHQYmhrp2opVtRbUaFuq1kpGbuW2GkWc+24FI1ve\nV23wfHyv4vLRIuNc8hyH2/PnWBNezdrKs6+11lor9/G0C14bz0+3wjZBMnRfZI0SMyckDBJ8JXxm\nVTuqeFqJRv2tRitu+hqtCIA1grgZo9dprrnmAkKrHpk5tgyKDJbvD5/Z4zYG4ED2r33ta0AIRMrM\n1uS/8sorhfeVfOaEhA5DUz5zO1BaK6ofFTd3k0X8fSNN0vJg6aWXBkKL31IMdEauhwrpndyfteJJ\nLbEjZuzQsc8++2SjXeKGd3GqSt/SbbQjot8O+GxusskmQHhGrrrqKiDorT0P65kd02Sr4SK10fUw\noM1sX1Z7gmm6xZ0Qi5yDD6AXeyClNdqBVhRaOO3ib3/7W9m/mprC+zVmzBhOOeWUst/ZdMAunHk0\nADFcVHRxXAD6+h4OHTo06zZq84FnnnkGCGW5LlIuak899RQQiMd/i86froVkZickDBIMODO7FAZB\nHnzwQQA23HBDoGflh8CuRUxev5NQHV53Ec+fivuWC03KGWaYgeWWWw6ALbfcEujdJK8IYjNfRu4v\nzDjjjJkqTpfDBo5eg/322w8I5rfPaHwu00wzTcMquRiJmRMSBgkGNDPbSteZQK5qth6ycZ/BhFbD\nSX9OqoQwd3fbbbcFQvtfAzf6Sk6qdCrCr371KyAfM9nH2QmJfQ194Wpa7Hp+3tNPP52laNzW2LFj\ngXBdZP88rYir7a+ZyZGNwJjNnHPOmVket912GxB6bGtF/vGPfwR6z5aKg4iffvpp1tSw2UBuYuaE\nhEGCARnNlhFcvWocT9P7anUkNJ71KzPrO3788cfccccdQGAnV3kxZcoUIEwabIah+7JtkOczYsSI\nbD7Y+PF/RawTAAAZY0lEQVTjgcBgZiJ22WUXIKRqirTWjdFX0ezFF18c6LEy5plnHiBYakbvFYXE\nVk01+fC8885btw1vimYnJHQYBhwzlybRqx2bfpf5xmaQZ1U3cjlixAi23357AM4999yyz5x55pkA\n/Pd//zcAU6dOzb4Dwce++uqrc1sU+lDLL788EGZar7/++rm+D33DzJ6j1+nzzz/PmMm2yM899xwA\nf/3rX4EQ93Dm2EAugfQcHLJw/vnns/feewNw7bXXAvRq6Vx6LUr/HqP0Wah2DRIzJyR0GAYMMxv1\nGzVqVJ9GL4us6ksvvXRFaSeEldec4XbbbQcE5c+LL74I9BSNfO973wOCOkp/yqi5MtKJEycCZCxw\n3nnnAeWR0CLnB9XPsZHGD/rI0003HQC33nprdrzxdVIZZaFFM0zs+ct67WJmz+/YY48F4IgjjgB6\ncsnmuueff36geiTaqL6D8vIgvheJmRMSOgz9lmeOlTDmZz/++GOuvPJKIPiZAwVGLitBH1p2uuKK\nKwBYd911AVhiiSWAHhWb4nxzkSuvvDIQIr02Y1Ct5njTJZdcEqhc+NEsGlFmyZDmX8866ywAbr/9\n9uze6RMbU2gF2t1Ywmdz7rnnBgIjl8Jots+vo2nNRAh17EXQ6PklZk5IGCToN5/5jDPOAODAAw8E\nyvOMrkyxRjivz6zvqYKrFor6W9V8S4/Vf43aqny68MILs88utdRSADzxxBMAvPHGG0BgghhW5rz2\n2mv1Dq8X2hnNlrEWW2wxINzDu+++O4u8H3PMMUDomOp3YiVUnjxzXw3/MxL95ptvAr3Hxtx77738\n+Mc/BkJMZL755gNCnMPn4PnnnwdglVVWAUJJaBEknzkhocPQdmautppaRWM+1sjuq6++mq3i1bbV\nSrQrEmqE1/MTw4YNy1jIOMEll1wCwEYbbQQE9tffVkXVSA1wO5nZhot33nknEHz9ZZZZJssz21hQ\nDbt+v4xcq5FgXrT6Hsqqqrk8TzF58uQs93/ppZcC4bw233xzIMRMVHetvfbaQGMjbPMyc9sDYNUW\ni5KwOxAki5VeZB/uetPyBhLil1iUXg/Tceuttx4QztOX3QJ40zqKF9qBRsT+W2+9NRBkjhZNTD/9\n9NnLu+qqqwIhRePCpADD4Jm9sgZC11ShNFeXsJSY4hfdQJfXQHN7woQJAJm8VSlvO5DM7ISEQYK2\nmdn15iLH2GmnnQC46KKLev3NooUVV1wx7+7r4utf/zoAr776ar+1DfLaxCamzKxJpsleZJtaOvPP\nP3/bRCNCgcw999wD9KTnHn30USCc2+9//3sgBP2Uxfqdhx9+uOL+a/XIMjA4adKktpjZpgq1jLRa\n3n77bdZYYw0gMPBhhx0GwOGHHw6EIJrWl80LdEXyoOT5SAGwhIROwoCRc9YaENYO6Kt98skn2U7G\njh3bDXDUUUe1bb8Qgj8GtGSAcePGAbDrrrsCYRVvJkYQB0+GDRvWDc35pqb8FlxwQaCn8ABCEPOD\nDz7IGiwYA4lbDWlt7LXXXgCZUMjmjUVQeo6jR4/uhmJijfg5sz/7SSedBIRmfbLusssum6XenC1l\nsYlMffLJJwNBLHT99dcDIVhYBCk1lZDQYeg3Oac+WrWobzOIx5pWQqUihXYzcryfuGWwpY1GrdsR\ntW9FtNhrZ2RWpjbCO9NMM2X9oxXC6G9aoBD76O+++27ZPkz11JLQVkIj8kmhtabc1riDkWiPdf/9\n92eRRRYBwjPmc+w10Uf2mtx1111l+2rHnLbEzAkJgwT97jMrvVRAAWG1iuWc7UAlwUERiWEjiAvZ\nnYx4wAEHAKFxeiOoNYj8y793V/pcI/u47LLLANhqq63Kfn/QQQdl1pFRXpnYfy1MkEn9rvlofec8\n96BR0Uh8DbSIFLrIvgpAzKpss8022TaU5irwMUZg/tlJlUUsonr3sBoSMyckDBL0m8+sL1TKyMIc\nXX+hXYwMPc3Q4+jp8ccfDzTHyCIv0zbjqxm5dSiBlswtt9wC9LRz+vnPfw4E60pm8tyvvvpqAP70\npz8BcOSRRwKhZW1pk7tqVkSzlpuRaBvX22jAAgv9/vh5vOOOOzJ1nnENo/M33ngjACeeeCIACyyw\nABAKLvKg0XuTmDkhYZCg333mCuVsfeIrl+yvrr/lqBEb2TeC0sbycc7Vlb+VkU3RjkILI9TmmX/3\nu98BsMMOOwA9qif9SyPf+qNGrT3n+O/+XGQETZ57WBoHKW3QCKGE04YKtjjWH46Z+bPPPsvyyUbp\nZfn/+q//AoKyrVr8p0hhSfKZExI6DE0x86GHHgoE/yAPXL0vuOACIKyO4rzzzmOPPfbIvb2iUCO8\n6aabAn3XQN185Oyzz54xspHPds56rsfMRTTZ3ivVXZdffjkQWOjpp58GevTXNjBUm/zNb34TCL6j\nbCdDxT516XMpq/lZGyLqtzZ6D2Xr0aNHA6GFs3nl3XffHQixAX3rkSNH9vKztTjaMVc6MXNCQoeh\nz5oTCCPFrra//e1vgTAczrravkK7mVm/y7GoQ4YMyXKSNvRrZw1vIz5zzI5GamXTH/zgB0Dwa43g\nau1cccUVWcWXbK4vHDdtaEWcoNl76Hl5H6zOs9HiNddcAwR114QJE7Iqv0YaRhRFYuaEhA5D25nZ\nldjV3sZn+lmOwHzggQcAsvxkX6FdzKwvevPNNwPBzyuFVVIqqJqB4170TUW8qg8ZMqT7y9/X3WbM\nqkItdrwNa8RffvnlXuyuyq0daPQeqsX2/KpFnD0Xq6kmT56cVVK1w0eOkZeZm3qZLXkrcqPi2cOa\nNI888kjubbQSpRfqkEMO6YZQvtYMNN282aXuhoGWZZddFghi/HagL6dANtPgoBm0ekGOXUPfkfjl\n7yskMzshocPQ76KR/kazq7qruO6E0yeE0kDF+08//XTGyCXHUHS3uVGUmWu16cmLZrbRCLsXuYet\nOL++RmLmhIQOQ78VWohmSvEUuduCtz+Oyc/IyLaQta2MzCzTTJo0KQssVYs1VGOndpdmQstSRQ1/\nt93+9leNlYsgMXNCwiBBS33mIUOGNDWZoNVYaKGFsplP1dBXcs7+Qjui2e1oeVMEsbxzIN/D+Fjz\nIDUnSEjocBRi5oSEhIGLxMwJCYME6WVOSBgkKJSaaiS4oF63kUHhfYFWiUYGqrvSyu6c1b5jlw3T\nc93d3W25Ls0MW691PPHfGglaCUcVx+KhInCSiR0++0Sb3d9Q/9zIzFsxkNRD7cgjF41mTzvttJn2\nOC40KNlm2e+rDcCrBHPoceukPNe1mZdZbLzxxtxwww1199Uo2rGIpWh2QkKHoSkFmKuqq20p+qKC\nphlGbgStMEkh6LhtBmdTdRs1tGJ1r9cU0b+XNhqEHlWa+/czJfnOsm1U+30t1GoPVA9x9ZJtl2rB\nck2HLbSClWtZaK1k5AsvvLDQ5xMzJyQMEnylfeZWoNkAWL1gSewHDx06lM033xyA3/zmN0AIIB1x\nxBEAnHDCCUAYYu640yK+qag20jXeRqXnoJlAUAyPfZZZZinbtv75nnvuCcAvfvGLXsdQb/8DQQHW\nzkBo8pkTEjoM/VY1VWsls4OJXTpsUr7bbrsB8JOf/KTsc3PNNRcAjz76KJDPl2oV6q3EMrLnO3Lk\nyGyYmL6zqQhbFu+9994AXHLJJWX70L9thilL9Mxlx1WKeoxcj4Uq+ZRu04FytlGyne1GG20EBGvE\n8TWzzTYbU6ZMybXfUrQjM2C122effZbLsulrtN3MjgNh5puvvfZaAH74wx8CIQBz3333sd566wGh\ntNG+xosuumjZZydMmFC2j1o3zikM8fzevsozn3vuuQCsuuqq2WLzl7/8BYCzzz4bCPO3NLv92W3H\nbZrypMpa0Zwgfrl9qOOGDAamVlhhBfbaay8g9Mm2x5v9tO0B57k89NBDQJj+6D5POumk7P92wmx1\nocXCCy8MwAsvvAD0Lq31vM4880yg59l1rlYrX2I1GT7vDz/8sPtIZnZCQieh5Wa2Eyuc3RubI5rM\nroK//OUvgRD8WWaZZbIV3xVRxnWVv/jiiwHYeeedy/5eCzEjN4tqjBwLWWaaaSagZzIiwLzzzpt9\nRoWPkKVkp1iU4d9lpkUXXTSbItHK84nPLb6HThxxOojuj9d4jTXWYJ111gF6JpRACOLJ6h988AEQ\n5jHbN90Amdu6+uqrs4kf9umuJmapd17itttuA2D99dcHwnPlsWn6OwVSS0nLcN11182O2++0IgWr\n1dqoWjIxc0LCIEGfp6Zc3XbddVegx78CWGWVVYAe/1fmcWK9QZEzzjgDCCmdJ554Ij4+oFxj2+q0\nRp7gDwS/y37WMnR3d3fGBIsvvnjZd+LgVB7U0wJXS03F4o3SY4h/FwfLllhiCSDEIWyb7JSHUaNG\nscsuuwAhniG7xdvSCpHp4okn77//fhbg9JhjS6zoPbQ3+09/+tOyY5JlDZ55nk4eKZ0G6WdKjgEI\nz7eWZyvSeslnTkjoMPQ5MzvRQr9AP2jUqFFAzwqngMBpELEP6cwfV0EZIm5S/t5772VyvmpoVwN1\nfUD9SvHBBx9k0Wz96mqFDPUwdOjQur5aK6LZcUYijmVoHTin2cEGAFtuuSUA++67LwC33norEKYm\nHnvssWWfu/POO4Fwb1977bVs+9V8+Fr3MJamzjHHHFlUPL7+1UQ59913HxCaNeaZH57Hn8+LxMwJ\nCR2Glkez60V5zd25OsrIFh08//zz2Sp9/vnnl23DbSo4iJvJO/PZKYX1WLkeihSLeN7uW8ZxFTd6\nu9tuu/UqEImvVcx8QoZy5nGpoKJVKI0zxEyoT+n90W900qVxgn/+85/8+te/zv4PIQLuNVUYo1jE\n50Ery3OfeeaZs7iKeVdRif0qNMMr+7dSVqNawUicmcjDtp73PPPMAwSrsi+QmDkhYZCg5T5zNSmg\nDGwpmvN5zSlvv/32ANx///2ZusacdL19NNOkoFU+8+yzzw6EHKaRao9RRhoxYkTuMkCnDqp0c/Kg\n519qMeQp3P/yc3XbJWsZxE36jVA7a9osg9FmrZJTTz01u59GjI1jOOvYaZUO0fO5UMLreUw//fSZ\nJVINeXxmtzfPPPPw+uuvl/0uht8xm2Kcx1hAJYbWAjFP3koknzkhocPQcp85ZmQZ6Uc/+lHZ72Vk\nP68+9p577qnKyHG0MW6KYGRc363V4vdaWmijs7JaLPTXR6wUgY5Xev2teeedFwgRYNHKhg9xBuCL\nL76oOjZH5Z1+reyqln7//fcHeuZNez+33XZbAE455RQArrvuOoDM19eaii21yy+/HIDNN9+ckSNH\nAiGuIvJEleO+Xnl8WL9joYuW4rrrrgv0lKj6rK288spAGAzYn0jMnJAwSNBneWZXXldZo5f6uzLD\nMsssk/mfVhU9+OCDAGy66aZA8LP0r9QB33jjjWX7yINW+cyPPfZYdvxfbsvtA0F1NHbs2IxZZSV9\nMZVhVpStscYaAJn+es011wSCmqqW3txrMHXq1JaNdNVS8Hrvt99+QKjy8T4tuuii2Vigc845B+ip\nFoOg1TYOoAVjTEXGK0U93/aLL74odA/rZSm0Kmwioe88ceJEoOe587k1NjBu3DggKNleffXVmsde\nBMlnTkjoMLStOYFso1orbjigb6TKy9zl7LPPzo477giE5mv6kHfffTcQqnRc/WQMt6mG+4ADDmiJ\nf6ml8Pbbb2e/kxU8r/HjxwNBz+vKrX9vfe/o0aM55JBDgJAXX2655cqOXyvFnLz7UN/t32uxaOxf\n1kOtYn7/ZsRWTbzX3ePScnrjjTey+y+b+W+cgfDnamN5hw0bVrUqrlHWqxezUIFoPXlcxTRu3DjO\nOussIDzn5tq9lz67xgYa0WjHVXX10HIz25tkwMHuH6ZqhEEuzbHSCxrfPM1OyyQXWmghIHTo8CW4\n//77gXAR/HstlJowQ4YM6f7yd2WfKe1SWZoyAVh66aXLfrZ8brPNNgNC6Z+yziWXXJINN9yw7Dx9\nINy2gSbdBtM7xx9/PBBSRs8880y2oOU5P2gsvei98d4uueSSQHApbLxQCboSulUHHnggEDqJxEHN\nIg99SYqurpldmr6MF64NNtgACM+RLqGLuPfDbTzzzDOZkEXz+rvf/W7Z/nSZttpqK4CmSlWTmZ2Q\n0GFouZntSixj/d///R8QUhPi2WefBUJC3tROJYHA97///bKfXfX8rObe2LFjgSDW//jjjwvJHatZ\nKaVsYfBEdyFOScmaMvRJJ50EBFNt3333zdrPHHbYYWX70TRW4ihDaNYaJFKY4jE0g0piE/8fM5if\n9d5pYXjPZa7PP/+cN998Ewj92NZee20gCE4M5tmN04BYnjLW2ETPg1JBUWxm6+opfvHYLr30UiAw\ns+7d+eefn5VFxvO/3baW2ltvvZX7GGMcffTRhT6fmDkhYZCgkM+8wQYbdENIoueBK5SFBvohlsut\nvvrqubclqpWq6b8suOCCQAhK1ELR1JR+msIJj2HOOecE4N577wUCE9vATj/+uOOOywQRFi7EjQVs\nvuC1mn/++YFQinf44YcDPQUn+tV5zg9g1KhR3RDuRyVUk8d67qaivHdaI2+88QbQw1weu9aD18nu\nnMcddxwA3/nOd4DQrEIm0wIy2FcJlVJTI0aM6K73vXrbk6G1IgxAagFOO+20meWhlWUAz3tl2s6C\nm4MPPhgIVk6ckq2F5DMnJHQY+kw0cuihhwIhVK84QHlfI9DHdOXUX7Gtq6KLWmhUNGLva4UbrtBa\nBYo29Bm32GILoKdYweZ+MpurtcyrEMF4grGBV155BQhy1SlTptRtZpg3ml3qq8YST+F1Fp6HFoZi\ni3HjxmUpOqPWWi5mN2yn5HWxmYOCIP81bpD3HFs50WKfffYBgg/tfVl44YV55JFHgBBXsDxT2a5x\nHXu8G/nXAtFirZaSK0Vi5oSEDkPbmVlBgY0CzDfqU9XLk1aC/qqlbLLKH/7wByA0NdAvr4VG5zPH\nc4aNOJtPlt1sB2x0e8SIEZmo5aKLLgKC0ESfdJNNNsk+C4GdlApaLJBHEFONmfM0748naMTnPmbM\nGAAWWGABAJ566imgx3f2OzaQsKWyegMZabXVVis7R60tnxuFKpVQK8/caCumUujfxxHpUaNGZT6z\nGQbzyd5/YwVmJmzwZ07enPaKK65YeJBBNSRmTkgYJOgzn9kcsLkzBfgyVhHUywe7Yudp89KsvyUD\nu2+VbUaZ/b0MNH78+EwWKvs4Y8oot3lcS+6MM8gQ1Rrb1Ts/qH+O00wzTRbFNr+vNNE4gP7st771\nLSBEpm0NNHXq1ExnIDNZAuv1Mg6gX27mQYaupLorOQegbLZ0n0+B1Fc292wu2uP32PSVtTDMbsSz\nr2shMXNCQoehJQowI7b6wxBWTwvXVQCpr1Z4H69M008/feZv6BOqBbZ8LoaRUdvq6L+0Cvr7trYp\nhcxidFLfyLas5oyNjA4fPjzT6RrBPe2004AwQE7fOM7z5mmP1Oic4NLhe0ap9W89dgtejNyaO1fl\nZo71mWeeyXLRXjOVcvrXXoMDDjgACPp0S0lrtQry3Fo54bEo3LfWikowc9E+u1pfPu+tbMEbIzFz\nQsIgQUuYuZSRhavnSiutBIQIs4zlyh3jww8/zKK9RgxPOOEEoLeP6M8ywumnnw40pv6phUqMHEMV\nlyuv18Smd9tssw3Q42vZakaVkAxY77gbaVhYDTFDVIqMy6Y2pLdZnZaTg9e0mI466iigh23jHL++\nopVfyy+/PBBy51odMnKjFkZfoLQpow0Mqs0Et5LPzISNJWop8Bo998TMCQmDBH02bF1/S9+hFmRc\nfVVztVbYqPCyVtgi/0ZW8Xaph+Jmcyp/Ro0aleW/9R9t4BC3s2lHy5lqNduiq6url/5ca8oRM0aq\nb7rpJiA0HJR13nzzzewztgvyHmqxxDGV66+/HgjWR61zj62Kom2DWgGPQYWXqjwbT1gteMwxxwAh\nI2EmoNaAhgqN/FM0OyGhk9Dng+P0HY3ymnN1SPsdd9zRSwNcsn+gtX5Uu5hZGM00drDppptmVTl/\n/vOfATj55JM9lrJ/W4G8CrDS4WnmUI1FOKrVVrqyq2xjq5ySfWbblZFlXv1tGdh9+fsi97ik8qhP\nmbmrqytT7ZXWzkOwtrRu/H0zo13zMnPbXmYDA3kklUVRZAZUPeRpG9QMlK3a6mj8+PHZg9zqQF0l\nxA/CzDPP3A3B3BOVeoDZTueuu+4CQhrGgoNTTz0VCDJOgzzPPvtsJg6ydZNmpS+rQcDbb78dqD5f\nuug5Dh8+vDs+j1Zj+umnzwhHoYxBW+WoupVKjvviZU5mdkLCIEHbZk0pUje9NFDRaKFFo+jq6qra\nXKEdKBoAK4Xs+dBDD5X9vpqJXiqu8W8KUJQzxvOSNVftkR6jUtugGI26Sgo8ihb7zDLLLBX7e0No\ntmA5a2w9anZXcyUrITFzQkKHoc8DYAMN7Q6AxWgFuxdBXmYeaCKNatNEK6Gv72EeFDn+ekjMnJDQ\nYWjbRIuEyuhv5qu2/1YeV624QGwBVGtRVNqA30J+I961pm8UQSuzIjGKMHIz88VLkZg5IWGQoJDP\nnJCQMHCRmDkhYZAgvcwJCYME6WVOSBgkSC9zQsIgQXqZExIGCdLLnJAwSJBe5oSEQYL0MickDBKk\nlzkhYZAgvcwJCYME/w+lkPNuuwUL9QAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2250, D: 