{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# CNTK 206 Part C: Wasserstein and Loss Sensitive GAN with CIFAR Data\n", "\n", "**Prerequisites**: We assume that you have successfully downloaded the CIFAR data by completing tutorial CNTK 201A. Or you can run the CNTK 201A image data downloader notebook to download and prepare CIFAR dataset.\n", "\n", "**Contributed by**: [Anqi Li](https://www.linkedin.com/in/anqi-li-4a02b1103/) October 17, 2017 \n", "\n", "## Introduction\n", "Generative models have gained a lot of attention in deep learning community which has traditionally leveraged discriminative models for semi-supervised and unsupervised learning. [Generative Adversarial Network (GAN)](https://arxiv.org/pdf/1406.2661v1.pdf) (Goodfellow *et al.*, 2014) is one of the most popular generative model because of its promising results in [various tasks](https://github.com/HKCaesar/really-awesome-gan) in computer vision and natural language processing. However, the original version of GANs are notoriously difficult to train. Without carefully-chosen hyper-parameters and network architecture that balances Generator and Discriminator training, GANs could easily suffer from vanishing gradient or mode collapse (where the model is only able to produce a single or a few samples). In this tutorial, we introduce several improved GAN models, namely [Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf) (W-GAN) (Arjovsky *et al.*, 2017) and [Loss Sensitive GAN](https://arxiv.org/pdf/1701.06264.pdf) (LS-GAN) (Qi, 2017), that are proposed to address the problems of vanishing gradient and mode collapse.\n", "\n", "## Overview\n", "In this section, we hightlight the key differences between Wasserstein GAN and original GANs (shown in CNTK 206 A and B tutorials) both theoretically as well as from an implementation perspective.\n", "\n", "### Why is GAN hard to train?\n", "In the training of the original GANs, balancing the convergence of the discriminator and the generator is extremely important because if one is far ahead of the other, the other cannot get enough gradient to improve. However, balancing the convergence of two neural networks is hard.\n", "\n", "The mathematical details are summarized here and in [this paper here](https://arxiv.org/pdf/1701.04862.pdf). You may skip the math and look at the implementation details for W-GAN and LS-GAN.\n", "\n", "A [typical GAN](https://arxiv.org/pdf/1406.2661v1.pdf) includes two neural network, a *Generator* $G$ and a *Discriminator* $D$. The training of GAN is modeled as a two-player zero-sum game. The Discriminator D is trained to predict the probability that a sample is a real sample rather than generated from the generator G, while the generator G is trained to better fool the discriminator by producing real-looking samples. The objective (also referred to as the value function) for GAN training is,\n", "\n", "$$\\min_G\\max_D V(D,G)=\\mathbb{E}_{x\\sim p_{data}(x)}[\\log D(x)] + \\mathbb{E}_{z\\sim p_z(x)}[\\log(1-D(G(z)))]$$\n", "\n", "In the original GAN paper, the author proves that the optimal strategy for discriminator is predicting\n", "\n", "$$D^*(x)=\\frac{p_{data}(x)}{p_{data}(x)+p_{model}(x)}$$\n", "\n", "By plugging it into the GAN objective function, one may find that the discriminator is actually an estimation of *Jensen-Shannon* divergence (JS divergence or JSD) of two distributions (data and model).\n", "\n", "$$L(D^*,g_\\theta)=2 JSD(\\mathbb{P}_{data}\\|\\mathbb{P}_{model}) - 2\\log2$$\n", "\n", "This implies that the optimal strategy is when $$\\mathbb{P}_{data}= \\mathbb{P}_{model}$$. However, JS distance may become locally saturated and gets vanishing gradient to train the GAN generator if the discriminator is over-trained. Also JSD is a positive value. \n", "\n", "\n", "### Wasserstein GAN\n", "To address this problem, [Wasserstein GAN](https://arxiv.org/pdf/1701.07875.pdf) was proposed to use a different distance measurement for probability distributions, namely *Earth-Mover* (EM) distance or *Wasserstein* distance instead of JS divergence. The authors claimed that by using EM distance, one no longer needs to carefully maintain the balance between the generator and the discriminator, and, notably, the output of the discriminator is referred as the critic instead. EM distance serves as a good indicator of image quality of generated samples. The EM distance of two distribution is defined