TensorFlow栗子：生成式对抗网络应用在mnist

python3.5

TensorFlow 1.3

生成式对抗网络（GAN)包含：生成模型和一个判别模型。

生成式对抗网络主要解决的问题是如何从训练样本中学习出新样本。

from __future__ import print_functionfrom collections import defaultdicttry:    import cPickle as pickleexcept ImportError:    import picklefrom PIL import Imagefrom six.moves import rangeimport keras.backend as Kfrom keras.datasets import mnistfrom keras import layersfrom keras.layers import Input, Dense, Reshape, Flatten, Embedding, Dropoutfrom keras.layers.advanced_activations import LeakyReLUfrom keras.layers.convolutional import UpSampling2D, Conv2Dfrom keras.models import Sequential, Modelfrom keras.optimizers import Adamfrom keras.utils.generic_utils import Progbarimport numpy as npnp.random.seed(1337)K.set_image_data_format('channels_first')def build_generator(latent_size):    # we will map a pair of (z, L), where z is a latent vector and L is a    # label drawn from P_c, to image space (..., 1, 28, 28)    cnn = Sequential()    cnn.add(Dense(1024, input_dim=latent_size, activation='relu'))    cnn.add(Dense(128 * 7 * 7, activation='relu'))    cnn.add(Reshape((128, 7, 7)))    # 图片采样为14×14    cnn.add(UpSampling2D(size=(2, 2)))    cnn.add(Conv2D(256, 5, padding='same',                   activation='relu',                   kernel_initializer='glorot_normal'))    # upsample to (图像尺寸变为28×28)    cnn.add(UpSampling2D(size=(2, 2)))    cnn.add(Conv2D(128, 5, padding='same',                   activation='relu',                   kernel_initializer='glorot_normal'))    # 规约到一个通道    cnn.add(Conv2D(1, 2, padding='same',                   activation='tanh',                   kernel_initializer='glorot_normal'))    # 生成模型的输入层，特征向量    latent = Input(shape=(latent_size, ))    # 生成模型的输入层，标记    image_class = Input(shape=(1,), dtype='int32')    # 10 classes in MNIST    cls = Flatten()(Embedding(10, latent_size,                              embeddings_initializer='glorot_normal')(image_class))    # hadamard product between z-space and a class conditional embedding    h = layers.multiply([latent, cls])    fake_image = cnn(h)#虚假图片    return Model([latent, image_class], fake_image)def build_discriminator():    # 采用激活函数Leaky Relu来替代标准的卷积神经网罗中的激活函数    cnn = Sequential()    cnn.add(Conv2D(32, 3, padding='same', strides=2,                   input_shape=(1, 28, 28)))    cnn.add(LeakyReLU())    cnn.add(Dropout(0.3))    cnn.add(Conv2D(64, 3, padding='same', strides=1))    cnn.add(LeakyReLU())    cnn.add(Dropout(0.3))    cnn.add(Conv2D(128, 3, padding='same', strides=2))    cnn.add(LeakyReLU())    cnn.add(Dropout(0.3))    cnn.add(Conv2D(256, 3, padding='same', strides=1))    cnn.add(LeakyReLU())    cnn.add(Dropout(0.3))    cnn.add(Flatten())    image = Input(shape=(1, 28, 28))    features = cnn(image)    # 有两个输出，输出真假值    fake = Dense(1, activation='sigmoid', name='generation')(features)    aux = Dense(10, activation='softmax', name='auxiliary')(features)    return Model(image, [fake, aux])if __name__ == '__main__':    # 定义超参数    epochs = 50    batch_size = 100    latent_size = 100    # Adam parameters suggested in https://arxiv.org/abs/1511.06434    #优化器学习率    adam_lr = 0.0002    adam_beta_1 = 0.5    # 构建判别网络    discriminator = build_discriminator()    discriminator.compile(        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']    )    # 构建生成网络    generator = build_generator(latent_size)    generator.compile(optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),                      loss='binary_crossentropy')    latent = Input(shape=(latent_size, ))    image_class = Input(shape=(1,), dtype='int32')    # 生成虚假图片    fake = generator([latent, image_class])    # 生成组合模型    discriminator.trainable = False    fake, aux = discriminator(fake)    combined = Model([latent, image_class], [fake, aux])    combined.compile(        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']    )    # 将mnist数据转化维度，并取值范围为[-1,1]    (X_train, y_train), (X_test, y_test) = mnist.load_data()    X_train = (X_train.astype(np.float32) - 127.5) / 127.5    X_train = np.expand_dims(X_train, axis=1)    X_test = (X_test.astype(np.float32) - 127.5) / 127.5    X_test = np.expand_dims(X_test, axis=1)    num_train, num_test = X_train.shape[0], X_test.shape[0]    train_history = defaultdict(list)    test_history = defaultdict(list)    for epoch in range(epochs):        print('Epoch {} of {}'.format(epoch + 1, epochs))        num_batches = int(X_train.shape[0] / batch_size)        progress_bar = Progbar(target=num_batches)        epoch_gen_loss = []        epoch_disc_loss = []        for index in range(num_batches):            progress_bar.update(index)            # 产生一个批次的噪声            noise = np.random.uniform(-1, 1, (batch_size, latent_size))            # 获得一个批次的真是数据            image_batch = X_train[index * batch_size:(index + 1) * batch_size]            