diff --git a/.gitignore b/.gitignore
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@@ -1,23 +1,23 @@
-*checkpoints*
-*.dat
-*.demo
-*.pyc
-*.numpy
-*.symbol
-*.json
-*.dat
-*.log
-*checkpoint*
-*.demo
-*.csv
-ml-100k/
-*.zip
-*.params
-*.jpg
-*.tar
-*label
-*synset.txt
-*.rec
-*.lst
-*.tar.gz
-*.gz
+-*checkpoints*
+-*.dat
+-*.demo
+-*.pyc
+-*.numpy
+-*.symbol
+-*.json
+-*.dat
+-*.log
+-*checkpoint*
+-*.demo
+-*.csv
+-ml-100k/
+-*.zip
+-*.params
+-*.jpg
+-*.tar
+-*label
+-*synset.txt
+-*.rec
+-*.lst
+-*.tar.gz
+-*.gz
diff --git a/python/tutorials/dcgan-model.png b/python/tutorials/dcgan-model.png
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@@ -0,0 +1,1014 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "# DCGAN - Create Images from Random Numbers!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "### Generative Adversarial Networks\n",
+ "Ever since Ian Goodfellow and colleagues [introduced the concept of Generative Adversarial Networks (GANs)](https://arxiv.org/abs/1406.2661), GANs have been a popular topic in the field of AI. GANs are an application of unsupervised learning - you don't need labels for your dataset in order to train a GAN. \n",
+ "\n",
+ "The GAN framework composes of two neural networks: a generator network and a discriminator network. \n",
+ "\n",
+ "The generator's job is to take a set of random numbers and produce data (such as images or text).\n",
+ "\n",
+ "The discriminator then takes in that data as well as samples of that data from a dataset and tries to determine if is \"fake\" (created by the generator network) or \"real\" (from the original dataset). \n",
+ "\n",
+ "During training, the two networks play a game against each other. \n",
+ "The generator tries to create realistic data, so that it can fool the discriminator into thinking that the data it generated is from the original dataset. At the same time, the discriminator tries to not be fooled - it learns to become better at determining if data is real or fake. \n",
+ "\n",
+ "Since the two networks are fighting in this game, they can be seen as as adversaries, which is where the term \"Generative Adverserial Network\" comes from. \n",
+ "\n",
+ "### Deep Convolutional Generative Adversarial Networks\n",
+ "This notebook takes a look at Deep Convolutional Generative Adversarial Networks (DCGAN), which combines Convolutional Neural Networks (CNNs) ands GANs. \n",
+ "\n",
+ "We will create a DCGAN that is able to create images of handwritten digits from random numbers. \n",
+ "\n",
+ "The tutorial uses the neural net architecture and guidelines outlined in [this paper](https://arxiv.org/abs/1511.06434), and the MNIST dataset.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## How to Use This Tutorial"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "You can use this tutorial by executing each snippet of python code in order as it appears in the notebook. \n",
+ "\n",
+ "In this tutorial, we will train DCGAN on MNIST which will ultimately produces two neural networks:\n",
+ "- The first net is the \"generator\" and creates images of handwritten digits from random numbers.\n",
+ "\n",
+ "\n",
+ "- The second net is the \"discriminator\" and determines if the image created by the generator is real (a realistic looking image of handwritten digits) or fake (an image that doesn't look like it came from the original dataset). \n",
+ "\n",
+ "Apart from creating a DCGAN, you'll also learn:\n",
+ "\n",
+ "- How to manipulate and iterate through batches images that you can feed into your neural network.\n",
+ "\n",
+ "\n",
+ "- How to create a custom MXNet data iterator that generates random numbers from a normal distribution.\n",
+ "\n",
+ "\n",
+ "- How to create a custom training process in MXNet, using lower level functions from the [MXNet Module API](http://mxnet.io/api/python/module.html) such as `.bind()` `.forward()` and `.backward()`. The training process for a DCGAN is more complex than many other neural net's, so we need to use these functions instead of using the higher level `.fit()` function. \n",
+ "\n",
+ "\n",
+ "- How to visualize images as they are going through the training process"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## Prerequisites"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "This notebook assumes you're familiar with the concept of CNN's and have implemented one in MXNet. If you haven't, check out [this tutorial](https://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/mnist.ipynb), which walks you through implementing a CNN in MXNet. You should also be familiar with the concept of logistic regression. \n",
+ "\n",
+ "Having a basic understanding for MXNet data iterators helps, since we'll create a custom Data Iterator to iterate though random numbers as inputs to our generator network. Take a look at [this tutorial](https://github.com/dmlc/mxnet-notebooks/blob/master/python/basic/data.ipynb) for a better understanding of how MXNet `DataIter` works.\n",
