Tensorflow-DCGAN

参考：
1、https://github.com/awjuliani/TF-Tutorials
2、https://github.com/AYLIEN/gan-intro/blob/master/gan.py
3、https://github.com/tflearn/tflearn/tree/master/examples/images
5、https://github.com/carpedm20/DCGAN-tensorflow
6、https://github.com/carpedm20/BEGAN-tensorflow

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注意生成的图像的shape [n,32,32,1] （图像宽度和高度必须相等且满足2^m），所以原始mnist数据28x28 需转成32x32

#!/usr/bin/python3# -*- coding:utf-8 -*-from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_function"""https://github.com/awjuliani/TF-Tutorials/blob/master/DCGAN.ipynb"""#Import the libraries we will need.import tensorflow as tfimport numpy as npfrom tensorflow.examples.tutorials.mnist import input_dataimport matplotlib.pyplot as pltimport tensorflow.contrib.slim as slimimport osimport scipy.miscimport scipyfrom skimage import iomnist = input_data.read_data_sets("MNIST_data/", one_hot=False)train=1 # 1 train ;0 test# This function performns a leaky relu activation, which is needed for the discriminator network.def lrelu(x, leak=0.2, name="lrelu"):    with tf.variable_scope(name):        f1 = 0.5 * (1 + leak)        f2 = 0.5 * (1 - leak)        return f1 * x + f2 * abs(x)# The below functions are taken from carpdem20's implementation https://github.com/carpedm20/DCGAN-tensorflow# They allow for saving sample images from the generator to follow progressdef save_images(images, size, image_path):    return imsave(inverse_transform(images), size, image_path)def imsave(images, size, path):    return scipy.misc.imsave(path, merge(images, size))    # return scipy.misc.imsave(path, images)def inverse_transform(images):    return (images + 1.) / 2.def merge(images, size):    h, w = images.shape[1], images.shape[2]    img = np.zeros((h * size[0], w * size[1]))    for idx, image in enumerate(images):        i = idx % size[1]        j = idx // size[1]        img[j * h:j * h + h, i * w:i * w + w] = image    return imgdef generator(z):    '''    The generator takes a vector of random numbers and transforms it into a 32x32 image.    :param z: a vector of random numbers    :return: a 32x32 image    '''    with tf.variable_scope('G'):        zP = slim.fully_connected(z, 4 * 4 * 256, normalizer_fn=slim.batch_norm, \                                  activation_fn=tf.nn.relu, scope='g_project', weights_initializer=initializer)        zCon = tf.reshape(zP, [-1, 4, 4, 256]) # [n,4,4,256]        gen1 = slim.convolution2d_transpose( \            zCon, num_outputs=64, kernel_size=[5, 5], stride=[2, 2], \            padding="SAME", normalizer_fn=slim.batch_norm, \            activation_fn=tf.nn.relu, scope='g_conv1', weights_initializer=initializer) # [n,8,8,64] 采样方式SAME h*stride        gen2 = slim.convolution2d_transpose( \            gen1, num_outputs=32, kernel_size=[5, 5], stride=[2, 2], \            padding="SAME", normalizer_fn=slim.batch_norm, \            activation_fn=tf.nn.relu, scope='g_conv2', weights_initializer=initializer) # [n,16,16,32] 采样方式SAME h*stride        gen3 = slim.convolution2d_transpose( \            gen2, num_outputs=16, kernel_size=[5, 5], stride=[2, 2], \            padding="SAME", normalizer_fn=slim.batch_norm, \            activation_fn=tf.nn.relu, scope='g_conv3', weights_initializer=initializer) # [n,32,32,16] 采样方式SAME h*stride        g_out = slim.convolution2d_transpose( \            gen3, num_outputs=1, kernel_size=[32, 32], padding="SAME", \            biases_initializer=None, activation_fn=tf.nn.tanh, \            scope='g_out', weights_initializer=initializer) # [n,32,32,1] 这里stride默认为1    return g_outdef discriminator(bottom, reuse=False):    '''    The discriminator network takes as input a 32x32 image and     transforms it into a single valued probability of being generated from real-world data.    :param bottom: a 32x32 image    :param reuse:     :return: a single valued (0 or 1)    '''    with tf.variable_scope('D', reuse=reuse):        dis1 = slim.convolution2d(bottom, 16, [4, 4], stride=[2, 2], padding="SAME", \                                  biases_initializer=None, activation_fn=lrelu, \                                  reuse=reuse, scope='d_conv1', weights_initializer=initializer) # [n,15,15,16]        dis2 = slim.convolution2d(dis1, 32, [4, 4], stride=[2, 2], padding="SAME", \                                  normalizer_fn=slim.batch_norm, activation_fn=lrelu, \                                  reuse=reuse, scope='d_conv2', weights_initializer=initializer) # [n,8,8,32]        dis3 = slim.convolution2d(dis2, 64, [4, 4], stride=[2, 2], padding="SAME", \                                  normalizer_fn=slim.batch_norm, activation_fn=lrelu, \                                  reuse=reuse, scope='d_conv3', weights_initializer=initializer) # [n,4,4,64]        d_out = slim.fully_connected(slim.flatten(dis3), 1, activation_fn=tf.nn.sigmoid, \                                     reuse=reuse, scope='d_out', weights_initializer=initializer) # [n,1]    return d_out# Connecting them togethertf.reset_default_graph()z_size = 100 #Size of z vector used for generator.