使用DCGAN生成动漫人物头像

$$使用DCGAN生成动漫人物头像$$

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import glob
import os

os.listdir('../input/anime-faces/data/data')[:10]

print('Tensorflow version: {}'.format(tf.__version__))

image_path = glob.glob('../input/anime-faces/data/data/*.png')

len(image_path)

def load_preprosess_image(path):
    image = tf.io.read_file(path)
    image = tf.image.decode_png(image, channels=3)
#    image = tf.image.resize_with_crop_or_pad(image, 256, 256)
    image = tf.cast(image, tf.float32)
    image = (image / 127.5) - 1
    return image

image_ds = tf.data.Dataset.from_tensor_slices(image_path)

AUTOTUNE = tf.data.experimental.AUTOTUNE

image_ds = image_ds.map(load_preprosess_image, num_parallel_calls=AUTOTUNE)

image_ds

BATCH_SIZE = 64
image_count = len(image_path)

image_ds = image_ds.shuffle(image_count).batch(BATCH_SIZE)
image_ds = image_ds.prefetch(AUTOTUNE)

def generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(8*8*256, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((8, 8, 256)))   #8*8*256

    model.add(layers.Conv2DTranspose(128, (5, 5), strides=(1, 1), padding='same', use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())             #8*8*128

    model.add(layers.Conv2DTranspose(64, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())             #16*16*128
    
    model.add(layers.Conv2DTranspose(32, (5, 5), strides=(2, 2), padding='same', use_bias=False))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())             #32*32*32
    

    model.add(layers.Conv2DTranspose(3, (5, 5), strides=(2, 2), padding='same', use_bias=False, activation='tanh'))
                                              #64*64*3
    
    return model

generator = generator_model()

noise = tf.random.normal([1, 100])
generated_image = generator(noise, training=False)

plt.imshow((generated_image[0, :, :, :3] + 1)/2)

def discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(32, (5, 5), strides=(2, 2), padding='same',
                                     input_shape=[64, 64, 3]))
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))      # 32*32*32

    model.add(layers.Conv2D(64, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
    model.add(layers.Dropout(0.3))      # 16*16*64
    
    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
#    model.add(layers.Dropout(0.3))      # 8*8*128
    
    model.add(layers.Conv2D(256, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())       # 4*4*256

    model.add(layers.GlobalAveragePooling2D())
    
    model.add(layers.Dense(1024))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())
    model.add(layers.Dense(1))

    return model

discriminator = discriminator_model()
decision = discriminator(generated_image)
print(decision)

cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)

def discriminator_loss(real_output, fake_output):
    real_loss = cross_entropy(tf.ones_like(real_output), real_output)
    fake_loss = cross_entropy(tf.zeros_like(fake_output), fake_output)
    total_loss = real_loss + fake_loss
    return total_loss

def generator_loss(fake_output):
    return cross_entropy(tf.ones_like(fake_output), fake_output)

generator_optimizer = tf.keras.optimizers.Adam(1e-5)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-5)

EPOCHS = 800
noise_dim = 100
num_examples_to_generate = 4

seed = tf.random.normal([num_examples_to_generate, noise_dim])

@tf.function
def train_step(images):
    noise = tf.random.normal([BATCH_SIZE, noise_dim])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)

        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)

        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

    gradients_of_generator = gen_tape.gradient(gen_loss, generator.trainable_variables)
    gradients_of_discriminator = disc_tape.gradient(disc_loss, discriminator.trainable_variables)

    generator_optimizer.apply_gradients(zip(gradients_of_generator, generator.trainable_variables))
    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator, discriminator.trainable_variables))

def generate_and_save_images(model, epoch, test_input):
  # Notice `training` is set to False.
  # This is so all layers run in inference mode (batchnorm).
    predictions = model(test_input, training=False)

    fig = plt.figure(figsize=(6, 6))

    for i in range(predictions.shape[0]):
        plt.subplot(2, 2, i+1)
        plt.imshow((predictions[i, :, :, :] + 1)/2)
        plt.axis('off')

#    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))
    plt.show()

def train(dataset, epochs):
    for epoch in range(epochs):
        for image_batch in dataset:
            train_step(image_batch)
#            print('.', end='')
#        print()
        if epoch%10 == 0:
            generate_and_save_images(generator,
                                     epoch + 1,
                                     seed)


    generate_and_save_images(generator,
                           epochs,
                           seed)

train(image_ds, EPOCHS)