+ "\n",
+ "This example is designed to be trained on a single GPU. Training this network on CPU can be slow, so it's recommended that you use a GPU for training. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "To complete this tutorial, you need:\n",
+ "\n",
+ "- [MXNet](http://mxnet.io/get_started/setup.html#overview)\n",
+ "- [Python 2.7](https://www.python.org/download/releases/2.7/), and the following libraries for Python: \n",
+ " - [Numpy](http://www.numpy.org/) - for matrix math\n",
+ " - [OpenCV](http://opencv.org/) - for image manipulation\n",
+ " - [Scikit-learn](http://scikit-learn.org/) - to easily get our dataset\n",
+ " - [Matplotlib](https://matplotlib.org/) - to visualize our output"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## The Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "We need two pieces of data to train our DCGAN:\n",
+ "1. Images of handwritten digits from the MNSIT dataset\n",
+ "2. Random numbers from a normal distribution\n",
+ "\n",
+ "Our generator network will use the random numbers as the input to produce images of handwritten digits, and out discriminator network will use images of handwritten digits from the MNIST dataset to determine if images produced by our generator are realistic.\n",
+ "\n",
+ "We are going to use the python library, scikit-learn, to get the MNIST dataset. Scikit-learn comes with a function that gets the dataset for us, which we will then manipulate to create our training and testing inputs. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "The MNIST dataset contains 70,000 images of handwritten digits. Each image is 28x28 pixels in size. \n",
+ "\n",
+ "\n",
+ "To create random numbers, we're going to create a custom MXNet data iterator, which will returns random numbers from a normal distribution as we need then. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## Prepare the Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "### 1. Preparing the MNSIT dataset\n",
+ "Let's start by preparing our handwritten digits from the MNIST dataset. We import the fetch_mldata function from scikit-learn, and use it to get the MNSIT dataset. Notice that it's shape is 70000x784. This contains the 70000 images on every row and 784 pixels of each image in the columns of each row. Each image is 28x28 pixels, but has been flattened so that all 784 images are represented in a single list."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(70000, 784)"
+ ]
+ },
+ "execution_count": 1,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from sklearn.datasets import fetch_mldata\n",
+ "mnist = fetch_mldata('MNIST original')\n",
+ "mnist.data.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Next, we'll randomize the handwritten digits by using numpy to create random permutations on the dataset on our rows (images). We'll then reshape our dataset from 70000x786 to 70000x28x28, so that every image in our dataset is arranged into a 28x28 grid, where each cell in the grid represents 1 pixel of the image. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "#Use a seed so that we get the same random permutation each time\n",
+ "np.random.seed(1)\n",
+ "p = np.random.permutation(mnist.data.shape[0])\n",
+ "X = mnist.data[p]\n",
+ "X = X.reshape((70000, 28, 28))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Since the DCGAN that we're creating takes in a 64x64 image as the input, we'll use OpenCV to resize the each 28x28 image to 64x64 images:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "import cv2\n",
+ "X = np.asarray([cv2.resize(x, (64,64)) for x in X])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Each pixel in our 64x64 image is represented by a number between 0-255, that represents the intensity of the pixel. However, we want to input numbers between -1 and 1 into our DCGAN, as suggested by the research paper. To rescale our pixels to be in the range of -1 to 1, we'll divide each pixel by (255/2). This put our images on a scale of 0-2. We can then subtract by 1, to get them in the range of -1 to 1."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "X = X.astype(np.float32)/(255.0/2) - 1.0"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Ultimately, images are inputted into our neural net from a 70000x3x64x64 array, and they are currently in a 70000x64x64 array. We need to add 3 channels to our images. Typically when we are working with images, the 3 channels represent the red, green, and blue components of each image. Since the MNIST dataset is grayscale, we only need 1 channel to represent our dataset. We will pad the other channels with 0's:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "X = X.reshape((70000, 1, 64, 64))\n",