as\n", "\n", "$$W(p_{data}, p_{model})=\\inf_{\\gamma\\in\\prod(p_{data},p_{model})}\\mathbb{E}_{(x,y)\\sim\\gamma}\\left[\\|x-y\\|\\right]$$\n", "\n", "EM distance is a more sensible distance measurement than JS divergence since EM distance is continuous and differentiable anywhere while JS divergence is not. The authors uses the Kantorovich-Rubinstein duality to derive the objective for Wasserstein GAN,\n", "\n", "$$\\min_G\\max_{\\|D\\|_L\\leq K} \\mathbb{E}_{x\\sim p_{data}(x)}[D(x)] - \\mathbb{E}_{z\\sim p_z(x)}[D(G(z))]$$\n", "\n", "**Note**: The Kantorovich-Rubinstein duality requires the function to be K-Lipschitz. The authors suggest *clipping the weights of discriminator* to satisfy [Lipschitz continuity](https://en.wikipedia.org/wiki/Lipschitz_continuity).\n", "\n", "#### Implementation details\n", "\n", "The modification needed on implementation side is minor. One can change an original GAN into a Wasserstein GAN with a few lines of code:\n", "\n", "1. Use W-GAN loss function\n", "2. Remove the sigmoid activation for the last layer of discriminator\n", "3. Clip the weights of the discriminator after updates (e.g., to [-0.01, 0.01])\n", "4. Train discriminator more iterations than generator (e.g., train the discriminator for 5 iterations and train the generator for one iteration only at each round)\n", "5. Use Adam with `momentum=0`\n", "6. Use small learning rate (e.g., 0.00005)\n", "\n", "### Loss Sensitive GAN\n", "\n", "[Loss Sensitive GAN](https://arxiv.org/pdf/1701.06264.pdf) was proposed to address the problem of vanishing gradient. LS-GAN is trained on a loss function that allows the generator to focus on improving poor generated samples that are far from the real sample manifold. The author shows that the loss learned by LS-GAN has non-vanishing gradient almost everywhere, even when the discriminator is over-trained.\n", "\n", "$$\\min_D L_D = \\mathbb{E}_{x\\sim p_{data}(x)}[D(x)] + \\lambda\\mathbb{E}_{x\\sim p_{data}(x), z\\sim p_z(x)}\\left[\\left(\\|x-G(z)\\|_1 + D(x) - D(G(z))\\right)_+\\right]$$\n", "$$\\min_G L_G = \\mathbb{E}_{z\\sim p_z(x)}[D(G(z))]$$\n", "\n", "#### Implementation details\n", "\n", "The modification needed on implementation side is also minor. One can change an original GAN into a Loss Sensitive GAN with a few lines of code:\n", "\n", "1. Use the LS-GAN loss function\n", "2. Remove the sigmoid activation for the last layer of discriminator\n", "3. Update both the generator and the discriminator with weight decay\n", "4. Train discriminator and generator each with one iteration at each round\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import os\n", "\n", "import cntk as C\n", "import cntk.tests.test_utils\n", "cntk.tests.test_utils.set_device_from_pytest_env() # (only needed for our build system)\n", "C.cntk_py.set_fixed_random_seed(1) # fix a random seed for CNTK components\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "There are two run modes:\n", "*Fast mode*: `isFast` is set to `True`. This is the default mode for the notebooks, which means we train for fewer iterations or train / test on limited data. This ensures functional correctness of the notebook though the models produced are far from what a completed training would produce.\n", "*Slow mode*: We recommend the user to set this flag to `False` once the user has gained familiarity with the notebook content and wants to gain insight from running the notebooks for a longer period with different parameters for training.\n", "\n", "**Note**: If the `isFlag` is set to `False` the notebook will take hours or even days on a GPU enabled machine. You can try fewer iterations by setting the `num_minibatches` to a smaller number which comes at the expense of quality of the generated images." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "isFast = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data Reading\n", "The input to the GANs will be a vector of random numbers. At the end of the training, the GAN \"learns\" to generate images drawn from the CIFAR dataset. We will be using the same CIFAR data prepared in tutorial CNTK 201A. For our purposes, you only need to know that the following function returns an object that will be used to read images from the CIFAR dataset. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# image dimensionalities\n", "img_h, img_w = 32, 32\n", "img_c = 3" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Determine the data path for testing\n", "# Check for an environment variable defined in CNTK's test infrastructure\n", "envvar = 'CNTK_EXTERNAL_TESTDATA_SOURCE_DIRECTORY'\n", "def is_test(): return envvar in os.environ\n", "\n", "if is_test():\n", " data_path = os.path.join(os.environ[envvar],'Image','CIFAR','v0','tutorial201')\n", " data_path = os.path.normpath(data_path)\n", "else:\n", " data_path = os.path.join('data', 'CIFAR-10')\n", " \n", "train_file = os.path.join(data_path, 'train_map.txt')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_reader(map_file, train):\n", " print(\"Reading map file:\", map_file)\n", " \n", " if not os.path.exists(map_file):\n", " raise RuntimeError(\"This tutorials depends 201A tutorials, please run 201A first.\")\n", " \n", " import cntk.io.transforms as xforms\n", " transforms = [xforms.crop(crop_type='center', side_ratio=0.8),\n", " xforms.scale(width=img_w, height=img_h, channels=img_c, interpolations='linear')]\n", " # deserializer\n", " return C.io.MinibatchSource(C.io.ImageDeserializer(map_file, C.io.StreamDefs(\n", " features = C.io.StreamDef(field='image', transforms=transforms), # first column in map file is referred to as 'image'\n", " labels = C.io.StreamDef(field='label', shape=10) # and second as 'label'\n", " )))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def noise_sample(num_samples):\n", " return np.random.uniform(\n", " low = -1.0,\n", " high = 1.0,\n", " size = [num_samples, g_input_dim]\n", " ).astype(np.float32)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## W-GAN Implementation\n", "\n", "Note that we assume that you have already completed the DCGAN tutorial. If you need a basic recap of GAN concepts or DCGAN architecture, please visit our [DCGAN tutorial](https://github.com/Microsoft/CNTK/blob/master/Tutorials/CNTK_206B_DCGAN.ipynb). \n", "### Model Configuration\n", "We implemented the W-GAN based on [DCGAN](https://arxiv.org/pdf/1511.06434.pdf) architecture. In this step, we define some of the architectural and training hyper-parameters for our model.\n", "* The generator is convolutional transpose layer with $5\\times5$ kernels and strides of $2$\n", "* The input of the generator is a 100-dimensional random vector\n", "* The output of the generator is a flattened $64\\times64$ image with $3$ channels\n", "* The discriminator is a convolutional layer with $5\\times5$ kernels and strides of $2$\n", "* The input of the discriminator is also a flattened $64\\times64$ image with $3$ channels" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# architectural hyper-parameters\n", "gkernel = dkernel = 5\n", "gstride = dstride = 2\n", "\n", "# Input / Output parameter of Generator and Discriminator\n", "g_input_dim = 100\n", "g_output_dim = d_input_dim = (img_c, img_h, img_w)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We first establish some of the helper functions (batch normalization with relu and batch normalization with leaky relu) that will make our lives easier when defining the generator and the discriminator." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Helper functions\n", "def bn_with_relu(x, activation=C.relu):\n", " h = C.layers.BatchNormalization(map_rank=1)(x)\n", " return C.relu(h)\n", "\n", "# We use param-relu function to use a leak=0.2 since CNTK implementation \n", "# of Leaky ReLU is fixed to 0.01\n", "def bn_with_leaky_relu(x, leak=0.2):\n", " h = C.layers.BatchNormalization(map_rank=1)(x)\n", " r = C.param_relu(C.constant((np.ones(h.shape)*leak).astype(np.float32)), h)\n", " return r\n", "\n", "def leaky_relu(x, leak=0.2):\n", " return C.param_relu(C.constant((np.ones(x.shape)*leak).astype(np.float32)), x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Generator**\n", "\n", "We define the generator according to the DCGAN architecture. The generator takes a 100-dimensional random vector as input and outputs a flattened $3\\times64\\times64$ image. We use convolution transpose layers with ReLU activation and batch normalization except for the last layer, where we use tanh to normalize the output to the interval $[-1, 1]$." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def convolutional_generator(z):\n", " with C.layers.default_options(init=C.normal(scale=0.02)):\n", " \n", " gfc_dim = 256\n", " gf_dim = 64\n", " \n", " print('Generator input shape: ', z.shape)\n", " \n", " h0 = C.layers.Dense([gfc_dim, img_h//8, img_w//8], activation=None)(z)\n", " h0 = bn_with_relu(h0)\n", " print('h0 shape', h0.shape)\n", "\n", " h1 = C.layers.ConvolutionTranspose2D(gkernel,\n", " num_filters=gf_dim*2,\n", " strides=gstride,\n", " pad=True,\n", " output_shape=(img_h//4, img_w//4),\n", " activation=None)(h0)\n", " h1 = bn_with_relu(h1)\n", " print('h1 shape', h1.shape)\n", "\n", " h2 = C.layers.ConvolutionTranspose2D(gkernel,\n", " num_filters=gf_dim,\n", " strides=gstride,\n", " pad=True,\n", " output_shape=(img_h//2, img_w//2),\n", " activation=None)(h1)\n", " h2 = bn_with_relu(h2)\n", " print('h2 shape :', h2.shape)\n", " \n", " h3 = C.layers.ConvolutionTranspose2D(gkernel,\n", " num_filters=img_c,\n", " strides=gstride,\n", " pad=True,\n", " output_shape=(img_h, img_w),\n", " activation=C.tanh)(h2)\n", " print('h3 shape :', h3.shape)\n", "\n", " return h3" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Discriminator**\n", "\n", "We define the discriminator according to the DCGAN architecture except for the last layer. The discriminator takes a flattened image as input and outputs a single scalar. We do not use any activation at the last layer." ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def convolutional_discriminator(x):\n", " with C.layers.default_options(init=C.normal(scale=0.02)):\n", " \n", " dfc_dim = 256\n", " df_dim = 64\n", " \n", " print('Discriminator convolution input shape', x.shape)\n", "\n", " h0 = C.layers.Convolution2D(dkernel, df_dim, strides=dstride, pad=True)(x)\n", " h0 = leaky_relu(h0, leak=0.2)\n", " print('h0 shape :', h0.shape)\n", "\n", " h1 = C.layers.Convolution2D(dkernel, df_dim*2, strides=dstride, pad=True)(h0)\n", " h1 = bn_with_leaky_relu(h1, leak=0.2)\n", " print('h1 shape :', h1.shape)\n", "\n", " h2 = C.layers.Convolution2D(dkernel, dfc_dim, strides=dstride, pad=True)(h1)\n", " h2 = bn_with_leaky_relu(h2, leak=0.2)\n", " print('h2 shape :', h2.shape)\n", "\n", " h3 = C.layers.Dense(1, activation=None)(h2)\n", " print('h3 shape :', h3.shape)\n", "\n", " return h3" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# training config\n", "minibatch_size = 64\n", "num_minibatches = 500 if isFast else 20000\n", "lr = 0.00005 # small learning rates are preferred\n", "momentum = 0.0 # momentum is not suggested since it can make W-GANs unstable\n", "clip = 0.01 # the weight clipping parameter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the graph\n", "\n", "The discriminator must be used on both the real CIFAR images and fake images generated by the generator function. One way to represent this in the computational graph is to create a clone of the output of the discriminator function, but with substituted inputs. Setting `method=share` in the clone function ensures that both paths through the discriminator model use the same set of parameters\n", "\n", "We need to update the parameters for the generator and discriminator model separately using the gradients from different loss functions. We can get the parameters for a Function in the graph with the parameters attribute. However, when updating the model parameters, update only the parameters of the respective models while keeping the other parameters unchanged. In other words, when updating the generator we will update only the parameters of the function while keeping the parameters of the function fixed and vice versa.\n", "\n", "Because W-GAN needs to clip the weights of the discriminator before every update in order to maintain K-Lipschitz continuity. We build a graph with clipped parameters stored in `clipped_D_params`. The suggested value of clipping threshold is 0.01.\n", "\n", "**Note**: CNTK parameter learner uses sum of gradient within a minibatch by default instead of mean of gradient. To reproduce results with the same hyper-parameter in the paper, we need to set `use_mean_gradient = True`, and `unit_gain = False`." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_WGAN_graph(noise_shape, image_shape, generator, discriminator):\n", " \n", " input_dynamic_axes = [C.Axis.default_batch_axis()]\n", " Z = C.input_variable(noise_shape, dynamic_axes=input_dynamic_axes)\n", " X_real = C.input_variable(image_shape, dynamic_axes=input_dynamic_axes)\n", " X_real_scaled = (X_real - 127.5) / 127.5\n", "\n", " # Create the model function for the generator and discriminator models\n", " X_fake = generator(Z)\n", " D_real = discriminator(X_real_scaled)\n", " D_fake = D_real.clone(\n", " method = 'share',\n", " substitutions = {X_real_scaled.output: X_fake.output}\n", " )\n", " \n", " clipped_D_params = [C.clip(p, -clip, clip) for p in D_real.parameters]\n", " \n", " G_loss = - D_fake\n", " D_loss = - D_real + D_fake\n", "\n", " G_learner = C.adam(\n", " parameters = X_fake.parameters,\n", " lr = C.learning_rate_schedule(lr, C.UnitType.sample),\n", " momentum = C.momentum_schedule(momentum),\n", " variance_momentum = C.momentum_schedule(0.999),\n", " unit_gain=False,\n", " use_mean_gradient=True\n", " )\n", " \n", " D_learner = C.adam(\n", " parameters = D_real.parameters,\n", " lr = C.learning_rate_schedule(lr, C.UnitType.sample),\n", " momentum = C.momentum_schedule(momentum),\n", " variance_momentum = C.momentum_schedule(0.999),\n", " unit_gain=False,\n", " use_mean_gradient=True\n", " )\n", " \n", " # Instantiate the trainers\n", " G_trainer = C.Trainer(X_fake,\n", " (G_loss, None),\n", " G_learner)\n", " D_trainer = C.Trainer(D_real,\n", " (D_loss, None),\n", " D_learner)\n", "\n", " return X_real, X_fake, D_real, clipped_D_params, Z, G_trainer, D_trainer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train the model\n", "The code for training the GAN closely follows Algorithm 1 in the W-GAN paper. Note that compared to original GANs, we train the discriminator many more times than the generator. The reason behind that is the output of the discriminator serves as an estimation of the EM distance. We want to train the discriminator until it can closely estimate the EM distance. In order to make sure that the discriminator has a sufficient good estimation at the very beginning of the training, we even train it for 100 iterations before train the generator (this is disabled in fast mode because this will significantly take longer time)." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train_WGAN(reader_train, generator, discriminator):\n", " X_real, X_fake, D_real, clipped_D_params, Z, G_trainer, D_trainer = \\\n", " build_WGAN_graph(g_input_dim, d_input_dim, generator, discriminator)\n", " # print out loss for each model for upto 25 times\n", " \n", " print_frequency_mbsize = num_minibatches // 25\n", " \n", " print(\"First row is Generator loss, second row is Discriminator loss\")\n", " pp_G = C.logging.ProgressPrinter(print_frequency_mbsize)\n", " pp_D = C.logging.ProgressPrinter(print_frequency_mbsize)\n", " \n", " input_map = {X_real: reader_train.streams.features}\n", "\n", " for training_step in range(num_minibatches):\n", " # train the discriminator model for diter steps\n", " if not isFast and (training_step < 25 or training_step % 500 == 0):\n", " diter = 100\n", " else:\n", " diter = 5\n", " for d_train_step in range(diter):\n", " for parameter, clipped in zip(D_real.parameters, clipped_D_params):\n", " C.assign(parameter, clipped).eval()\n", " Z_data = noise_sample(minibatch_size)\n", " X_data = reader_train.next_minibatch(minibatch_size, input_map)\n", " batch_inputs = {X_real: X_data[X_real].data, Z: Z_data}\n", " D_trainer.train_minibatch(batch_inputs)\n", " \n", " Z_data = noise_sample(minibatch_size)\n", " batch_inputs = {Z: Z_data}\n", " G_trainer.train_minibatch(batch_inputs)\n", " \n", " pp_G.update_with_trainer(G_trainer)\n", " pp_D.update_with_trainer(D_trainer)\n", "\n", " G_trainer_loss = G_trainer.previous_minibatch_loss_average\n", " return Z, X_fake, G_trainer_loss" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading map file: c:\\Data\\CNTKTestData\\Image\\CIFAR\\v0\\tutorial201\\train_map.txt\n", "Generator input shape: (100,)\n", "h0 shape (256, 4, 4)\n", "h1 shape (128, 8, 8)\n", "h2 shape : (64, 16, 16)\n", "h3 shape : (3, 32, 32)\n", "Discriminator convolution input shape (3, 32, 32)\n", "h0 shape : (64, 16, 16)\n", "h1 shape : (128, 8, 8)\n", "h2 shape : (256, 4, 4)\n", "h3 shape : (1,)\n", "First row is Generator loss, second row is Discriminator loss\n", " Minibatch[ 1- 20]: loss = 0.134046 * 1280;\n", " Minibatch[ 1- 20]: loss = -0.230201 * 1280;\n", " Minibatch[ 21- 40]: loss = 0.290967 * 1280;\n", " Minibatch[ 21- 40]: loss = -0.547906 * 1280;\n", " Minibatch[ 41- 60]: loss = 0.340717 * 1280;\n", " Minibatch[ 41- 60]: loss = -0.654352 * 1280;\n", " Minibatch[ 61- 80]: loss = 0.356203 * 1280;\n", " Minibatch[ 61- 80]: loss = -0.691419 * 1280;\n", " Minibatch[ 81- 100]: loss = 0.359952 * 1280;\n", " Minibatch[ 81- 100]: loss = -0.701833 * 1280;\n", " Minibatch[ 101- 120]: loss = 0.358435 * 1280;\n", " Minibatch[ 101- 120]: loss = -0.694525 * 1280;\n", " Minibatch[ 121- 140]: loss = 0.359395 * 