#This initializaer is used to initialize all the weights of the network.initializer = tf.truncated_normal_initializer(stddev=0.02)#These two placeholders are used for input into the generator and discriminator, respectively.z_in = tf.placeholder(shape=[None,z_size],dtype=tf.float32) #Random vectorreal_in = tf.placeholder(shape=[None,32,32,1],dtype=tf.float32) #Real imagesGz = generator(z_in) #Generates images from random z vectors [n,32,32,1]Dx = discriminator(real_in) #Produces probabilities for real images [n,1]Dg = discriminator(Gz,reuse=True) #Produces probabilities for generator images [n,1]#These functions together define the optimization objective of the GAN.d_loss = -tf.reduce_mean(tf.log(Dx) + tf.log(1.-Dg)) #This optimizes the discriminator.g_loss = -tf.reduce_mean(tf.log(Dg)) #This optimizes the generator.tvars = tf.trainable_variables()d_params = [v for v in tvars if v.name.startswith('D/')]g_params = [v for v in tvars if v.name.startswith('G/')]#The below code is responsible for applying gradient descent to update the GAN.trainerD = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.5)trainerG = tf.train.AdamOptimizer(learning_rate=0.0002,beta1=0.5)# d_grads = trainerD.compute_gradients(d_loss,tvars[9:]) #Only update the weights for the discriminator network.# g_grads = trainerG.compute_gradients(g_loss,tvars[0:9]) #Only update the weights for the generator network.d_grads = trainerD.compute_gradients(d_loss,d_params) #Only update the weights for the discriminator network.g_grads = trainerG.compute_gradients(g_loss,g_params) #Only update the weights for the generator network.update_D = trainerD.apply_gradients(d_grads)update_G = trainerG.apply_gradients(g_grads)# Training the networkbatch_size = 128 #Size of image batch to apply at each iteration.iterations = 500000 #Total number of iterations to use.sample_directory = './figs' #Directory to save sample images from generator in.model_directory = './models' #Directory to save trained model to.if not os.path.exists(sample_directory):os.makedirs(sample_directory)if not os.path.exists(model_directory):os.makedirs(model_directory)init = tf.global_variables_initializer()saver = tf.train.Saver()if train:    with tf.Session() as sess:        sess.run(init)        for i in range(iterations):            zs = np.random.uniform(-1.0,1.0,size=[batch_size,z_size]).astype(np.float32) #Generate a random z batch            xs,_ = mnist.train.next_batch(batch_size) #Draw a sample batch from MNIST dataset.            xs = (np.reshape(xs,[batch_size,28,28,1]) - 0.5) * 2.0 #Transform it to be between -1 and 1            xs = np.lib.pad(xs, ((0,0),(2,2),(2,2),(0,0)),'constant', constant_values=(-1, -1)) #Pad the images so the are 32x32            _,dLoss = sess.run([update_D,d_loss],feed_dict={z_in:zs,real_in:xs}) #Update the discriminator            _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs}) #Update the generator, twice for good measure.            _,gLoss = sess.run([update_G,g_loss],feed_dict={z_in:zs})            if i % 10 == 0:                print("Gen Loss: " + str(gLoss) + " Disc Loss: " + str(dLoss))                z2 = np.random.uniform(-1.0,1.0,size=[batch_size,z_size]).astype(np.float32) #Generate another z batch                newZ = sess.run(Gz,feed_dict={z_in:z2}) #Use new z to get sample images from generator.                if not os.path.exists(sample_directory):                    os.makedirs(sample_directory)                #Save sample generator images for viewing training progress.                save_images(np.reshape(newZ[0:36],[36,32,32]),[6,6],sample_directory+'/fig'+str(i)+'.png')                # 将6x6个 大小32x32的图像整合在一起保存 保存后的图像大小32*6=192 192x192            if i % 1000 == 0 and i != 0:                if not os.path.exists(model_directory):                    os.makedirs(model_directory)                saver.save(sess,model_directory+'/model-'+str(i)+'.cptk')                print("Saved Model")else:    # Using a trained network    # sample_directory = './figs'  # Directory to save sample images from generator in.    # model_directory = './models'  # Directory to load trained model from.    batch_size_sample = 36    init = tf.global_variables_initializer()    saver = tf.train.Saver()    with tf.Session() as sess:        sess.run(init)        # Reload the model.        print('Loading Model...')        ckpt = tf.train.get_checkpoint_state(model_directory)        saver.restore(sess, ckpt.model_checkpoint_path)        zs = np.random.uniform(-1.0, 1.0, size=[batch_size_sample, z_size]).astype(np.float32)  # Generate a random z batch        newZ = sess.run(Gz, feed_dict={z_in: zs})  # Use new z to get sample images from generator.        if not os.path.exists(sample_directory):            os.makedirs(sample_directory)        save_images(np.reshape(newZ[0:batch_size_sample], [36, 32, 32]), [6, 6],sample_directory + '/fig_test' + '.png') # 192x192        # images=np.reshape(newZ[0:batch_size_sample], [36, 32, 32])        # [io.imsave(sample_directory + '/fig' + str(i) + '.png',images[i]) for i in range(batch_size_sample)] # 保存成32x32的图像        # [scipy.misc.imsave(sample_directory + '/fig' + str(i) + '.png',images[i]) for i in range(batch_size_sample)] # 保存成32x32的图像