+ "X = np.tile(X, (1, 3, 1, 1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Finally, we'll put our images into MXNet's NDArrayIter, which will allow MXNet to easily iterate through our images during training. We'll also split up them images into a batches, with 64 images in each batch. Every time we iterate, we'll get a 4 dimensional array with size `(64, 3, 64, 64)`, representing a batch of 64 images. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "import mxnet as mx\n",
+ "batch_size = 64\n",
+ "image_iter = mx.io.NDArrayIter(X, batch_size=batch_size)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## 2. Preparing Random Numbers"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "We need to input random numbers from a normal distribution to our generator network, so we'll create an MXNet DataIter that produces random numbers for each training batch. The `DataIter` is the base class of [MXNet's Data Loading API](http://mxnet.io/api/python/io.html). Below, we create a class called `RandIter` which is a subclass of `DataIter`. If you want to know more about how MXNet data loading works in python, please look at [this notebook](https://github.com/dmlc/mxnet-notebooks/blob/master/python/basic/data.ipynb). We use MXNet's built in `mx.random.normal` function in order to return the normally distributed random numbers every time we iterate. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "class RandIter(mx.io.DataIter):\n",
+ " def __init__(self, batch_size, ndim):\n",
+ " self.batch_size = batch_size\n",
+ " self.ndim = ndim\n",
+ " self.provide_data = [('rand', (batch_size, ndim, 1, 1))]\n",
+ " self.provide_label = []\n",
+ "\n",
+ " def iter_next(self):\n",
+ " return True\n",
+ "\n",
+ " def getdata(self):\n",
+ " #Returns random numbers from a gaussian (normal) distribution \n",
+ " #with mean=0 and standard deviation = 1\n",
+ " return [mx.random.normal(0, 1.0, shape=(self.batch_size, self.ndim, 1, 1))]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "When we initalize our `RandIter`, we need to provide two numbers: the batch size and how many random numbers we want to produce a single image from. This number is referred to as `Z`, and we'll set this to 100. This value comes from the research paper on the topic. Every time we iterate and get a batch of random numbers, we will get a 4 dimensional array with shape: `(batch_size, Z, 1, 1)`, which in our example is `(64, 100, 1, 1)`. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "Z = 100\n",
+ "rand_iter = RandIter(batch_size, Z)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## Create the Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Our model has two networks that we will train together - the generator network and the disciminator network. \n",
+ "Below is an illustration of our generator network:\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "\n",
+ "Source: https://arxiv.org/abs/1511.06434\n",
+ "\n",
+ "The discriminator works exactly the same way but in reverse - using convolutional layers instead of deconvolutional layers to take an image and determine if it is real or fake.\n",
+ "\n",
+ "The DCGAN paper recommends the following best practices for architecting DCGANs:\n",
+ "\n",
+ "- Replace any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).\n",
+ "- Use batchnorm in both the generator and the discriminator.\n",
+ "- Remove fully connected hidden layers for deeper architectures.\n",
+ "- Use ReLU activation in generator for all layers except for the output, which uses Tanh.\n",
+ "- Use LeakyReLU activation in the discriminator for all layers.\n",
+ "\n",
+ "Our model will implement these best practices."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "### The Generator\n",
+ "Let's start off by defining the generator network:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "no_bias = True\n",
+ "fix_gamma = True\n",
+ "epsilon = 1e-5 + 1e-12\n",
+ "\n",
+ "rand = mx.sym.Variable('rand')\n",
+ "\n",
+ "g1 = mx.sym.Deconvolution(rand, name='g1', kernel=(4,4), num_filter=1024, no_bias=no_bias)\n",
+ "gbn1 = mx.sym.BatchNorm(g1, name='gbn1', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "gact1 = mx.sym.Activation(gbn1, name='gact1', act_type='relu')\n",
+ "\n",
+ "g2 = mx.sym.Deconvolution(gact1, name='g2', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=512, no_bias=no_bias)\n",
+ "gbn2 = mx.sym.BatchNorm(g2, name='gbn2', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "gact2 = mx.sym.Activation(gbn2, name='gact2', act_type='relu')\n",
+ "\n",
+ "g3 = mx.sym.Deconvolution(gact2, name='g3', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=256, no_bias=no_bias)\n",
+ "gbn3 = mx.sym.BatchNorm(g3, name='gbn3', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "gact3 = mx.sym.Activation(gbn3, name='gact3', act_type='relu')\n",