1280;\n", " Minibatch[ 121- 140]: loss = -0.700816 * 1280;\n", " Minibatch[ 141- 160]: loss = 0.355547 * 1280;\n", " Minibatch[ 141- 160]: loss = -0.694811 * 1280;\n", " Minibatch[ 161- 180]: loss = 0.343038 * 1280;\n", " Minibatch[ 161- 180]: loss = -0.667510 * 1280;\n", " Minibatch[ 181- 200]: loss = 0.327844 * 1280;\n", " Minibatch[ 181- 200]: loss = -0.639241 * 1280;\n", " Minibatch[ 201- 220]: loss = 0.319497 * 1280;\n", " Minibatch[ 201- 220]: loss = -0.617136 * 1280;\n", " Minibatch[ 221- 240]: loss = 0.306403 * 1280;\n", " Minibatch[ 221- 240]: loss = -0.589553 * 1280;\n", " Minibatch[ 241- 260]: loss = 0.296030 * 1280;\n", " Minibatch[ 241- 260]: loss = -0.574477 * 1280;\n", " Minibatch[ 261- 280]: loss = 0.293737 * 1280;\n", " Minibatch[ 261- 280]: loss = -0.573276 * 1280;\n", " Minibatch[ 281- 300]: loss = 0.303713 * 1280;\n", " Minibatch[ 281- 300]: loss = -0.593846 * 1280;\n", " Minibatch[ 301- 320]: loss = 0.301374 * 1280;\n", " Minibatch[ 301- 320]: loss = -0.579623 * 1280;\n", " Minibatch[ 321- 340]: loss = 0.304054 * 1280;\n", " Minibatch[ 321- 340]: loss = -0.591519 * 1280;\n", " Minibatch[ 341- 360]: loss = 0.309324 * 1280;\n", " Minibatch[ 341- 360]: loss = -0.602651 * 1280;\n", " Minibatch[ 361- 380]: loss = 0.312418 * 1280;\n", " Minibatch[ 361- 380]: loss = -0.604142 * 1280;\n", " Minibatch[ 381- 400]: loss = 0.305925 * 1280;\n", " Minibatch[ 381- 400]: loss = -0.592394 * 1280;\n", " Minibatch[ 401- 420]: loss = 0.306042 * 1280;\n", " Minibatch[ 401- 420]: loss = -0.592130 * 1280;\n", " Minibatch[ 421- 440]: loss = 0.310501 * 1280;\n", " Minibatch[ 421- 440]: loss = -0.602418 * 1280;\n", " Minibatch[ 441- 460]: loss = 0.294023 * 1280;\n", " Minibatch[ 441- 460]: loss = -0.576110 * 1280;\n", " Minibatch[ 461- 480]: loss = 0.308501 * 1280;\n", " Minibatch[ 461- 480]: loss = -0.605088 * 1280;\n", " Minibatch[ 481- 500]: loss = 0.305335 * 1280;\n", " Minibatch[ 481- 500]: loss = -0.594910 * 1280;\n" ] } ], "source": [ "reader_train = create_reader(train_file, True)\n", "G_input, G_output, G_trainer_loss = train_WGAN(reader_train,\n", " convolutional_generator,\n", " convolutional_discriminator)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training loss of the generator is: 0.31\n" ] } ], "source": [ "# Print the generator loss \n", "print(\"Training loss of the generator is: {0:.2f}\".format(G_trainer_loss))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating Fake (Synthetic) Images (W-GAN)\n", "Now that we have trained the model, we can create fake images simply by feeding random noise into the generator and displaying the outputs. Below are a few images generated from random samples. To get a new set of samples, you can re-run the last cell." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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io7FleLSLM+jAJuUPduHg6S0vlwWWGmDXJsLK+QxF11Zc5wXLQwMZ6Fi7AwbD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56MbsxZYq2lLpS/L05LuqAzeHUcwyHdjsLikrgzisEKkg0i1dXVEc5Sh/wu5ui+4P+FbjTID0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\n+tsQuQjYNt/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5v01VvOfr6T8mW+5omymTcod0V4RxTKJLrFZkkxyxaThKQ8S5gBvB2D7O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\nCcVRbjjdbmneNZQnixExcTbBW0E3bDmZDd4YnOkwseMwnJEqAD/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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_images(images, subplot_shape):\n", " plt.style.use('ggplot')\n", " fig, axes = plt.subplots(*subplot_shape)\n", " for image, ax in zip(images, axes.flatten()):\n", " image = image[np.array([2,1,0]),:,:]\n", " image = np.rollaxis(image / 2 + 0.5, 0, 3)\n", " ax.imshow(image, vmin=-1.0, vmax=1.0)\n", " ax.axis('off')\n", " plt.show()\n", "\n", "noise = noise_sample(36)\n", "images = G_output.eval({G_input: noise})\n", "plot_images(images, subplot_shape=[6, 6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Larger number of iterations should generate more realistic looking images." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## LS-GAN Implementation\n", "\n", "Since the generator and discriminator architectures of LS-GAN is the same as W-GAN, we will reuse the generator and the discriminator we defined for W-GAN. The main difference between W-GAN and LS-GAN is their loss function and optimizer they use. We redefine the training parameters for LS-GAN." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# training config\n", "minibatch_size = 64\n", "num_minibatches = 1000 if isFast else 20000\n", "lr = 0.0001\n", "momentum = 0.5\n", "lambda_ = 0.0002 # lambda in LS-GAN loss function, controls the size of margin\n", "weight_decay = 0.00005" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Build the graph\n", "As we mentioned above, one of the differences between LS-GAN and W-GAN is their loss function. In `build_LSGAN_graph`, we should define the loss function for the generator and the discriminator. Another difference is that we do not do weight clipping in LS-GAN, so `clipped_D_parames` is no longer needed. Instead, we use weight decay which is mathematically equivalent to adding an l2 regularization in the optimizer." ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def build_LSGAN_graph(noise_shape, image_shape, generator, discriminator):\n", " \n", " input_dynamic_axes = [C.Axis.default_batch_axis()]\n", " Z = C.input_variable(noise_shape, dynamic_axes=input_dynamic_axes)\n", " X_real = C.input_variable(image_shape, dynamic_axes=input_dynamic_axes)\n", " X_real_scaled = (X_real - 127.5) / 127.5\n", "\n", " # Create the model function for the generator and discriminator models\n", " X_fake = generator(Z)\n", " D_real = discriminator(X_real_scaled)\n", " D_fake = D_real.clone(\n", " method = 'share',\n", " substitutions = {X_real_scaled.output: X_fake.output}\n", " )\n", " \n", " G_loss = D_fake\n", " D_loss = C.element_max(D_real - D_fake + lambda_ * C.reduce_sum(C.abs(X_fake - X_real_scaled)), [0.])\n", " \n", " G_learner = C.adam(\n", " parameters = X_fake.parameters,\n", " lr = C.learning_rate_schedule(lr, C.UnitType.sample),\n", " momentum = C.momentum_schedule(momentum),\n", " variance_momentum = C.momentum_schedule(0.999),\n", " l2_regularization_weight=weight_decay,\n", " unit_gain=False,\n", " use_mean_gradient=True\n", " )\n", " \n", " D_learner = C.adam(\n", " parameters = D_real.parameters,\n", " lr = C.learning_rate_schedule(lr, C.UnitType.sample),\n", " momentum = C.momentum_schedule(momentum),\n", " variance_momentum = C.momentum_schedule(0.999),\n", " l2_regularization_weight=0.00005,\n", " unit_gain=False,\n", " use_mean_gradient=True\n", " )\n", " \n", " # Instantiate the trainers\n", " G_trainer = C.Trainer(X_fake,\n", " (G_loss, None),\n", " G_learner)\n", " D_trainer = C.Trainer(D_real,\n", " (D_loss, None),\n", " D_learner)\n", "\n", " return X_real, X_fake, Z, G_trainer, D_trainer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Train the model\n", "To train the LS-GAN model, we can just simply update the discriminator and the generator alternatively at each round." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def train_LSGAN(reader_train, generator, discriminator):\n", " X_real, X_fake, Z, G_trainer, D_trainer = \\\n", " build_LSGAN_graph(g_input_dim, d_input_dim, generator, discriminator)\n", " \n", " # print out loss for each model for upto 25 times\n", " print_frequency_mbsize = num_minibatches // 25\n", " \n", " print(\"First row is Generator loss, second row is Discriminator loss\")\n", " pp_G = C.logging.ProgressPrinter(print_frequency_mbsize)\n", " pp_D = C.logging.ProgressPrinter(print_frequency_mbsize)\n", " \n", " \n", " input_map = {X_real: reader_train.streams.features}\n", "\n", " for training_step in range(num_minibatches):\n", " # Train the discriminator and the generator alternatively\n", " Z_data = noise_sample(minibatch_size)\n", " X_data = reader_train.next_minibatch(minibatch_size, input_map)\n", " batch_inputs = {X_real: X_data[X_real].data, Z: Z_data}\n", " D_trainer.train_minibatch(batch_inputs)\n", " \n", " Z_data = noise_sample(minibatch_size)\n", " batch_inputs = {Z: Z_data}\n", " G_trainer.train_minibatch(batch_inputs)\n", " \n", " pp_G.update_with_trainer(G_trainer)\n", " pp_D.update_with_trainer(D_trainer)\n", "\n", " G_trainer_loss = G_trainer.previous_minibatch_loss_average\n", " return Z, X_fake, G_trainer_loss" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Reading map file: c:\\Data\\CNTKTestData\\Image\\CIFAR\\v0\\tutorial201\\train_map.txt\n", "Generator input shape: (100,)\n", "h0 shape (256, 4, 4)\n", "h1 shape (128, 8, 8)\n", "h2 shape : (64, 16, 16)\n", "h3 shape : (3, 32, 32)\n", "Discriminator convolution input shape (3, 32, 32)\n", "h0 shape : (64, 16, 16)\n", "h1 shape : (128, 8, 8)\n", "h2 shape : (256, 4, 4)\n", "h3 shape : (1,)\n", "First row is Generator loss, second row is Discriminator loss\n", " Minibatch[ 1- 