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examples/3_NeuralNetworks/dcgan.py
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陈敏华
Update dcgan.py (#320)
05 авг 2019, 08:24
05 авг 2019, 08:24
11380c8
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""" Deep Convolutional Generative Adversarial Network (DCGAN). Using deep convolutional generative adversarial networks (DCGAN) to generate digit images from a noise distribution. References: - Unsupervised representation learning with deep convolutional generative adversarial networks. A Radford, L Metz, S Chintala. arXiv:1511.06434. Links: - [DCGAN Paper](https://arxiv.org/abs/1511.06434). - [MNIST Dataset](http://yann.lecun.com/exdb/mnist/). Author: Aymeric Damien Project: https://github.com/aymericdamien/TensorFlow-Examples/ """ from __future__ import division, print_function, absolute_import import matplotlib.pyplot as plt import numpy as np import tensorflow as tf # Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/tmp/data/", one_hot=True) # Training Params num_steps = 20000 batch_size = 32 # Network Params image_dim = 784 # 28*28 pixels * 1 channel gen_hidden_dim = 256 disc_hidden_dim = 256 noise_dim = 200 # Noise data points # Generator Network # Input: Noise, Output: Image def generator(x, reuse=False): with tf.variable_scope('Generator', reuse=reuse): # TensorFlow Layers automatically create variables and calculate their # shape, based on the input. x = tf.layers.dense(x, units=6 * 6 * 128) x = tf.nn.tanh(x) # Reshape to a 4-D array of images: (batch, height, width, channels) # New shape: (batch, 6, 6, 128) x = tf.reshape(x, shape=[-1, 6, 6, 128]) # Deconvolution, image shape: (batch, 14, 14, 64) x = tf.layers.conv2d_transpose(x, 64, 4, strides=2) # Deconvolution, image shape: (batch, 28, 28, 1) x = tf.layers.conv2d_transpose(x, 1, 2, strides=2) # Apply sigmoid to clip values between 0 and 1 x = tf.nn.sigmoid(x) return x # Discriminator Network # Input: Image, Output: Prediction Real/Fake Image def discriminator(x, reuse=False): with tf.variable_scope('Discriminator', reuse=reuse): # Typical convolutional neural network to classify images. x = tf.layers.conv2d(x, 64, 5) x = tf.nn.tanh(x) x = tf.layers.average_pooling2d(x, 2, 2) x = tf.layers.conv2d(x, 128, 5) x = tf.nn.tanh(x) x = tf.layers.average_pooling2d(x, 2, 2) x = tf.contrib.layers.flatten(x) x = tf.layers.dense(x, 1024) x = tf.nn.tanh(x) # Output 2 classes: Real and Fake images x = tf.layers.dense(x, 2) return x # Build Networks # Network Inputs noise_input = tf.placeholder(tf.float32, shape=[None, noise_dim]) real_image_input = tf.placeholder(tf.float32, shape=[None, 28, 28, 1]) # Build Generator Network gen_sample = generator(noise_input) # Build 2 Discriminator Networks (one from real image input, one from generated samples) disc_real = discriminator(real_image_input) disc_fake = discriminator(gen_sample, reuse=True) disc_concat = tf.concat([disc_real, disc_fake], axis=0) # Build the stacked generator/discriminator stacked_gan = discriminator(gen_sample, reuse=True) # Build Targets (real or fake images) disc_target = tf.placeholder(tf.int32, shape=[None]) gen_target = tf.placeholder(tf.int32, shape=[None]) # Build Loss disc_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=disc_concat, labels=disc_target)) gen_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits( logits=stacked_gan, labels=gen_target)) # Build Optimizers optimizer_gen = tf.train.AdamOptimizer(learning_rate=0.001) optimizer_disc = tf.train.AdamOptimizer(learning_rate=0.001) # Training Variables for each optimizer # By default in TensorFlow, all variables are updated by each optimizer, so we # need to precise for each one of them the specific variables to update. # Generator Network Variables gen_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Generator') # Discriminator Network Variables disc_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='Discriminator') # Create training operations train_gen = optimizer_gen.minimize(gen_loss, var_list=gen_vars) train_disc = optimizer_disc.minimize(disc_loss, var_list=disc_vars) # Initialize the variables (i.e. assign their default value) init = tf.global_variables_initializer() # Start training with tf.Session() as sess: # Run the initializer sess.run(init) for i in range(1, num_steps+1): # Prepare Input Data # Get the next batch of MNIST data (only images are needed, not labels) batch_x, _ = mnist.train.next_batch(batch_size) batch_x = np.reshape(batch_x, newshape=[-1, 28, 28, 1]) # Generate noise to feed to the generator z = np.random.uniform(-1., 1., size=[batch_size, noise_dim]) # Prepare Targets (Real image: 1, Fake image: 0) # The first half of data fed to the discriminator are real images, # the other half are fake images (coming from the generator). batch_disc_y = np.concatenate( [np.ones([batch_size]), np.zeros([batch_size])], axis=0) # Generator tries to fool the discriminator, thus targets are 1. batch_gen_y = np.ones([batch_size]) # Training feed_dict = {real_image_input: batch_x, noise_input: z, disc_target: batch_disc_y, gen_target: batch_gen_y} _, _, gl, dl = sess.run([train_gen, train_disc, gen_loss, disc_loss], feed_dict=feed_dict) if i % 100 == 0 or i == 1: print('Step %i: Generator Loss: %f, Discriminator Loss: %f' % (i, gl, dl)) # Generate images from noise, using the generator network. f, a = plt.subplots(4, 10, figsize=(10, 4)) for i in range(10): # Noise input. z = np.random.uniform(-1., 1., size=[4, noise_dim]) g = sess.run(gen_sample, feed_dict={noise_input: z}) for j in range(4): # Generate image from noise. Extend to 3 channels for matplot figure. img = np.reshape(np.repeat(g[j][:, :, np.newaxis], 3, axis=2), newshape=(28, 28, 3)) a[j][i].imshow(img) f.show() plt.draw() plt.waitforbuttonpress()