+ "\n",
+ "g4 = mx.sym.Deconvolution(gact3, name='g4', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=128, no_bias=no_bias)\n",
+ "gbn4 = mx.sym.BatchNorm(g4, name='gbn4', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "gact4 = mx.sym.Activation(gbn4, name='gact4', act_type='relu')\n",
+ "\n",
+ "g5 = mx.sym.Deconvolution(gact4, name='g5', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=3, no_bias=no_bias)\n",
+ "generatorSymbol = mx.sym.Activation(g5, name='gact5', act_type='tanh')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Our generator image starts with random numbers that will be obtained from the `RandIter` we created earlier, so we created the `rand` variable for this input. \n",
+ "\n",
+ "We then start creating the model starting with a Deconvolution layer (sometimes called 'fractionally strided layer'). We apply batch normalization and ReLU activation after the Deconvolution layer.\n",
+ "\n",
+ "We repeat this process 4 times, applying a `(2,2)` stride and `(1,1)` pad at each Deconvolutional layer, which doubles the size of our image at each layer. By creating these layers, our generator network will have to learn to upsample our input vector of random numbers, `Z` at each layer, so that network output a final image. We also reduce half the number of filters at each layer, reducing dimensionality at each layer. Ultimatley, our output layer is a 64x64x3 layer, representing the size and channels of our image. We use tanh activation instead of relu on the last layer, as recommended by the research on DCGANs. The output of neurons in the final `gout` layer represent the pixels of generated image. \n",
+ "\n",
+ "Notice we used 3 parameters to help us create our model: no_bias, fixed_gamma, and epsilon.\n",
+ "Neurons in our network won't have a bias added to them, this seems to work better in practice for the DCGAN. \n",
+ "In our batch norm layer, we set `fixed_gamma=True`, which means `gamma=1` for all of our batch norm layers.\n",
+ "`epsilon` is a small number that gets added to our batch norm so that we don't end up dividing by zero. By default, CuDNN requires that this number is greater than `1e-5`, so we add a small number to this value, ensuring this values stays small."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "### The Discriminator"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Let's now create our discriminator network, which will take in images of handwritten digits from the MNIST dataset and images created by the generator network:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "data = mx.sym.Variable('data')\n",
+ "\n",
+ "d1 = mx.sym.Convolution(data, name='d1', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=128, no_bias=no_bias)\n",
+ "dact1 = mx.sym.LeakyReLU(d1, name='dact1', act_type='leaky', slope=0.2)\n",
+ "\n",
+ "d2 = mx.sym.Convolution(dact1, name='d2', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=256, no_bias=no_bias)\n",
+ "dbn2 = mx.sym.BatchNorm(d2, name='dbn2', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "dact2 = mx.sym.LeakyReLU(dbn2, name='dact2', act_type='leaky', slope=0.2)\n",
+ "\n",
+ "d3 = mx.sym.Convolution(dact2, name='d3', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=512, no_bias=no_bias)\n",
+ "dbn3 = mx.sym.BatchNorm(d3, name='dbn3', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "dact3 = mx.sym.LeakyReLU(dbn3, name='dact3', act_type='leaky', slope=0.2)\n",
+ "\n",
+ "d4 = mx.sym.Convolution(dact3, name='d4', kernel=(4,4), stride=(2,2), pad=(1,1), num_filter=1024, no_bias=no_bias)\n",
+ "dbn4 = mx.sym.BatchNorm(d4, name='dbn4', fix_gamma=fix_gamma, eps=epsilon)\n",
+ "dact4 = mx.sym.LeakyReLU(dbn4, name='dact4', act_type='leaky', slope=0.2)\n",
+ "\n",
+ "d5 = mx.sym.Convolution(dact4, name='d5', kernel=(4,4), num_filter=1, no_bias=no_bias)\n",
+ "d5 = mx.sym.Flatten(d5)\n",
+ "\n",
+ "label = mx.sym.Variable('label')\n",
+ "discriminatorSymbol = mx.sym.LogisticRegressionOutput(data=d5, label=label, name='dloss')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "We start off by creating the `data` variable, which is used to hold our input images to the discriminator.\n",
+ "\n",
+ "The discriminator then goes through a series of 5 convolutional layers, each with a 4x4 kernel, 2x2 stride, and 1x1 pad. These layers half the size of the image (which starts at 64x64) at each convolutional layer. Our model also increases dimensionality at each layer by doubling the number of filters per convolutional layer, starting at 128 filters and ending at 1024 filters before we flatten the output. \n",
+ "\n",