1.353, G:0.7564\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2500, D: 1.362, G:0.7879\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2750, D: 1.381, G:0.751\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3000, D: 1.379, G:0.7309\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3250, D: 1.366, G:0.775\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3500, D: 1.354, G:0.7631\n" ] }, { "data": { "image/png": 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IIYcAbt45DdJS5qjyzHxz31HvjdonFyq0SYops2HUGan4zIpgf/rpp0Fk1q8E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\nrkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3750, D: 1.369, G:0.7178\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n" ] } ], "source": [ "# Make the discriminator\n", "D = discriminator().type(dtype)\n", "\n", "# Make the generator\n", "G = generator().type(dtype)\n", "\n", "# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\n", "D_solver = get_optimizer(D)\n", "G_solver = get_optimizer(G)\n", "# Run it!\n", "run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Least Squares GAN\n", "We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alernative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:\n", "$$\\ell_G = \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[\\left(D(G(z))-1\\right)^2\\right]$$\n", "and the discriminator loss:\n", "$$ \\ell_D = \\frac{1}{2}\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\left(D(x)-1\\right)^2\\right] + \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[ \\left(D(G(z))\\right)^2\\right]$$\n", "\n", "\n", "**HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`scores_real` and `scores_fake`)." ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def ls_discriminator_loss(scores_real, scores_fake):\n", " \"\"\"\n", " Compute the Least-Squares GAN loss for the discriminator.\n", " \n", " Inputs:\n", " - scores_real: PyTorch Variable of shape (N,) giving scores for the real data.\n", " - scores_fake: PyTorch Variable of shape (N,) giving scores for the fake data.\n", " \n", " Outputs:\n", " - loss: A PyTorch Variable containing the loss.\n", " \"\"\"\n", " N, _ = scores_real.size()\n", " loss_real = 0.5*torch.mean(torch.pow(scores_real-Variable(torch.ones(N)).type(dtype), 2))\n", " loss_fake = 0.5*torch.mean(torch.pow(scores_fake, 2))\n", " loss = loss_real + loss_fake\n", " return loss\n", "\n", "def ls_generator_loss(scores_fake):\n", " \"\"\"\n", " Computes the Least-Squares GAN loss for the generator.\n", " \n", " Inputs:\n", " - scores_fake: PyTorch Variable of shape (N,) giving scores for the fake data.\n", " \n", " Outputs:\n", " - loss: A PyTorch Variable containing the loss.\n", " \"\"\"\n", " N, _ = scores_fake.size()\n", " loss = 0.5*torch.mean(torch.pow(scores_fake-Variable(torch.ones(N)).type(dtype), 2))\n", " return loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before running a GAN with our new loss function, let's check it:" ] }, { "cell_type": "code", "execution_count": 64, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Maximum error in d_loss: 0\n", "Maximum error in g_loss: 1.45919e-07\n" ] } ], "source": [ "def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):\n", " d_loss = ls_discriminator_loss(Variable(torch.Tensor(score_real)).type(dtype),\n", " Variable(torch.Tensor(score_fake)).type(dtype)).data.cpu().numpy()\n", " g_loss = ls_generator_loss(Variable(torch.Tensor(score_fake)).type(dtype)).data.cpu().numpy()\n", " print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n", " print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n", "\n", "test_lsgan_loss(answers['logits_real'], answers['logits_fake'],\n", " answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter: 0, D: 0.3726, G:0.4073\n" ] }, { "data": { "image/png": 