《TensorFlow技术解析与实战》之 生成式对抗网络

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 《TensorFlow技术解析与实战》13 生成式对抗网络
# win10 Tensorflow-gpu1.2.0 python3.5.3
# CUDA v8.0 cudnn-8.0-windows10-x64-v5.1
# filename:nntf13.01.py mnist的ac-gan
# 源码：https://github.com/fchollet/keras/blob/master/examples/mnist_acgan.py

"""
Train an Auxiliary Classifier Generative Adversarial Network (ACGAN) on the
MNIST dataset. See https://arxiv.org/abs/1610.09585 for more details.

You should start to see reasonable images after ~5 epochs, and good images
by ~15 epochs. You should use a GPU, as the convolution-heavy operations are
very slow on the CPU. Prefer the TensorFlow backend if you plan on iterating,
as the compilation time can be a blocker using Theano.

Timings:

Hardware           | Backend | Time / Epoch
-------------------------------------------
 CPU               | TF      | 3 hrs
 Titan X (maxwell) | TF      | 4 min
 Titan X (maxwell) | TH      | 7 min

Consult https://github.com/lukedeo/keras-acgan for more information and
example output
"""
from __future__ import print_function

from collections import defaultdict
try:
    import cPickle as pickle
except ImportError:
    import pickle
from PIL import Image

from six.moves import range

import keras.backend as K
from keras.datasets import mnist
from keras import layers
from keras.layers import Input, Dense, Reshape, Flatten, Embedding, Dropout
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam
from keras.utils.generic_utils import Progbar
import numpy as np

np.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)))

    # upsample to (..., 14, 14)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(256, 5, padding='same',
                   activation='relu',
                   kernel_initializer='glorot_normal'))

    # upsample to (..., 28, 28)
    cnn.add(UpSampling2D(size=(2, 2)))
    cnn.add(Conv2D(128, 5, padding='same',
                   activation='relu',
                   kernel_initializer='glorot_normal'))

    # take a channel axis reduction
    cnn.add(Conv2D(1, 2, padding='same',
                   activation='tanh',
                   kernel_initializer='glorot_normal'))

    # this is the z space commonly refered to in GAN papers
    latent = Input(shape=(latent_size, ))

    # this will be our label
    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)

    return Model([latent, image_class], fake_image)


def build_discriminator():
    # build a relatively standard conv net, with LeakyReLUs as suggested in
    # the reference paper
    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)

    # first output (name=generation) is whether or not the discriminator
    # thinks the image that is being shown is fake, and the second output
    # (name=auxiliary) is the class that the discriminator thinks the image
    # belongs to.
    fake = Dense(1, activation='sigmoid', name='generation')(features)
    aux = Dense(10, activation='softmax', name='auxiliary')(features)

    return Model(image, [fake, aux])

if __name__ == '__main__':

    # batch and latent size taken from the paper
    epochs = 50
    batch_size = 100
    latent_size = 100

    # Adam parameters suggested in https://arxiv.org/abs/1511.06434
    adam_lr = 0.0002
    adam_beta_1 = 0.5

    # build the discriminator
    discriminator = build_discriminator()
    discriminator.compile(
        optimizer=Adam(lr=adam_lr, beta_1=adam_beta_1),
        loss=['binary_crossentropy', 'sparse_categorical_crossentropy']
    )

    # build the generator
    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')

    # get a fake image
    fake = generator([latent, image_class])

    # we only want to be able to train generation for the combined model
    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']
    )

    # get our mnist data, and force it to be of shape (..., 1, 28, 28) with
    # range [-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)
            # generate a new batch of noise
            noise = np.random.uniform(-1, 1, (batch_size, latent_size))

            # get a batch of real images
            image_batch = X_train[index * batch_size:(index + 1) * batch_size]
            label_batch = y_train[index * batch_size:(index + 1) * batch_size]

            # sample some labels from p_c
            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'))
'''
...
598/600 [============================>.] - ETA: 1s
599/600 [============================>.] - ETA: 0s
Testing for epoch 2:
component              | loss | generation_loss | auxiliary_loss
-----------------------------------------------------------------
generator (train)      | 9.37 | 7.06            | 2.31 
generator (test)       | 9.61 | 7.30            | 2.30 
discriminator (train)  | 1.24 | 0.00            | 1.24 
discriminator (test)   | 1.20 | 0.00            | 1.20 
Epoch 3 of 50
  0/600 [..............................] - ETA: 0s
  1/600 [..............................] - ETA: 479s
  ...
'''