+ "At the final convolution, we flatten the neural net to get one number as the final output of discriminator network. This number is the probability the image is real, as determined by our discriminator. We use logistic regression to determine this probability. When we pass in \"real\" images from the MNIST dataset, we can label these as `1` and we can label the \"fake\" images from the generator net as `0` to perform logistic regression on the discriminator network. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "### Prepare the models using the `Module` API\n",
+ "\n",
+ "So far we have defined a MXNet `Symbol` for both the generator and the discriminator network.\n",
+ "Before we can train our model, we need to bind these symbols using the `Module` API, which creates the computation graph for our models. It also allows us to decide how we want to initialize our model and what type of optimizer we want to use. Let's set up `Module` for both of our networks:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "#Hyperperameters\n",
+ "sigma = 0.02\n",
+ "lr = 0.0002\n",
+ "beta1 = 0.5\n",
+ "ctx = mx.gpu(0)\n",
+ "\n",
+ "#=============Generator Module=============\n",
+ "generator = mx.mod.Module(symbol=generatorSymbol, data_names=('rand',), label_names=None, context=ctx)\n",
+ "generator.bind(data_shapes=rand_iter.provide_data)\n",
+ "generator.init_params(initializer=mx.init.Normal(sigma))\n",
+ "generator.init_optimizer(\n",
+ " optimizer='adam',\n",
+ " optimizer_params={\n",
+ " 'learning_rate': lr,\n",
+ " 'beta1': beta1,\n",
+ " })\n",
+ "mods = [generator]\n",
+ "\n",
+ "# =============Discriminator Module=============\n",
+ "discriminator = mx.mod.Module(symbol=discriminatorSymbol, data_names=('data',), label_names=('label',), context=ctx)\n",
+ "discriminator.bind(data_shapes=image_iter.provide_data,\n",
+ " label_shapes=[('label', (batch_size,))],\n",
+ " inputs_need_grad=True)\n",
+ "discriminator.init_params(initializer=mx.init.Normal(sigma))\n",
+ "discriminator.init_optimizer(\n",
+ " optimizer='adam',\n",
+ " optimizer_params={\n",
+ " 'learning_rate': lr,\n",
+ " 'beta1': beta1,\n",
+ " })\n",
+ "mods.append(discriminator)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "First, we create `Modules` for our networks and then bind the symbols that we've created in the previous steps to our modules. \n",
+ "\n",
+ "We use `rand_iter.provide_data` as the `data_shape` to bind our generator network. This means that as we iterate though batches of data on the generator `Module`, our `RandIter` will provide us with random numbers to feed our `Module` using it's `provide_data` function.\n",
+ "\n",
+ "Similarly, we bind the discriminator `Module` to `image_iter.provide_data`, which gives us images from MNIST from the `NDArrayIter` we had set up earlier, called `image_iter`. \n",
+ "\n",
+ "Notice that we're using the `Normal` initialization, with the hyperparameter `sigma=0.02`. This means our weight initializations for the neurons in our networks will random numbers from a Gaussian (normal) distribution with a mean of 0 and a standard deviation of 0.02. \n",
+ "\n",
+ "We also use the adam optimizer for gradient decent. We've set up two hyperparameters, `lr` and `beta1` based on the values used in the DCGAN paper. We're using a single gpu, `gpu(0)` for training.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "### Visualizing Our Training\n",
+ "\n",
+ "Before we train the model, let's set up some helper functions that will help visualize what our generator is producing, compared to what the real image is:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "collapsed": true,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [],
+ "source": [
+ "from matplotlib import pyplot as plt\n",
+ "\n",
+ "#Takes the images in our batch and arranges them in an array so that they can be\n",
+ "#Plotted using matplotlib\n",
+ "def fill_buf(buf, num_images, img, shape):\n",
+ " width = buf.shape[0]/shape[1]\n",
+ " height = buf.shape[1]/shape[0]\n",
+ " img_width = (num_images%width)*shape[0]\n",
+ " img_hight = (num_images/height)*shape[1]\n",
+ " buf[img_hight:img_hight+shape[1], img_width:img_width+shape[0], :] = img\n",
+ "\n",
+ "#Plots two images side by side using matplotlib\n",
+ "def visualize(fake, real):\n",
+ " #64x3x64x64 to 64x64x64x3\n",
+ " fake = fake.transpose((0, 2, 3, 1))\n",
+ " #Pixel values from 0-255\n",
+ " fake = np.clip((fake+1.0)*(255.0/2.0), 0, 255).astype(np.uint8)\n",
+ " #Repeat for real image\n",
+ " real = real.transpose((0, 2, 3, 1))\n",
+ " real = np.clip((real+1.0)*(255.0/2.0), 0, 255).astype(np.uint8)\n",
+ " \n",
+ " #Create buffer array that will hold all the images in our batch\n",
+ " #Fill the buffer so to arrange all images in the batch onto the buffer array\n",
+ " n = np.ceil(np.sqrt(fake.shape[0]))\n",
+ " fbuff = np.zeros((int(n*fake.shape[1]), int(n*fake.shape[2]), int(fake.shape[3])), dtype=np.uint8)\n",
+ " for i, img in enumerate(fake):\n",
+ " fill_buf(fbuff, i, img, fake.shape[1:3])\n",
