深度卷积生成对抗网络(DCGAN)来生成对抗图像

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 DCGAN

实现深度卷积生成对抗网络(DCGAN)来生成对抗图像

图来源网络

main.py

import  os
import  numpy as np
import  tensorflow as tf
from    tensorflow import keras
from    scipy.misc import toimage

from    gen import Generator, Discriminator



def save_result(val_out, val_block_size, image_fn, color_mode):
    def preprocess(img):
        img = ((img + 1.0) * 127.5).astype(np.uint8)
        return img

    preprocesed = preprocess(val_out)
    final_image = np.array([])
    single_row = np.array([])
    for b in range(val_out.shape[0]):
        # concat image into a row
        if single_row.size == 0:
            single_row = preprocesed[b, :, :, :]
        else:
            single_row = np.concatenate((single_row, preprocesed[b, :, :, :]), axis=1)

        # concat image row to final_image
        if (b+1) % val_block_size == 0:
            if final_image.size == 0:
                final_image = single_row
            else:
                final_image = np.concatenate((final_image, single_row), axis=0)

            # reset single row
            single_row = np.array([])

    if final_image.shape[2] == 1:
        final_image = np.squeeze(final_image, axis=2)
    toimage(final_image, mode=color_mode).save(image_fn)


# shorten sigmoid cross entropy loss calculation
def celoss_ones(logits, smooth=0.0):
    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,
                                                                  labels=tf.ones_like(logits)*(1.0 - smooth)))


def celoss_zeros(logits, smooth=0.0):
    return tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,
                                                                  labels=tf.zeros_like(logits)*(1.0 - smooth)))




def d_loss_fn(generator, discriminator, input_noise, real_image, is_trainig):
    fake_image = generator(input_noise, is_trainig)
    d_real_logits = discriminator(real_image, is_trainig)
    d_fake_logits = discriminator(fake_image, is_trainig)

    d_loss_real = celoss_ones(d_real_logits, smooth=0.1)
    d_loss_fake = celoss_zeros(d_fake_logits, smooth=0.0)
    loss = d_loss_real + d_loss_fake
    return loss


def g_loss_fn(generator, discriminator, input_noise, is_trainig):
    fake_image = generator(input_noise, is_trainig)
    d_fake_logits = discriminator(fake_image, is_trainig)
    loss = celoss_ones(d_fake_logits, smooth=0.1)
    return loss





def main():

    tf.random.set_seed(22)
    np.random.seed(22)
    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
    assert tf.__version__.startswith('2.')


    # hyper parameters
    z_dim = 100
    epochs = 3000000
    batch_size = 128
    learning_rate = 0.0002
    is_training = True

    # for validation purpose
    assets_dir = './images'
    if not os.path.isdir(assets_dir):
        os.makedirs(assets_dir)
    val_block_size = 10
    val_size = val_block_size * val_block_size

    # load mnist data
    (x_train, _), (x_test, _) = keras.datasets.mnist.load_data()
    x_train = x_train.astype(np.float32) / 255.
    db = tf.data.Dataset.from_tensor_slices(x_train).shuffle(batch_size*4).batch(batch_size).repeat()
    db_iter = iter(db)
    inputs_shape = [-1, 28, 28, 1]


    # create generator & discriminator
    generator = Generator()
    generator.build(input_shape=(batch_size, z_dim))
    generator.summary()
    discriminator = Discriminator()
    discriminator.build(input_shape=(batch_size, 28, 28, 1))
    discriminator.summary()

    # prepare optimizer
    d_optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)
    g_optimizer = keras.optimizers.Adam(learning_rate=learning_rate, beta_1=0.5)


    for epoch in range(epochs):


        # no need labels
        batch_x = next(db_iter)

        # rescale images to -1 ~ 1
        batch_x = tf.reshape(batch_x, shape=inputs_shape)
        # -1 - 1
        batch_x = batch_x * 2.0 - 1.0

        # Sample random noise for G
        batch_z = tf.random.uniform(shape=[batch_size, z_dim], minval=-1., maxval=1.)


        with tf.GradientTape() as tape:
            d_loss = d_loss_fn(generator, discriminator, batch_z, batch_x, is_training)
        grads = tape.gradient(d_loss, discriminator.trainable_variables)
        d_optimizer.apply_gradients(zip(grads, discriminator.trainable_variables))

        with tf.GradientTape() as tape:
            g_loss = g_loss_fn(generator, discriminator, batch_z, is_training)
        grads = tape.gradient(g_loss, generator.trainable_variables)
        g_optimizer.apply_gradients(zip(grads, generator.trainable_variables))



        if epoch % 100 == 0:

            print(epoch, 'd loss:', float(d_loss), 'g loss:', float(g_loss))

            # validation results at every epoch
            val_z = np.random.uniform(-1, 1, size=(val_size, z_dim))
            fake_image = generator(val_z, training=False)
            image_fn = os.path.join('images', 'gan-val-{:03d}.png'.format(epoch + 1))
            save_result(fake_image.numpy(), val_block_size, image_fn, color_mode='L')




if __name__ == '__main__':
    main()

gen.py

import  tensorflow as tf
from    tensorflow import keras


class Generator(keras.Model):

    def __init__(self):
        super(Generator, self).__init__()

        self.n_f = 512
        self.n_k = 4

        # input z vector is [None, 100]
        self.dense1 = keras.layers.Dense(3 * 3 * self.n_f)
        self.conv2 = keras.layers.Conv2DTranspose(self.n_f // 2, 3, 2, 'valid')
        self.bn2 = keras.layers.BatchNormalization()
        self.conv3 = keras.layers.Conv2DTranspose(self.n_f // 4, self.n_k, 2, 'same')
        self.bn3 = keras.layers.BatchNormalization()
        self.conv4 = keras.layers.Conv2DTranspose(1, self.n_k, 2, 'same')
        return

    def call(self, inputs, training=None):
        # [b, 100] => [b, 3, 3, 512]
        x = tf.nn.leaky_relu(tf.reshape(self.dense1(inputs), shape=[-1, 3, 3, self.n_f]))
        x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training))
        x = tf.nn.leaky_relu(self.bn3(self.conv3(x), training=training))
        x = tf.tanh(self.conv4(x))
        return x


class Discriminator(keras.Model):

    def __init__(self):
        super(Discriminator, self).__init__()

        self.n_f = 64
        self.n_k = 4

        # input image is [-1, 28, 28, 1]
        self.conv1 = keras.layers.Conv2D(self.n_f, self.n_k, 2, 'same')
        self.conv2 = keras.layers.Conv2D(self.n_f * 2, self.n_k, 2, 'same')
        self.bn2 = keras.layers.BatchNormalization()
        self.conv3 = keras.layers.Conv2D(self.n_f * 4, self.n_k, 2, 'same')
        self.bn3 = keras.layers.BatchNormalization()
        self.flatten4 = keras.layers.Flatten()
        self.dense4 = keras.layers.Dense(1)
        return

    def call(self, inputs, training=None):
        x = tf.nn.leaky_relu(self.conv1(inputs))
        x = tf.nn.leaky_relu(self.bn2(self.conv2(x), training=training))
        x = tf.nn.leaky_relu(self.bn3(self.conv3(x), training=training))
        x = self.dense4(self.flatten4(x))
        return x

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