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HEEjqmxD3cQaUzQ4eIJ1HgUaMGJGUUEqGyID+l80CMjHCCOJWz549sx+oJ7qk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\noJ8rrbRSHr+DrqLNrkE0Iq5dddVVTfqHBs6bNy8FR9TdBgP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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 250, D: 0.7319, G:0.3646\n" ] }, { "data": { "image/png": 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kTYSgXjjG0SRgQbv7+P8+ffrkXtGuK9wzvjY8+NGPfhQRxVZLtq0R7vbv3z8d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\nvElCxObWUJsCakDh3ZUUysIjCo0RflKCffbZJ5tgbDEjiuD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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 500, D: 0.2219, G:0.2428\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 750, D: 0.2219, G:0.1592\n" ] }, { "data": { "image/png": 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BxIdn/McjFemoY5SFwJTZMCJCwWxm7b/17t0bqD1j/fe//wVg++23Z8KECQAc\nfPDB2V4uY8K2mfV9KopInvYnn3wy48/Q/vKtt96aa3NCtZm1QlCfDjjgAMDZu/LsrlixIq6UCzhl\nlqprvOUFzqXIX9AxlLL6Peva41bbZ86cGfd7xWw3b97cs/kzOR6ZK2YzG0aZkXdlViyu7CsRRvLz\nMAirPE0YqJTLTTfdFNpnhqHMTz31FOBWVffccw/gIqKkyDoB1aRJEx599FHARfopDbLOAY8YMQII\nZ/xzHUPZyIq31r91Sky+C6VHqq6uzmtUoh9TZsMoMwpmM/tT4YRZllVMnToVgD322CPjvwnLZlY/\n0nniE6FItnRFxLI5PRWmzXz66acDLkZAe8TyXMciT7BWYLIxP//883TtVTszbldYY6iVhU54nXba\naYDzd8QmqW/SpAng7OhMeeuttwCXzSQTMlVmqqurM/4PqM73f/Xr18/7NWL/K3T/qmsuVA1UV1VV\nVVdVVRWsf4Xq41lnnRXpMVy4cGHR+pfqP1tmG0ZE+FPkzc4npXgEMkzykZwgHfXq1fOWrPkwp/yU\n2xgmw5TZMCJC0Y5AGtFFqgyZK3IhFDzqmDIbRkQwZTZKAlPk3DFlNoyIEMibbRhG6WLKbBgRwR5m\nw4gIgRxgUd+Qj3r/IPp9VP9SbXX92bbBMg0aMW+2EUlSPahhPsSlcpQXbJltGJHBlNkwssC/VE9W\n7K+QmDIbRkQo2sO87rrr1ioeFiUqKipyTt5fp04dzyYzCs+AAQMYMGBArbHs0qULDRo0oEGDBt7v\n/GNVt27duP9EGPdFMuxOMYyIUNLnmZVYbZ999gFcWh2lb1F6l1zsk3xvTaXydl544YUA3H///YBL\nN9u/f3/AJcXPhWJvTSVLLeRP0ZMLuY6hlNJvB/sLG6h0zvz5873Xpk+fDsBDDz0EuILzd911FwBn\nnXUWUDuVcGwiwHRbZZluTRX9YdYSRDf7H3/8wXfffQe4WkznnXeerg+4L3f48OGAq1KfDfl+mBMN\n1JZbbgnAt99+C8Bll10GuBteOae22247wOXNymbSKvbDLJSdVdlahXJwKw9aNgQdQ//D659o/ctg\n5fxWHvdJkyZ59ackLMoFtvfeewOw//77A65yi+qQ+7N5VlRUpB1XS05gGGVGwZX5jjvuAJwaaRbU\n7Dd79mzvvZrF5s+fD8Brr70GuFpTffv2BVyVvnHjxgEwcuRIwC2BUpGtMidbGin38sKFC+NeV7WE\nKVOmeMo1x3tpAAALn0lEQVSsipjqp/r+4IMPAq7G0TbbbAO46otawi1evDhtsEIxlLmqqoqOHTsC\nrvqnVlOqJiGF1jJb98X5558PuFrIn376adrrBR1DrRJUuVEVOdQ23T9nn3024KpCdu3aFYCtt97a\nq+4xZMgQAH788UfAVb9UPWaNj7LSqpKk+j1nzpxay/pU/UuFKbNhRISi2cxSNuUfViXBJk2a0L17\ndwDeeOMNoGYrAOCcc84B4L777gOcffLZZ5/FfWaQukX5spmlRLvtthsAw4YNA6Bp06belpxyKLdp\n0waARx55BHCKoO9B9taSJUsA5zzy10pKRCGUOZntmQi9x7/lptefeeYZAE466SQgeB/9/UtWVyq2\nDVo16b6RH2errbYC4JJLLgFg0aJFAAwaNIhdd90VcCsHrSS0GtR9rWofv/76a9w1YtpuNrNhGPEU\nTZlVt1dVAgcOHAhAnz59ks70qm2kLRtVVpCdtXTpUsDZRNraSkWuyqxts8mTJ8e9rooOsh01k/fv\n39+zI2VfCc3eRx99NADXX389AKecckrc76XMUopUhKHMuq7a7UfjpHtp+fLlNGjQACAuYCIW/9aU\nf6y1kunTp0/a9qUaw0S+Df9raqO/bvakSZMAVwNszpw5QE11SKm5dlS23357wHmx1T/9W7ayvN+6\nR1euXOld12xmwzCAIiqzKuxpr7Vx48YAPPDAA95srNk9WR1dzeb+/efYmkDpCNtmlo2mGr/aR5XH\neuHChd7e49NPPw3ARRddBDil7devH1DjFQZXy1gVFfWZmZBPm1nqKRs/FX7183twL730UgAGDx6s\ndmbcjiBj2LRpU8/TrPtF19LYqU6Y7Nt7770XgBdffBGoiX9QYI/a7X+O9Nna3Vi2bFnctXQ/rFmz\nJvCORDJMmQ0jIhRcmWUryKbQHp/2UjPxRMvOknLLpguiyCJsZZaqjho1CoBjjz0WgKFDhwI1+6t+\nb2ls9BvA2LFjAbj44osB51eYMWNGreulOxyfD2WWuh522GEAPP/884Ab29goJ/VRVUClVH5UPXOj\njTYK3J4gYxir+FJi/3cof4d2SW699VYALrjgAu/vdM8lQ/2Qn6FVq1aA28HQyhTcfZvs3jdlNowy\no2DJCTQjVlZWAk5NtWesqK7bb7+d3XffHXAz1oYbbgi4KBupemwle8Dbv83Eyxs2mmmlMFKiQw45\nBHAz9BVXXMFtt90GOH+BgvSl6tp3lid85syZgNsBiI2KKka6mgULFgDw0ksvAW6PVUr3wAMP0KtX\nL8DtkUuRFc3Xtm1bAObOnQs4JZOqJ/OC50psLLS+O11T0Vyq760YAcU5yA/SsGFDPvroo7i/Vfu1\nr6x+qh9NmzYF3GGN5cuXAzUx3WEdczVlNoyIUDBl1mzoPzVy5plnAm5PtUWLFrVs359++glw3t9u\n3brFfaYohiILtVm28o033gjApptuCjiP9Lx582jUqBHg1PyWW24BnAJrlp8wYQLgVjWZxCkXAq2U\n9P3L1tOKKdb2087DAQccAMCrr74KuJWZ32udL0VOFAmma/uPae65556A81XoGKNs5tWrV9e6j3WP\nanX18ssvA06ptarUPRq7px3W6sqU2TAiQsG92ZqpdUZX+3T69+rVq/niiy+A2sqrv5W3V7ZMLoTt\nzZZPQJE/77zzDuA88GuttZZnP8pe1Gkc/dQ+qFYtYSVfgHD3mf3Rb7Eqq4goKZP/FJmUKQwlztab\nvckmm8S14dxzzwXguOOOA9z9NmDAAABGjBgB1NyjyaILd9xxRwDefPNNwCWgePbZZ+PeJ5t5zZo1\nocVmF/xhzuRguBxgugHkVLr88ssBuOmmmwB3jC7WzR+UsB9m9Us3gpZjsaGDCgvUclsOJU1ocprp\niOc999yTdXuKlZwg3X2l70lJCYIEwiS4VqAx1JJbP7U0lgmg7USFaspEir1X/YEmmqw1hnqvTKb9\n9tsPgA8//BBwJknDhg29yTuT/qXCltmGERGKHs6pJZoOb6+33nqeImvZqWW1tne0daOlTC7kS5m1\n3dCjRw/ABU/07dvXS7LgP/Su70A5wbS9kQuFUOZjjjkGcGmeEiGTQqGSWrmEQbYHLXRwR1tSWiJf\nffXVgEvv5D/OunLlSm/FpcClrbfeGnDHdE877TQA3n33XcBt0SkARff/rFmz0gY7mTIbRplR9IR+\nQiF0chhB7TC7G264AYC7774bcG7+I444AoDx48cHvm6hCsdNmzYNqAmWUL80a/fs2RNwyqCEfv6D\nJKWe0C9RdQe9JmXWYZF04ZABrxvIAeYP0pBKKo2QUlpJMZWW6tprrwVqbGy/70cKLf+H/Dk65qoV\nie5zje2SJUvsoIVhGPGEoszZVMKT909ePTFu3DgeffRRAMaMGZPw8/W38gY3bNgQqJ2bORPypcz+\nmVvbH3PnzvXaLTta3mp5UcOkkMqcaIdC6ib1y0c1h0zGUHb9mDFjah320f2kNuo4rrZNlUBR21HH\nHnuslx9bATJKEySbuXfv3oDz8ygVr1Zfen3p0qWWNsgwjHhKxmYWp556quflVdim0Ka+KlvocLyC\n9v0ETTBeiP49+eSTXr8UPCL7Sh5QpUOSYiSzLzMJBSzGPnPsdz569GigdlrhMMM2g46hVnr6ftUm\nhd6qbQrsUGIBpaVq2bIlP//8M+CUWYEmCnW98sorAafYSkDx+OOPA27FUllZ6Y2vP0Q0Uf9S9iuT\nNxmGUfoU/Aik9u607+aPpJk5c6Y3M2om1LGxTp06ATVH7MApsj9VqihGjVy/59kfCda6dWsv/YwO\nTiiNkA5WKNQxnce3GMcfg6J0wVLmfB2kyJQ6dep4bZAy6/5SOSClQVK6X3ngZVN/8MEH3upQaq5w\nTa0qFU2mZICy2aXIuk8ysZkz7lson2IYRtEpmM2s68g+lOdW8dbaKx4xYoQ3u/m92JpRlQxeXuFc\nCMtmlqrqcIQOGMjTLk/osGHDvCN2Onqng+5K6i8V03FKv9oH2XcO02bWyidd9FaidikmWaupbHZA\nUlwv0D6zf3XgT1ivsbrqqqsA57vRPbvtttt6961eU8EGrcA6dOgAuHRBUmR9tvqdTSGDZJgyG0ZE\nCN2A0Yzrn501QylVjuxhRT/FHgnzexvvvPPOuM8OQ5GzJZkqdu7cGXBx1uqn9he1Bz58+HBOPvlk\nwPVDBeH02VJk4b9WMXwB4BRZ+7BaIQm1a8qUKV6cuX+/XRTa3o8dt+bNmwPuntPvZN+qCIF+L0+1\nfB1Tp071jrgeeuihgCtho1WWEjhKkXW/+9NEh4kps2FEhLzbzJqBlBJHNoMOr8sOkzd74sSJXmSU\nomj8pV/CJFebeerUqYCLKVeyPq04lARO53a///57LwpIZWdkb6Ur7ZkNYdjM/vZorJS0UOfPY9VP\n/6/VVTZpkAO0L6vkBPJOK8WPii8okaJS/eje1Tn0tdde29uVUWnXL7/8EnDnBfS3+q5kS2ejyGYz\nG0aZUXBvtuxEnVmWvagzvs2aNfNsmqAJ7FSsXGqYYbtyUmbN9IoS0rlWJYb/+OOPAbxY3s6dO3Pg\ngQcCbg9WiePSoYL0mb4fwvV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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1000, D: 0.2519, G:0.5202\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1250, D: 0.1873, G:0.1615\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1500, D: 0.2349, G:0.1759\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1750, D: 0.2361, G:0.1633\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2000, D: 0.2453, G:0.1532\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2250, D: 0.2494, G:0.138\n" ] }, { "data": { "image/png": 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VVlbmqZTfnswkxDFd8qHMyZII/jhe0IerRtBzKGesVPaUU04BXDBPIjS/8emp\nQWHKbBglRt62pvSElgLLTtS2kwIpli1b5m3dyOubbpmgYizsp7EvWrQIqAxfff/99wFo37494IJj\ntOJQkoJCQYudRMX3NuTeYH67N5Uii2HDhgFu6ykMha4JU2bDiAihKbPsQNlRUh2/vSullhr/8MMP\n3u86d+4MVCZfAIwbNy7lMf2KHIvFAg+wuPDCCwF4++23AZg4cSJAtcJ6KtansE55qgcMGODtZy5e\nvBioXgRQ51GMK414Ro4cCcA555yTl+NlUyA/HebMmQPArrvuCriApu+++y7pe6TA8n8ojbWQmDIb\nRkQITZnl3VPROiUJ7LzzzlV+lldbXt+VK1d6duZmm20GZN5rKX6vN+hwToXhqUCAyuEKhWaqXKvU\nS+rbokULrxBhonKyADfddFMgYw0LXdN8KbKutWIUgsJfBENccsklQPVSyKJ169bVVlMikw6kQWPK\nbBgRIXRvtp5gskfktW7RogXgoruOOeYYABo1auTZirJDZFumS7wKh5WUoNRG2fNKZZPaKtFCze7U\nlqVu3bqezScV39BQOeFkBOnJrl27tqfIQXajjEffyQEDBgDOzyF72L///8knn3irK/+1CDMGvSZM\nmQ0jIuRtn1kew9deew1w8a/qraySOGvWrPGevFI7ZVgls53zleYYi8U8O1FeesVe69iy/fV7eelv\nu+02oLLgn19ZlCYpH0GxotWIzi0fKE0UglNk//fLX9JHxfr8aYoqytimTZukq5OgmsBlgymzYUSE\ngpUNkm2pp6Dir1u1asWf/vQnAGbOnAm4kkJSML03k/Yhycg2rlcF67X3KUXOBGWH1VQEPReCjM3W\nOc6YMQNwDcfF0KFDAbj88svTGZfGk/J1LVq0qHElkO0cqhFhu3btgPRK+PhRlJ6KMt54441AsMUH\nLDbbMEqMghf0UzUHPaH33HNPWrVqBcCf//xnwHkVt9tuO8AVmg+CbJ/qGq+qoOg6KnooRRMwPvnk\nEwCvqXxNbWdzIQhlltd9++23B+Czzz4Dqnt7k7WPCZugCvq9++67gMs9l78jFcqwkq08b968TA9f\nI+kqc8Fv5vHjxwNw9NFHA5VfhDA7WPjJ9YugbabddtsNqO5MEfqCNG7c2Eus2GOPPTI9XMbkIwUy\nrC2jDI4fSFcS1WffcsstE75OW2RDhgyp0jssbGyZbRglRsGUOYwlczaEVZwg2XZZLBYLtGBfTRS6\no0U+CGvfHqkSAAAL3UlEQVQOd999dwBmz55d5ff77ruvVyl26dKlQR0uKabMhlFiFEyZC1XP2k/Y\nZYMKjSnzho8ps2GUGAXraFFoRTaMqGHKbBgRISOb2TCM4sWU2TAigt3MhhERMnKARd3tH/Xzg+if\n44ZyfplUXk13a6pg3mzDKEUUXxFG9J8tsw0jIhRcmZU6plY0hlHsxDf/86PsOaVC+gv2h7l7ZMps\nGBGh4PnMhWZDdJ6ofI+KIqbCHGDh4M9jVold5UKfdNJJgCvgP2bMGMAps9oTp4PFZhtGiRG6Mitv\nWW1cii3ibENU5kwoVmVWqSQpXHxJ3UzJdQ41FnmYNSbZxSo6qZ9XrFjhKbFWSWrhqsYNasvUrVs3\nwNnOKoV17733VjlmKopmayrT4gMbbbRRtYndkHv9ijDrfBUj+pLrfFUB86OPPgJcrbS77roLgLPP\nPhtwy099yY8//nimTp0KuM4TQeOfEwlOy5YtAfcdju+TtssuuwCu7JU6lki8VCddY+7fv3+Vzw5D\n1GyZbRgRIe8OsHQiX6TE6vAgJ0IYhLXM1pjV0XKnnXYCEvcW1jXRXCTb9siGQiyzy8rKPMVVPelk\nq6vTTz8dcOf+wAMPALD33nsDMHz4cK+3dTKCnkPVz/bXZdc8tWvXzqsNrp5p2pL69NNPATzllkPs\nzTffBGC//fYDMgsaMQeYYZQYRbk1lagIXojHyump7neWiIkTJwLOVmzatClQWXNatpf/PFXoT5+l\netWy6bKxs/KhzFKyU045BYBRo0bV+B7VG1ewkK7P999/D7juJb/88gudO3dO+VmZzmGqYouJfu9n\n4MCBbLLJJoBzaEnFpd5HHHEE4OztJUuWZDweYcpsGCVGwcM500HKJI9wMSEV1dNVhdTHjRsHQO/e\nvau8TrZzqs/yP6FV8lWdMIoFeXs1rlT9tnRO6lut/lHqtyU011tttRUAv//+e4AjrjqWdH8vBg4c\nCMANN9zgbUWpM6n6j8uLvWjRIiC1Iqd73HQxZTaMiFB0NvPkyZM9L7Y8msViM8disaRPUYVWys71\n27vpMGzYMADOOOMMwAUr+PtW+W3rVIRhM2tcUuLvvvsOcCunxYsXezZwut+vESNGAG6/WbZnOnMf\nduBP/fr1Aby2QmvWrOGiiy4C4JtvvgHcCmPlypWAG39NIbepvlPCbGbDKDGKzgjt2bNnoYeQlL/9\n7W+el/q0004D4K233gLck1gRTtlEek2aNAmASy65BHCqpO6E++yzDwBXXnklALfccktew2OlyM2a\nNQPgqquuApwiS4UuuuiijMel/Wjx+uuve79X6GO+0fXff//9AejevTtQ6WE/55xzALfPrHgCnXe6\n+8hBzp8ps2FEhKKzmVetWkV5ebn/uKEdL1N7S2NTJJf2SWVXXXDBBQDcfffdaY9Bqi7Fe+KJJwC3\nR619XF0HqYGSV1IRhM08d+5cwHmvxaOPPgrAMcccA7h+xrIbEyF7X4kK77//PuDinidPngxAr169\ngMwTEYL8jipZQisj+QiWL1/ujVvjTLYSS2dP2/aZDcOoQtEpc61atao95YpFmRs2bOhl9UiZNbY+\nffoALosmG9So3a+A8oxrb3P48OEAnHnmmRl7QnOZQ0V29ejRA4B77rkHgI8//hhwCp4oU04rGqUI\nXnbZZUB1T73U76CDDgJgypQpNY4r1Rxm06BQPgCttqTMikrr37+/F0/w448/VnmvMv7UmF2RbvJ2\n62ftRKxfv9682YZhVKXolBmKLzZbNu369es9G07/6m96UityKR2kuHqaK+d15MiRQKXyxv/9kUce\nAWD+/PmAszNTkYsySz20UtDKQeOR2ipDrE2bNkBlxNrLL78MQN++fQEYO3asf1waT8KfpfLKPkpF\nUDazzqdTp04A3jnous+cOROABx980MuGkt9Cnv5DDz0UcEUJrrjiCsCtqvSvmrUnKsrgvyZFU5wg\nU+JvZH8KWqHQjbt27VrPGeVPZdQyShORLAGjX79+PP300wC8+OKLgFvO7bHHHlVeqzTCY489FnAP\njkaNGuV6SmkR/xBL9G/Hjh0Bl4A/a9YsoHI7SctnnavQUlVL2ZtuugmAGTNmAHiFCPKJHqpyyumG\n0xL6X//6F+DSNWOxmDffX375JeC+Iwrr1ANQN62+N6pGm2rrMtvtKltmG0ZEKLpldvx4gnAqpXG8\nrNLn/Iqr5ZbKxigJXc6hxYsXA/DMM8+w9dZbAy4IwR/6p6e4kIpdffXVQGWgP6Su35zo/P4Yf9pz\nqCQRJY34zR1ty0llpGzl5eWeIu2www6AS5j4/PPPAbftpnMrZA0wqaXCaJUsIfNBhQfk/GzYsKGn\nxFqVaA4V5qmEGoUkn3DCCYDbilPZpNq1a9d47uYAM4wSo+hs5niSKUIh8QdqaCUhFTv44IMB5yzR\nVoSS2VetWuUpmrZwWrVqBbgtHzle/KGBug6ymf3bIkGh4+ictF2kkj4nn3wy4K6FgkZmz57tvV+K\nLKTae+21V5Vj5KLI2VC/fn3Pnldwi+ZDaqrrrSCYV155BXAFJwYNGlRtRaT51ZaUVF6rFqWJahUg\n4leimSTQJMKU2TAiQtEosz9QAqoHFBQD22+/PeC82W+88QYAxx13HOACOmRLffbZZ4BToHXr1nnb\nWLKZpVIKjxT+bRo97bViCQupxfnnnw+4lcKBBx4IVNr9ANdff32V8aVCXmxt2Ujt8s2KFSs877Wu\nr1YNmlNt/SmJ5u9//zvgEi4aNGjgqbo/rFcrMl0rbU0pKUV/33PPPYFKn4HUO9dCjsV3txiGkRVF\n7c2OO26Yx8vKEyql9Xdm8Cdi+Nl22209W1feUX+JXf/5yoZTMMM777yT7jBz8marbKwSDqRoWnUo\nUELjS7SS+vDDDwF47bXXALj00ks1rnSHUSOZzqF8FDo/7TQoyWPw4MGA211Yvnw54Oa0Tp063mdo\nb12lnaSySprReSqlU3vY8b2makomMW+2YZQYRWMzJ6LY+lLFI5vnrLPOApzHN9mY9feFCxcmfY2U\nd9q0aVV+rz1MKbL8C+oBHCTxZWwUgqk9UamOPLaKTLv//vsBF7pZVlZGgwYNAGcjSv0KPad16tTx\nvPGKX1Aao7zX8n9ITTVmeaSffvpp72/yG0iptQIZNGgQAEcddRTgItsU1ahwUF2XIDBlNoyIYDZz\nljazbGN5QlU2JlnMbTp7iNqDlI3mf+91110HOJuuSZMmNT7Zc7GZtSesgn2yGfWvkiDat28PuASR\ndevWcfHFFwNO1f/xj39UeW+QZDqHmjOVI9JYtW++dOlSACZMmAC4aLXmzZt7/2oetXuhFZqK/mmu\n5OXWHKqwhGK6Mz2/VJgyG0ZEKBplTuTlE9mUrU2XYurPrBWIVF7XRLHaWg3oaZ/O3OWizLIhVbBe\nZWWVeil7UKmaikuvX78+t99+OwA77rgj4NQvDBLNYaqiBLqubdu2BSrbxoLbR9ZKQ6sIxWg3adIE\nqIy8U8SX9s/lE1EKbLIdikSx/VY2yDCMKhSNMive+IMPPvBsk7jjhnXYolJmeYD1tJcS6npkcx1y\nUWZlPmkloL1TNSnQXrtWTop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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2500, D: 0.2395, G:0.1386\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 2750, D: 0.244, G:0.1394\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3000, D: 0.2525, G:0.1413\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3250, D: 0.241, G:0.1506\n" ] }, { "data": { "image/png": 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VElnjJEVfuHAhjz32GODUfcSIEYCzntR8Us3uFQdRVZmsrbZt2zao\nAvNbdeYzG0aFUTBl1myjnLAi0PIllH+On8E1M2l2U0RQSKGkDEJ+id8nSkauyqxoplY46bxVpRUU\nA2jZsqX3HaiRgZbYqaZZSqF2Qvn4VYVQZtUyazVbPPfffz/gIvLF2vwvmWWgSLRa3coCUi5YOWL9\nfu7cuUB9XbWi14qMa+z0HZx00kmAu7/VEkp1CcmaNARZc6bMhlFhFG3jOEX/8mnOp6iu1j+rQVs2\nZKvMWtsqP0ezqdrnqj5ckejbbrsNcG2SvvnmG6/FzOWXXw64qPnIkSMBtz1N0AZ52WwgVwhllurJ\ngrj66qu9PGuUvrLIZgzjK8B0blJo5Xy1Hltqu+666wLOmmzatKk3NrI49ZmvvvoqAHvssQfgxso/\nltlYW6bMhlFhlMxm65olf/3118AqK3+UL4xZP1tl9ueXdQ7KJyrirnPUzKyVOP379/deq89QzbV8\n5TCb3hVCmeUXqql/165dvU3FtUoufluesMl2DHUfaS248vryVaXIilkob66Kw/jPUKxHfraa4GtN\ngO5l/5hmU4eQqTIX/WEuNmF151SKSn2r1bzAf+NUV1d7N72fKHpLF2OhRTLyaT6RjlwfZgmJv/ho\n3LhxCT/V41rjtu2223p/U6DziiuuAPJfxpgMM7MNo8Io+P7M5YqKSIzkRKHIuVBVVeWdS9ACHRV2\n6Gf//v0Bl4bs0KGD507JilJAN5vzgHBTdabMhlEmlIzPHKVPlYqwfOYMjqNjNPibf5YOc9YuFZ85\nSvIdw0IvBgFXipxsl1A/5jMbRoURuTL7W++UGvnO6v6yzlLDlLmeVJafSnCDsgzJKKSamzIbRoWR\nlTIbhlG6mDIbRplgD7NhlAn2MBtGmWAPs2GUCfYwG0aZYA+zYZQJ9jAbRplgD7NhlAn2MBtGmWAP\ns2GUCf8PYia8RfpcHTIAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3500, D: 0.2368, G:0.1453\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 3750, D: 0.2431, G:0.1514\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n" ] } ], "source": [ "D_LS = discriminator().type(dtype)\n", "G_LS = generator().type(dtype)\n", "\n", "D_LS_solver = get_optimizer(D_LS)\n", "G_LS_solver = get_optimizer(G_LS)\n", "\n", "run_a_gan(D_LS, G_LS, D_LS_solver, G_LS_solver, ls_discriminator_loss, ls_generator_loss)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# INLINE QUESTION 1\n", "Describe how the visual quality of the samples changes over the course of training. Do you notice anything about the distribution of the samples? How do the results change across different training runs?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in the TensorFlow notebook, the network outputs similar images for different noise inputs in the beginning. After further training, the number of classes it starts to generate diversifies." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Deeply Convolutional GANs\n", "In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like \"sharp edges\" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks \n", "\n", "#### Discriminator\n", "We will use a discriminator inspired by the TensorFlow MNIST classification tutorial, which is able to get above 99% accuracy on the MNIST dataset fairly quickly. \n", "* Reshape into image tensor (Use Unflatten!)\n", "* 32 Filters, 5x5, Stride 1, Leaky ReLU(alpha=0.01)\n", "* Max Pool 2x2, Stride 2\n", "* 64 Filters, 5x5, Stride 1, Leaky ReLU(alpha=0.01)\n", "* Max Pool 2x2, Stride 2\n", "* Flatten\n", "* Fully Connected size 4 x 4 x 64, Leaky ReLU(alpha=0.01)\n", "* Fully Connected size 1" ] }, { "cell_type": "code", "execution_count": 75, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([128, 1])\n" ] } ], "source": [ "def build_dc_classifier():\n", " \"\"\"\n", " Build and return a PyTorch model for the DCGAN discriminator implementing\n", " the architecture above.\n", " \"\"\"\n", " return nn.Sequential(\n", " ###########################\n", " ######### TO DO ###########\n", " ###########################\n", " Unflatten(batch_size, 1, 28, 28),\n", " nn.Conv2d(1, 32, kernel_size=5, stride=1),\n", " nn.LeakyReLU(negative_slope=0.01, inplace=True),\n", " nn.MaxPool2d(2, stride=2),\n", " nn.Conv2d(32, 64, kernel_size=5, stride=1),\n", " nn.LeakyReLU(negative_slope=0.01, inplace=True),\n", " nn.MaxPool2d(2, stride=2),\n", " Flatten(),\n", " nn.Linear(1024, 1024),\n", " nn.LeakyReLU(negative_slope=0.01, inplace=True),\n", " nn.Linear(1024, 1))\n", "\n", "data = Variable(loader_train.__iter__().next()[0]).type(dtype)\n", "b = build_dc_classifier().type(dtype)\n", "out = b(data)\n", "print(out.size())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the number of parameters in your classifier as a sanity check:" ] }, { "cell_type": "code", "execution_count": 76, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in generator.\n" ] } ], "source": [ "def test_dc_classifer(true_count=1102721):\n", " model = build_dc_classifier()\n", " cur_count = count_params(model)\n", " if cur_count != true_count:\n", " print('Incorrect number of parameters in generator. Check your achitecture.')\n", " else:\n", " print('Correct number of parameters in generator.')\n", "\n", "test_dc_classifer()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Generator\n", "For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. See the documentation for [tf.nn.conv2d_transpose](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). We are always \"training\" in GAN mode. \n", "* Fully connected of size 1024, ReLU\n", "* BatchNorm\n", "* Fully connected of size 7 x 7 x 128, ReLU\n", "* BatchNorm\n", "* Reshape into Image Tensor\n", "* 64 conv2d^T filters of 4x4, stride 2, 'same' padding, ReLU\n", "* BatchNorm\n", "* 1 conv2d^T filter of 4x4, stride 2, 'same' padding, TanH\n", "* Should have a 28x28x1 image, reshape back into 784 vector" ] }, { "cell_type": "code", "execution_count": 83, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "torch.Size([128, 784])" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def build_dc_generator(noise_dim=NOISE_DIM):\n", " \"\"\"\n", " Build and return a PyTorch model implementing the DCGAN generator using\n", " the architecture described above.\n", " \"\"\"\n", " return nn.Sequential(\n", " ###########################\n", " ######### TO DO ###########\n", " ###########################\n", " nn.Linear(noise_dim, 1024),\n", " nn.ReLU(inplace=True),\n", " nn.BatchNorm1d(1024),\n", " nn.Linear(1024, 7*7*128),\n", " nn.ReLU(inplace=True),\n", " nn.BatchNorm1d(7*7*128),\n", " Unflatten(batch_size, 128, 7, 7),\n", " nn.ConvTranspose2d(128, 64, kernel_size=4, stride=2, padding=1),\n", " nn.ReLU(inplace=True),\n", " nn.BatchNorm2d(64),\n", " nn.ConvTranspose2d(64, 1, kernel_size=4, stride=2, padding=1),\n", " nn.Tanh(),\n", " Flatten())\n", "\n", "test_g_gan = build_dc_generator().type(dtype)\n", "test_g_gan.apply(initialize_weights)\n", "\n", "fake_seed = Variable(torch.randn(batch_size, NOISE_DIM)).type(dtype)\n", "fake_images = test_g_gan.forward(fake_seed)\n", "fake_images.size()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Check the number of parameters in your generator as a sanity check:" ] }, { "cell_type": "code", "execution_count": 84, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Correct number of parameters in generator.