+ " rbuff = np.zeros((int(n*real.shape[1]), int(n*real.shape[2]), int(real.shape[3])), dtype=np.uint8)\n",
+ " for i, img in enumerate(real):\n",
+ " fill_buf(rbuff, i, img, real.shape[1:3])\n",
+ " \n",
+ " #Create a matplotlib figure with two subplots: one for the real and the other for the fake\n",
+ " #fill each plot with our buffer array, which creates the image\n",
+ " fig = plt.figure()\n",
+ " ax1 = fig.add_subplot(2,2,1)\n",
+ " ax1.imshow(fbuff)\n",
+ " ax2 = fig.add_subplot(2,2,2)\n",
+ " ax2.imshow(rbuff)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "## Fit the Model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Training the DCGAN is a complex process that requires multiple steps.\n",
+ "\n",
+ "To fit the model, for every batch of data in our dataset:\n",
+ "\n",
+ "1. Use the `Z` vector, which contains our random numbers to do a forward pass through our generator. This outputs the \"fake\" image, since it's created from our generator.\n",
+ "\n",
+ "2. Use the fake image as the input to do a forward and backwards pass through the discriminator network. We set our labels for our logistic regression to `0` to represent that this is a fake image. This trains the discriminator to learn what a fake image looks like. We save the gradient produced in backpropogation for the next step.\n",
+ "\n",
+ "3. Do a forwards and backwards pass through the discriminator using a real image from our dataset. Our label for logistic regression will now be `1` to represent real images, so our discriminator can learn to recognize a real image.\n",
+ "\n",
+ "4. Update the discriminator by adding the result of the gradient generated during backpropogation on the fake image with the gradient from backpropogation on the real image. \n",
+ "\n",
+ "5. Now that the discriminator has been updated for the this batch, we still need to update the generator. First, do a forward and backwards pass with the same batch on the updated discriminator, to produce a new gradient. Use the new gradient to do a backwards pass\n",
+ "\n",
+ "\n",
+ "Here's the main training loop for our DCGAN:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": false,
+ "deletable": true,
+ "editable": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Training...\n",
+ "('epoch:', 0, 'iter:', 50)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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8LMxJtNWlyYo2muaOojOPMNpqI2dMUdIXMLpVpFN6vK1WfOerCOnb6Fi9mmZ5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\nqNFoNBn8BxZWP95dfm9uAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 100)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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AdV0sy+LKK6/k5ptv3m4f72NZFoIgUF1d/felh7tk27vF/F1UZJb3bkGxCswe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\n2trKSSedx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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 150)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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cTBi5q/zwuC9LIZkU3YpLqiUrV5z+C9lcLsr0PU6WmkRKrEydFBrS0lZTI2Yy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\n/0yEsaoEG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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 200)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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h1L2n4afzHsCso/aCVU9x582X4td33wQrsiFUBKux6dSxfrYPhULh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\nuW2FELjuuuvw3e9+dx0rH4APkdVfcMEFePHFF5Gm6ToP7t3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BliposYmENWi2RCoVRDeg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\n5hCigVQo0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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 250)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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rI4qpjGKQOCauPkyK/wuk6L3n/vvvz7IsWRQFa7XaOl/2nXfemTNmzFin3j33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\n6XhqbShk6eVsqoYucDRK0SYBhRDle0JQaEmhNAVAIVGWApkyX2tLGwS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dhXZ90LHh+/X7\nA2st4jjG3nvvvd7ax845TJ48+T23OXLkSIwYMeI9f/6DjPUdKf4SwHcBrBhKtANYSjJpnr8KYEV0\n+ygArwBA8/qypv1akSVEmhJhRWHOC3PwP669HXt88Uw898hjKIQSaZIi8xKQQC0BMqVhpIJpGIw7\naFvEPkRHm8ZTL76BJ+97GKnIU3xlmYLIgECGSLIGskaColWYus1HkIQeQUVi0uhtUEtqEEghTApK\nAZE0IKggUoG0keKHP/kuvnzKMfjJiRfgxXmv4bBj/xNEqpFGEsYC9RSA9VBSo95IIRoZhhoDEMhw\n71MLcPuf5iCtN6BNBics2EhgaCGTAAnqaCQJBDJE5RIWv3Y7Fi5ZgLnz5iEM2xCM9sikgEgTaGcA\nTYhUoBY7pGkNqAoce8E0fPtLX8bUvTthhhchvUDGOrLMgcggmaIQlJGkdTBNUQkl9t1zJzR0gji0\nCG2ARrZ5JE3aiNjg/Xp1cM7hsssuw9DQEO6//36EYfgOGyHESgK8/fbb8cgjj+B3v/sdLrroovVq\no1ar4brrrnsv7kFrjdmzZ+OYY455xzUpJa6++mq8/vrruO+++7D33nu/6/tPnz4dEyZMWC9bpRSu\nvfZavPDCC7j00kvXamutxeGHH44f//jHOOSQQ947qa/Hi+LPAPh183hvADcC6ADw/Co2owE80Tx+\nAkD3KtfmAehYzX1PRv4u5yEBsBiFbO8q8Dv7HZmL+fQM8rrbHsjjeR/8J6OwyGIUMwgqLLuQhSBg\nFEQsxZ53zbqPt/3l9+xdvIRZlvHa2bMYhZ7d7cM5dlgbi5Uiy3HMcqmNPihxwg5jOTTUz39cfRO/\nd+h/5oPz7mTJeRopGLSXWC6ELPiAxWLIqBSzEhT40ry5fOL+h/jUwsWsJnV+5WtfoQ88xxRLjMKY\nbT5kRxwx9iu2zIBtPmatPsQszdj7+pt0XTEDH3B4XGa5VGDJh4wKASPrqZXkv3xyGp94+jVmab6S\nPFBt8J6bH+KM/3YGS4WIpShioRKyElZoveaYrvF8atYdnP/8HNZrDaZZxiRJePKBB7McxvSFgIEz\n7CgPY7HYzp+cMI2vvPhPPvXMS5z9+By++NLL/P3vr+YO++3DCaUuluPSh2ahZUP167f3bbztZf8x\nxxzDhQsX8qSTTmIcx5w5cybvvffetS4Q7LTTTtxxxx05evTodxXhceyxx/LjH//4u1qMiOOYTz/9\nNHt6elYrX7r99tuv3OmwaNEi1ut1fvazn31XbbzwwgvrXEVfUWbMmMHnnnuOp5xyChctWvSOFeZV\ny1577bVyS1KapkyShDvttNO7XmhZn85zDvJvzJcAvAFgEMCVAJYA0E2b3QD8rXn8NwC7NY91006s\nrQ2pJI2xbItLVFJx8sQpNGGBEpKXnHsT+5YsbYorOQbW5QkMtGWxUKaznkd88mhefP193Gvb/Vks\nF2l9RKVzcao4ChkVAwbWMw4L1N6yEI3kwNAQ99rvBAohufMndqQUsrlRWuVhe87ROceCCymFpHcB\ndxm+NV3oGRW6WI4KtMbQK8PYVui9p1eOPoyptKIzhkltkGdOO4ZCCDptKJv6y067nAy9YyEI6Y2j\n1Iaz/3Yj40obBQSNsjzr8b/y4L13ZqVrRwZFS2cdQxew6EMarfnTo37OtNHgHpO2ogtHsp6kPPfM\nM6mVYqQ949AxDhwrPmRxdCfP/vUlzLKUz7z+MrU03GmnXXjpL/4npVEMrGXxQ6T7vDH6ddN25UO5\nyy67rBSsWlHnnGOapqvde7i6EgQBv/e9763V5he/+AWnTp3KH//4x2vd9Pz2UiwW2dPTwyzLOGbM\nmDXa3XXXXcyyjO3t7Ss3S69NbW/MmDFv0WPu7+/n448/vk5/Hn30Uc6aNWvlfstTTz11raJXP/nJ\nT9jV1UXvPaMoWhnL/b6T4tv+wXsDuLF5fC2Ao5vHlwA4tXn8VQCXNI+PBnDNuu6rlGQQRFRaUceW\ncZSHQgkhONRf43/6RK75GpaLdIFlHHpGxZiFKGIcBpy/+FWOHzORkVc85PNf5fZbT2LRKha722lt\nyKL3uXiTzrfsfPHr57HvzWXsOnIKf/ajQ3jTr6+lkIJSCTpvGMaOsXEMnKc1lkoo2jig1oLGaW6x\n3UforKRxmnFbxCgKGYWePiwyjAJqKbhs+RBrQ8soBThsXCcPOehrdIGndXmihkLgGIYFtkVtVEZz\n7Cc62bv4GULmv/eUid184oWnOWPaNTz7Fz9moRQzNp4uyvdpxsWIaZqye3iFQoC33vsMH7v/VkoB\nGmtojKFzOteUdp7eG765fDn7ly1m4A2Hb9XJ3836d3Z9ZByLbZ7FOKZzH86Ilg3Vr99Oin/84x95\n0003cdiwYW95WKdPn84LL7xwvYjLWsuenp612owaNYpTpkzheeedxx/96Efrdd+99tqLCxcuZJqm\nPOWUU9ZqO2fOnLckXDjnnHNYq9XWSLRz587lF77wBe6zzz688MILWa1W2dvbu9ZR3xVXXPGOUe5X\nvvIVbrHFFqu1v+6667hgwYK3ZPpZTdngpDgOwAMAnm92JNes983z55vXx62TFIVkub2NbSNL3GHU\nKH7m+On8whH7ceYlf2SaJjz/579jHIQc3z2WcdDFsgtY9DHjKOLIzpF88I57edqME/npw4/gvEef\n5fKBfi54dR63n7AbR5RixpUyQ+sYR0U6F3LnYw/nUFJj2pym9teHGAaeVkkG5TIrcchSGLG9rcS4\nHNJow9BHnHbSiazWa7z3qXv4LyecwEnjJ3CXEd0s+DAfjbmQoQ1YrsRMsyyPLPj8oazXqsyylN/4\n+c/pvGd7FLFQKjJ0nuWK5+iOYbzsy99mz5LlnDisi+1BzDf7l+dTgDTlbQ/fwkqxlO+fLIQc3jmS\n9/7zZaZJyqvOvpRf/v40ZlnGZxctojOKkXV01tIXAoZWs62UpxF7/dUlXNrfx298awaffnU+G0nC\nC396NqMw5LAoZjn+cKYO21D9+u2k+NRTT/HUU099x8N6/vnn81e/+tVqH+Qf/vCHvPzyy3n55Zdz\nn3324fHHH88nn3xyvYgOyPc5rmvKvdNOO3Hp0qVcsGABFy1atNYUY5MmTWKWZTz22GNX1iml2Nvb\nuzLUbkWRUvKOO+5gkiTs7+9nvV5fOVK++uqr15jx54wzzuBvfvObd9RfeeWVa4zSWbx4MYcPH76u\nv8cHaPO2lLTGc3znKIZRzCXzF7NRr7MzKLM8ZjIv+OHPqaSisxEDlycv8MrSF4r5hm1p6IsdlFIy\njgus1xNecPYPqZVkYB3D2DN0jsY4Cq0oheXi3uVMs4zPPvgQ//HcbAamRCUlnbUMI7Ny+uyMoxKC\nbXE7G7UhLutbznF7fZIzf3ERRxQqdMYyNiGD0Od6y1GJxmj+r1tnMU0Sfmmfj3PRm3kUTPvHt6OQ\n+bQ+KgQMnWPoI8ZhTKMM77vqLv717geoteW1v72DaZbxmAtOpAkq9JGmd/kUelSpk0EYcq9jj2dv\nzxI+NOdeNpIGZz04J499FoqBLjIKLWNrGbqI2hsGgeX5553M0pbjOWXC4exb3se4vUwpJa0yjPJv\n7g8dKW7IsupD+dBDD62WFHt7e1dLECvkOuv1PE5/xXuy448/fr1Jcdq0aZwxY8ZabeI45sc+9jE+\n8sgj60zxddRRRzHLsncQ0IUXXrgyLdqKYq1lo9Hg4OAg99xzz5WhfQMDA2t8pzhx4kTeeuut7wgf\nLBQKa435TpKEPT09HBwcZLVaXVPs9weJFAWdDRh4w28efRWTNOPYUZ0EBPeffBj/+xEnU9uA5TCi\n9pahN/SBZuRycXul85hkIQQ/9clP8MEr7qRsJl4wRtOG+QjLKkMI0GrBvT63G8dsP45TR+7Mxx57\nnN45GtuUU9WGgQsYuvz9oFGGV/35t7zt6ttpA8sT9jqU3SNGUUpJExgGzjEOLG2gGZj8veGI0V20\n1tN4yXq9xkVLF+SC9FLnkqJRwKAY0jlDKQS98Ryq1Xj1w7dxSsf2XN43yNvuvJtSKhZcQGvz1GSB\nCylVnlh2RFs7o2KFpXKRvUt7WXSFPP+iVfTO0q/wyzoKLWmNopCKUoL7TxjNjq7hlELk0S/e0LsW\nKW5IUrzlllt42mmnrTzv7Ozk3//+9zWS3MMPP8ytt96ahUKBl1xyCavV6pqSp76lrCCUrbbaim+8\n8cZqF0zeXg4++GAyd3itZbfddmOWZdx5551X1mmt+de//nW1I9hRo0a9o/3+/v41yrvec889nDRp\n0lvqJk+ezJ6eHkZRtEa/TjrpJN5xxx2cOXMmDz74YIZhyJtvvvmDS4pSCIbOcZdt9+DA0gGmScqR\nHeMoteLCxQvZ1zvAPQ/6AsdNHkPnHH1gGYQlxoUiC2GRf77hTp7zm+/z0afms3/xUnaN7KbWki7y\njELPQiFiEAR0xuXhf0YxHFfm/GVLmCb5StXvzruKEyaM4oixHbQVx0LRM44942KRQVxgbWiQTz7y\nGIulIl9/7Q0e/4VDaIxlMawwsCWGkae1Eb0Pc13pYoFtQZE9y/uZpik/feDn6U1I7fOEtMVigXGx\nyGKpwqhY4R7bbbMyldPs+/7BNE35xOMvMq50cMpWYxl2eMax5bBKyCD21EpzxLBubrP9Trzpgcc4\nUKvxuOM+zzBwLBbLDFzEKHL0kWfoY3rjqKym0YZfO/lUXjHzeu6y6+6sFNvp44hhIWQUfDinzxuL\nFK+88kqmacqDDjqIZ555Jnt7e9eY73AFKSZJwiRJeMMNN6zXyPCII47g7NmzOWvWLN54441rJcSb\nb76Z99xzD+fPn880TZllGW+66aa1JpKw1vKZZ55hX18fzz77bJ599tmcO3cuBwcHueP1DlP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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 300)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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vzHyF1Uo/SiEptaHRjsE4GuPpTUJvUzamDXxjyV+4dHU7pZQcs8VmHD14JJ3W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\n5kFf2p8QoJSC1oUMwyIj6+i8ZY2OGJUKHDyqN60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ullrJXNknNAyMpw89jbG01tGHAYWQ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+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 350)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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gKn7rK1dw/08fwb8+/zqtzYK+M2WcbFkvlaTUirIKfBCgs44nHfpFSitprGO+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\nXfD/ANWQ6R7Av4O3AAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "('epoch:', 0, 'iter:', 400)\n",