40]: loss = 0.228339 * 2560;\n", " Minibatch[ 1- 40]: loss = 0.216397 * 2560;\n", " Minibatch[ 41- 80]: loss = 1.178880 * 2560;\n", " Minibatch[ 41- 80]: loss = 0.045059 * 2560;\n", " Minibatch[ 81- 120]: loss = -0.669602 * 2560;\n", " Minibatch[ 81- 120]: loss = 0.016506 * 2560;\n", " Minibatch[ 121- 160]: loss = 3.001537 * 2560;\n", " Minibatch[ 121- 160]: loss = 0.020488 * 2560;\n", " Minibatch[ 161- 200]: loss = 4.907479 * 2560;\n", " Minibatch[ 161- 200]: loss = 0.038785 * 2560;\n", " Minibatch[ 201- 240]: loss = 3.370783 * 2560;\n", " Minibatch[ 201- 240]: loss = 0.008379 * 2560;\n", " Minibatch[ 241- 280]: loss = 2.767947 * 2560;\n", " Minibatch[ 241- 280]: loss = 0.050700 * 2560;\n", " Minibatch[ 281- 320]: loss = 2.857884 * 2560;\n", " Minibatch[ 281- 320]: loss = 0.056336 * 2560;\n", " Minibatch[ 321- 360]: loss = 3.734755 * 2560;\n", " Minibatch[ 321- 360]: loss = 0.026292 * 2560;\n", " Minibatch[ 361- 400]: loss = 4.191766 * 2560;\n", " Minibatch[ 361- 400]: loss = 0.028086 * 2560;\n", " Minibatch[ 401- 440]: loss = 5.489224 * 2560;\n", " Minibatch[ 401- 440]: loss = 0.096404 * 2560;\n", " Minibatch[ 441- 480]: loss = 5.872074 * 2560;\n", " Minibatch[ 441- 480]: loss = 0.033544 * 2560;\n", " Minibatch[ 481- 520]: loss = 4.891074 * 2560;\n", " Minibatch[ 481- 520]: loss = 0.104155 * 2560;\n", " Minibatch[ 521- 560]: loss = 6.039796 * 2560;\n", " Minibatch[ 521- 560]: loss = 0.057377 * 2560;\n", " Minibatch[ 561- 600]: loss = 7.452615 * 2560;\n", " Minibatch[ 561- 600]: loss = 0.044365 * 2560;\n", " Minibatch[ 601- 640]: loss = 7.931797 * 2560;\n", " Minibatch[ 601- 640]: loss = 0.069383 * 2560;\n", " Minibatch[ 641- 680]: loss = 8.130133 * 2560;\n", " Minibatch[ 641- 680]: loss = 0.051125 * 2560;\n", " Minibatch[ 681- 720]: loss = 7.301010 * 2560;\n", " Minibatch[ 681- 720]: loss = 0.057592 * 2560;\n", " Minibatch[ 721- 760]: loss = 6.646916 * 2560;\n", " Minibatch[ 721- 760]: loss = 0.070276 * 2560;\n", " Minibatch[ 761- 800]: loss = 6.848904 * 2560;\n", " Minibatch[ 761- 800]: loss = 0.086750 * 2560;\n", " Minibatch[ 801- 840]: loss = 6.596177 * 2560;\n", " Minibatch[ 801- 840]: loss = 0.053448 * 2560;\n", " Minibatch[ 841- 880]: loss = 5.710600 * 2560;\n", " Minibatch[ 841- 880]: loss = 0.066704 * 2560;\n", " Minibatch[ 881- 920]: loss = 5.868099 * 2560;\n", " Minibatch[ 881- 920]: loss = 0.050458 * 2560;\n", " Minibatch[ 921- 960]: loss = 6.192872 * 2560;\n", " Minibatch[ 921- 960]: loss = 0.028876 * 2560;\n", " Minibatch[ 961-1000]: loss = 6.553334 * 2560;\n", " Minibatch[ 961-1000]: loss = 0.049664 * 2560;\n" ] } ], "source": [ "reader_train = create_reader(train_file, True)\n", "G_input, G_output, G_trainer_loss = train_LSGAN(reader_train,\n", " convolutional_generator,\n", " convolutional_discriminator)" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Training loss of the generator is: 5.92\n" ] } ], "source": [ "# Print the generator loss \n", "print(\"Training loss of the generator is: {0:.2f}\".format(G_trainer_loss))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Generating Fake (Synthetic) Images (LS-GAN)\n", "Now that we have trained the LS-GAN model, we can create fake images simply by feeding random noise into the generator and displaying the outputs. Below are a few images generated from random samples. To get a new set of samples, you can re-run the last cell." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "image/png": 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8ywfFQ3I1QhHSKxUN6TB1y7LscFE8y2MreSw/RNaeJp19k8bRJuKrQ6otQaYV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\nK7xY4SqLfTWjOT0mmx6zPDmmS6Ykao4ODhVK1tPviN3Hf14BplPD3a1HGvB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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "def plot_images(images, subplot_shape):\n", " plt.style.use('ggplot')\n", " fig, axes = plt.subplots(*subplot_shape)\n", " for image, ax in zip(images, axes.flatten()):\n", " image = image[np.array([2,1,0]),:,:]\n", " image = np.rollaxis(image / 2 + 0.5, 0, 3)\n", " ax.imshow(image, vmin=-1.0, vmax=1.0)\n", " ax.axis('off')\n", " plt.show()\n", "\n", "noise = noise_sample(36)\n", "images = G_output.eval({G_input: noise})\n", "plot_images(images, subplot_shape=[6, 6])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Larger number of iterations should generate more realistic looking images. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.5.2" } }, "nbformat": 4, "nbformat_minor": 2 }