label_batch = y_train[index * batch_size:(index + 1) * batch_size]            # 标记噪声            sampled_labels = np.random.randint(0, 10, batch_size)            # generate a batch of fake images, using the generated labels as a            # conditioner. We reshape the sampled labels to be            # (batch_size, 1) so that we can feed them into the embedding            # layer as a length one sequence            generated_images = generator.predict(                [noise, sampled_labels.reshape((-1, 1))], verbose=0)            X = np.concatenate((image_batch, generated_images))            y = np.array([1] * batch_size + [0] * batch_size)            aux_y = np.concatenate((label_batch, sampled_labels), axis=0)            # see if the discriminator can figure itself out...            epoch_disc_loss.append(discriminator.train_on_batch(X, [y, aux_y]))            # make new noise. we generate 2 * batch size here such that we have            # the generator optimize over an identical number of images as the            # discriminator            noise = np.random.uniform(-1, 1, (2 * batch_size, latent_size))            sampled_labels = np.random.randint(0, 10, 2 * batch_size)            # we want to train the generator to trick the discriminator            # For the generator, we want all the {fake, not-fake} labels to say            # not-fake            trick = np.ones(2 * batch_size)            epoch_gen_loss.append(combined.train_on_batch(                [noise, sampled_labels.reshape((-1, 1))],                [trick, sampled_labels]))        print('\nTesting for epoch {}:'.format(epoch + 1))        # evaluate the testing loss here        # generate a new batch of noise        noise = np.random.uniform(-1, 1, (num_test, latent_size))        # sample some labels from p_c and generate images from them        sampled_labels = np.random.randint(0, 10, num_test)        generated_images = generator.predict(            [noise, sampled_labels.reshape((-1, 1))], verbose=False)        X = np.concatenate((X_test, generated_images))        y = np.array([1] * num_test + [0] * num_test)        aux_y = np.concatenate((y_test, sampled_labels), axis=0)        # see if the discriminator can figure itself out...        discriminator_test_loss = discriminator.evaluate(            X, [y, aux_y], verbose=False)        discriminator_train_loss = np.mean(np.array(epoch_disc_loss), axis=0)        # make new noise        noise = np.random.uniform(-1, 1, (2 * num_test, latent_size))        sampled_labels = np.random.randint(0, 10, 2 * num_test)        trick = np.ones(2 * num_test)        generator_test_loss = combined.evaluate(            [noise, sampled_labels.reshape((-1, 1))],            [trick, sampled_labels], verbose=False)        generator_train_loss = np.mean(np.array(epoch_gen_loss), axis=0)        # generate an epoch report on performance        train_history['generator'].append(generator_train_loss)        train_history['discriminator'].append(discriminator_train_loss)        test_history['generator'].append(generator_test_loss)        test_history['discriminator'].append(discriminator_test_loss)        print('{0:<22s} | {1:4s} | {2:15s} | {3:5s}'.format(            'component', *discriminator.metrics_names))        print('-' * 65)        ROW_FMT = '{0:<22s} | {1:<4.2f} | {2:<15.2f} | {3:<5.2f}'        print(ROW_FMT.format('generator (train)',                             *train_history['generator'][-1]))        print(ROW_FMT.format('generator (test)',                             *test_history['generator'][-1]))        print(ROW_FMT.format('discriminator (train)',                             *train_history['discriminator'][-1]))        print(ROW_FMT.format('discriminator (test)',                             *test_history['discriminator'][-1]))        # save weights every epoch        generator.save_weights(            'params_generator_epoch_{0:03d}.hdf5'.format(epoch), True)        discriminator.save_weights(            'params_discriminator_epoch_{0:03d}.hdf5'.format(epoch), True)        # generate some digits to display        noise = np.random.uniform(-1, 1, (100, latent_size))        sampled_labels = np.array([            [i] * 10 for i in range(10)        ]).reshape(-1, 1)        # get a batch to display        generated_images = generator.predict(            [noise, sampled_labels], verbose=0)        # arrange them into a grid        img = (np.concatenate([r.reshape(-1, 28)                               for r in np.split(generated_images, 10)                               ], axis=-1) * 127.5 + 127.5).astype(np.uint8)        Image.fromarray(img).save(            'plot_epoch_{0:03d}_generated.png'.format(epoch))    pickle.dump({'train': train_history, 'test': test_history},                open('acgan-history.pkl', 'wb'))