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 toimagefrom gen import Generator, Discriminatordef save_result(val_out, val_block_size, image_fn, color_mode):def preprocess(img):img = ((img + 1.0) * 127.5).astype(np.uint8)return imgpreprocesed = preprocess(val_out)final_image = np.array([])single_row = np.array([])for b in range(val_out.shape[0]):# concat image into a rowif single_row.size == 0:single_row = preprocesed[b, :, :, :]else:single_row = np.concatenate((single_row, preprocesed[b, :, :, :]), axis=1)# concat image row to final_imageif (b+1) % val_block_size == 0:if final_image.size == 0:final_image = single_rowelse:final_image = np.concatenate((final_image, single_row), axis=0)# reset single rowsingle_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_fakereturn lossdef 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 lossdef main():tf.random.set_seed(22)np.random.seed(22)os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'assert tf.__version__.startswith('2.')# hyper parametersz_dim = 100epochs = 3000000batch_size = 128learning_rate = 0.0002is_training = True# for validation purposeassets_dir = './images'if not os.path.isdir(assets_dir):os.makedirs(assets_dir)val_block_size = 10val_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 & discriminatorgenerator = 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 optimizerd_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 labelsbatch_x = next(db_iter)# rescale images to -1 ~ 1batch_x = tf.reshape(batch_x, shape=inputs_shape)# -1 - 1batch_x = batch_x * 2.0 - 1.0# Sample random noise for Gbatch_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 epochval_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 kerasclass Generator(keras.Model):def __init__(self):super(Generator, self).__init__()self.n_f = 512self.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')returndef 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 xclass Discriminator(keras.Model):def __init__(self):super(Discriminator, self).__init__()self.n_f = 64self.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)returndef 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