\n" ] } ], "source": [ "def test_dc_generator(true_count=6580801):\n", " model = build_dc_generator(4)\n", " cur_count = count_params(model)\n", " if cur_count != true_count:\n", " print('Incorrect number of parameters in generator. Check your achitecture.')\n", " else:\n", " print('Correct number of parameters in generator.')\n", "\n", "test_dc_generator()" ] }, { "cell_type": "code", "execution_count": 85, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Iter: 0, D: 1.349, G:0.2757\n" ] }, { "data": { "image/png": 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fY0wsas9lg4fWe4+2zrvuuisvioYOtNCSTk0D2ia9Hp1cZZVV0txxnMpLyhZ+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\nzdOA4TXQz5ZQjDsy56677srFEViIlkjmHYTGetBOSyHJoY8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 250, D: 1.413, G:1.069\n" ] }, { "data": { "image/png": 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OmapClEGyp3UoEFWVE4zkL8mVMs1NmzatoTKknyiuXRCXIQq23Xbb1fV1wQUX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\ngw8+yKjIaqutBjhb8aGHHgo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EjcRS+0hm33LLLQFYZpllgMZ6Ui+//HKmzdX8jz766Lp+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\nQXqZExI6BOllTkjoEKSXOSGhQ/D/AM9rihhtZCdWAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 500, D: 1.32, G:0.9724\n" ] }, { "data": { "image/png": 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EV/hvivzadNNNge5ywIpx125BLXOKzAG3I9GkU2v3Ist/R0dHYomp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\nBZ9xPQthJ7ZvvvnmXVDdH1tdQjo6OoJ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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 750, D: 1.329, G:0.5586\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1000, D: 1.341, G:0.9498\n" ] }, { "data": { "image/png": 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znwI7vvvd7wIu4EAztYwIhx12GOC6IWjp8/777wcd9p577jnABVcoJNCWYEki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\nCvUME1lyWMPhnEOGDClDfAx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1250, D: 1.325, G:0.8399\n" ] }, { "data": { "image/png": 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YaaNXC2Hicy6Xy5t4kjluttlmgCn6oM2utL/7778fMCmQlX6JfWV9SnqGYZDR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\notoJNArvLGcPaQ2SFpho2U/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1500, D: 1.295, G:0.8097\n" ] }, { "data": { "image/png": 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P0L1QUJA6QbhzU1ivWwmznAEtbI4KbNL3XCNea2ur+Z3CT8PcomHnEfVSTQNG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\nHhdXugY/HxUOG1XQT/YAd9z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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n", "Iter: 1750, D: 1.307, G:0.7924\n" ] }, { "data": { "image/png": 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ZE6yNXGhiL5UPCl8PXApeFul2mqPcMfoZhoJD7rvvPsDpg3Irnn766UXj00+x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\nkfRTu5h0y6Rob28PmEFuK52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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "()\n" ] } ], "source": [ "D_DC = build_dc_classifier().type(dtype) \n", "D_DC.apply(initialize_weights)\n", "G_DC = build_dc_generator().type(dtype)\n", "G_DC.apply(initialize_weights)\n", "\n", "D_DC_solver = get_optimizer(D_DC)\n", "G_DC_solver = get_optimizer(G_DC)\n", "\n", "run_a_gan(D_DC, G_DC, D_DC_solver, G_DC_solver, discriminator_loss, generator_loss, num_epochs=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# INLINE QUESTION 2\n", "What differences do you see between the DCGAN results and the original GAN results?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The digits look much smoother, less noisy. They look almost identical to the digits in the MNIST dataset. Using convolutional filters improved dramatically the performance of the network." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extra Credit \n", "** Be sure you don't destroy your results above, but feel free to copy+paste code to get results below **\n", "* For a small amount of extra credit, you can implement additional new GAN loss functions below, provided they converge. See AFI, BiGAN, Softmax GAN, Conditional GAN, InfoGAN, etc. \n", "* Likewise for an improved architecture or using a convolutional GAN (or even implement a VAE)\n", "* For a bigger chunk of extra credit, load the CIFAR10 data (see last assignment) and train a compelling generative model on CIFAR-10\n", "* Something new/cool.\n", "\n", "#### Describe what you did here\n", "** TBD **" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.13" } }, "nbformat": 4, "nbformat_minor": 1 }