+ "\n",
+ " From generator: From MNIST:\n"
+ ]
+ }
+ ],
+ "source": [
+ "# =============train===============\n",
+ "print('Training...')\n",
+ "for epoch in range(1):\n",
+ " image_iter.reset()\n",
+ " for i, batch in enumerate(image_iter):\n",
+ " #Get a batch of random numbers to generate an image from the generator\n",
+ " rbatch = rand_iter.next()\n",
+ " #Forward pass on training batch\n",
+ " generator.forward(rbatch, is_train=True)\n",
+ " #Output of training batch is the 64x64x3 image\n",
+ " outG = generator.get_outputs()\n",
+ " \n",
+ " #Pass the generated (fake) image through the discriminator, and save the gradient\n",
+ " #Label (for logistic regression) is an array of 0's since this image is fake\n",
+ " label = mx.nd.zeros((batch_size,), ctx=ctx)\n",
+ " #Forward pass on the output of the discriminator network\n",
+ " discriminator.forward(mx.io.DataBatch(outG, [label]), is_train=True)\n",
+ " #Do the backwards pass and save the gradient\n",
+ " discriminator.backward()\n",
+ " gradD = [[grad.copyto(grad.context) for grad in grads] for grads in discriminator._exec_group.grad_arrays]\n",
+ " \n",
+ " #Pass a batch of real images from MNIST through the discriminator\n",
+ " #Set the label to be an array of 1's because these are the real images\n",
+ " label[:] = 1\n",
+ " batch.label = [label]\n",
+ " #Forward pass on a batch of MNIST images\n",
+ " discriminator.forward(batch, is_train=True)\n",
+ " #Do the backwards pass and add the saved gradient from the fake images to the gradient \n",
+ " #generated by this backwards pass on the real images\n",
+ " discriminator.backward()\n",
+ " for gradsr, gradsf in zip(discriminator._exec_group.grad_arrays, gradD):\n",
+ " for gradr, gradf in zip(gradsr, gradsf):\n",
+ " gradr += gradf\n",
+ " #Update gradient on the discriminator \n",
+ " discriminator.update()\n",
+ "\n",
+ " #Now that we've updated the discriminator, let's update the generator\n",
+ " #First do a forward pass and backwards pass on the newly updated discriminator\n",
+ " #With the current batch\n",
+ " discriminator.forward(mx.io.DataBatch(outG, [label]), is_train=True)\n",
+ " discriminator.backward()\n",
+ " #Get the input gradient from the backwards pass on the discriminator,\n",
+ " #and use it to do the backwards pass on the generator\n",
+ " diffD = discriminator.get_input_grads()\n",
+ " generator.backward(diffD)\n",
+ " #Update the gradients on the generator\n",
+ " generator.update()\n",
+ " \n",
+ " #Increment to the next batch, printing every 50 batches\n",
+ " i += 1\n",
+ " if i % 50 == 0:\n",
+ " print('epoch:', epoch, 'iter:', i)\n",
+ " print\n",
+ " print(\" From generator: From MNIST:\")\n",
+ "\n",
+ " visualize(outG[0].asnumpy(), batch.data[0].asnumpy())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "deletable": true,
+ "editable": true
+ },
+ "source": [
+ "Here we have our GAN being trained and we can visualize the progress that we're making as our networks train. After every 25 iterations, we're calling the `visualize` function that we created earlier, which creates the visual plots during training. \n",
+ "\n",
+ "The plot on our left is what our generator created (the fake image) in the most recent iteration. The plot on the right is the original (real) image from the MNIST dataset that was inputted to the discriminator on the same iteration. \n",
+ "\n",
+ "As training goes on the generator becomes better at generating realistic images. You can see this happening since images on the left become closer to the original dataset with each iteration. \n",
+ "\n",
+ "\n",
+ "## Summary\n",
+ "\n",
+ "We've now sucessfully used Apache MXNet to train a Deep Convolutional GAN using the MNIST dataset. \n",
+ "\n",
+ "As a result, we've created two neural nets: a generator, which is able to create images of handwritten digits from random numbers, and a discriminator, which is able to take an image and determine if it is an image of handwritten digits. \n",
+ "\n",
+ "Along the way, we've learned how to do the image manipulation and visualization that's associted with training deep neural nets. We've also learned how to some of MXNet's advanced training functionality to fit our model.\n",
+ "\n",
+ "## Acknowledgements\n",
+ "This tutorial is based on [MXNet DCGAN codebase](https://github.com/dmlc/mxnet/blob/master/example/gan/dcgan.py), the [original paper on GANs](https://arxiv.org/abs/1406.2661), as well as [this paper](https://arxiv.org/abs/1511.06434) on deep convolutional GANs."
+ ]
+ }
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