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train_cifar10.py
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import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input,Concatenate,Flatten,Reshape,Conv2D,Conv2DTranspose,LeakyReLU,Dense
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.datasets import cifar10
import numpy as np
from PIL import Image
def build_generator(image_size, latent_code_length):
x = Input(latent_code_length)
y = Conv2DTranspose(512, (3, 3), strides=(2, 2), padding="same")(x)
y = LeakyReLU()(y)
y = Conv2D(512, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2DTranspose(256, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(256, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2DTranspose(128, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(128, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2DTranspose(image_size[-1],(3,3),strides=(2,2),padding="same")(y)
return Model(x, y)
def build_encoder(image_size, latent_code_length):
x = Input(image_size)
y = Conv2D(128, (3, 3), strides=(2, 2), padding="same")(x)
y = LeakyReLU()(y)
y = Conv2D(128, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(256, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(256, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(512, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(512, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(latent_code_length[-1],(3,3),strides=(2,2),padding="same")(y)
return Model(x, y)
def build_discriminator(image_size, latent_code_length):
x = Input(image_size)
z = Input(latent_code_length)
_z = Flatten()(z)
_z = Dense(image_size[0]*image_size[1]*image_size[2])(_z)
_z = Reshape(image_size)(_z)
y = Concatenate()([x,_z])
y = Conv2D(128, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(128, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(256, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(256, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(512, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(512, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(1024, (3, 3), strides=(2, 2), padding="same")(y)
y = LeakyReLU()(y)
y = Conv2D(1024, (3, 3), padding="same")(y)
y = LeakyReLU()(y)
y = Flatten()(y)
y = Dense(1)(y)
return Model([x, z], [y])
def build_train_step(generator, encoder, discriminator):
g_optimizer = Adam(lr=0.0001, beta_1=0.0, beta_2=0.9)
e_optimizer = Adam(lr=0.0001, beta_1=0.0, beta_2=0.9)
d_optimizer = Adam(lr=0.0001, beta_1=0.0, beta_2=0.9)
@tf.function
def train_step(real_image, real_code):
tf.keras.backend.set_learning_phase(True)
fake_image = generator(real_code)
fake_code = encoder(real_image)
d_inputs = [tf.concat([fake_image, real_image], axis=0),
tf.concat([real_code, fake_code], axis=0)]
d_preds = discriminator(d_inputs)
pred_g, pred_e = tf.split(d_preds,num_or_size_splits=2, axis=0)
d_loss = tf.reduce_mean(tf.nn.softplus(pred_g)) + \
tf.reduce_mean(tf.nn.softplus(-pred_e))
g_loss = tf.reduce_mean(tf.nn.softplus(-pred_g))
e_loss = tf.reduce_mean(tf.nn.softplus(pred_e))
d_gradients = tf.gradients(d_loss, discriminator.trainable_variables)
g_gradients = tf.gradients(g_loss, generator.trainable_variables)
e_gradients = tf.gradients(e_loss, encoder.trainable_variables)
d_optimizer.apply_gradients(zip(d_gradients, discriminator.trainable_variables))
g_optimizer.apply_gradients(zip(g_gradients, generator.trainable_variables))
e_optimizer.apply_gradients(zip(e_gradients, encoder.trainable_variables))
return d_loss, g_loss, e_loss
return train_step
def train():
check_point = 1000
iters = 200 * check_point
image_size = (32,32,3)
latent_code_length = (2,2,32)
batch_size = 16
(x_train, y_train), (x_test, y_test) = cifar10.load_data()
num_of_data = x_train.shape[0]
x_train = np.reshape(x_train, (-1, )+image_size)
x_train = (x_train.astype("float32") / 255) * 2 - 1
z_train = np.random.uniform(-1.0, 1.0, (num_of_data, )+latent_code_length).astype("float32")
z_test = np.random.uniform(-1.0, 1.0, (100, )+latent_code_length).astype("float32")
# ==================== save x images ====================
image = np.reshape(x_train[:100], (10, 10, 32, 32, 3))
image = np.transpose(image, (0, 2, 1, 3, 4))
image = np.reshape(image, (10 * 32, 10 * 32, 3))
image = 255 * (image + 1) / 2
image = np.clip(image, 0, 255)
image = image.astype("uint8")
Image.fromarray(image, "RGB").save("x.png")
# =======================================================
generator = build_generator(image_size, latent_code_length)
encoder = build_encoder(image_size, latent_code_length)
discriminator = build_discriminator(image_size, latent_code_length)
train_step = build_train_step(generator, encoder, discriminator)
for i in range(iters):
real_images = x_train[np.random.permutation(num_of_data)[:batch_size]]
real_code = z_train[np.random.permutation(num_of_data)[:batch_size]]
d_loss, g_loss, e_loss = train_step(real_images, real_code)
print("\r[{}/{}] d_loss: {:.4}, g_loss: {:.4}, e_loss: {:.4}".format(i,iters, d_loss, g_loss, e_loss),end="")
if (i+1)%check_point == 0:
# save G(x) images
image = generator.predict(encoder.predict(x_train[:100]))
image = np.reshape(image, (10, 10, 32, 32, 3))
image = np.transpose(image, (0, 2, 1, 3, 4))
image = np.reshape(image, (10 * 32, 10 * 32, 3))
image = 255 * (image + 1) / 2
image = np.clip(image,0,255)
image = image.astype("uint8")
Image.fromarray(image, "RGB").save("G_E_x-{}.png".format(i//check_point))
# save G(z) images
image = generator.predict(z_test)
image = np.reshape(image, (10, 10, 32, 32, 3))
image = np.transpose(image, (0, 2, 1, 3, 4))
image = np.reshape(image, (10 * 32, 10 * 32, 3))
image = 255 * (image + 1) / 2
image = np.clip(image,0,255)
image = image.astype("uint8")
Image.fromarray(image, "RGB").save("G_z-{}.png".format(i//check_point))
if __name__ == "__main__":
train()