代码
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
gpu = tf.config.experimental.list_physical_devices(device_type='GPU')
tf.config.experimental.set_memory_growth(gpu[0], True)
import tensorflow.keras.datasets.mnist as mnist
(train_image, train_label), (_, _) = mnist.load_data()
train_image = train_image / 127.5 - 1
train_image = np.expand_dims(train_image, -1)
dataset = tf.data.Dataset.from_tensor_slices((train_image, train_label))
BATCH_SIZE = 256
image_count = train_image.shape[0]
noise_dim = 30
con_dim = 30
dataset = dataset.shuffle(image_count).batch(BATCH_SIZE)
def generator_model():
noise_seed = layers.Input(shape=((noise_dim,)))
con_seed = layers.Input(shape=((con_dim,)))
label = layers.Input(shape=(()))
x = layers.Embedding(10, 30, input_length=1)(label)
x = layers.Flatten()(x)
x = layers.concatenate([noise_seed, con_seed, x])
x = layers.Dense(3*3*128, use_bias=False)(x)
x = layers.Reshape((3, 3, 128))(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x)
x = layers.Conv2DTranspose(64, (3, 3), strides=(2, 2), use_bias=False)(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x) # 7*7
x = layers.Conv2DTranspose(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)
x = layers.BatchNormalization()(x)
x = layers.ReLU()(x) # 14*14
x = layers.Conv2DTranspose(1, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)
x = layers.Activation('tanh')(x)
model = tf.keras.Model(inputs=[noise_seed, con_seed, label], outputs=x)
return model
def discriminator_model():
image = tf.keras.Input(shape=((28,28,1)))
x = layers.Conv2D(32, (3, 3), strides=(2, 2), padding='same', use_bias=False)(image)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
x = layers.Dropout(0.5)(x)
x = layers.Conv2D(32*2, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
x = layers.Dropout(0.5)(x)
x = layers.Conv2D(32*4, (3, 3), strides=(2, 2), padding='same', use_bias=False)(x)
x = layers.BatchNormalization()(x)
x = layers.LeakyReLU()(x)
x = layers.Dropout(0.5)(x)
x = layers.Flatten()(x)
x1 = layers.Dense(1)(x)
x2 = layers.Dense(10)(x)
x3 = layers.Dense(con_dim, activation='sigmoid')(x)
model = tf.keras.Model(inputs=image, outputs=[x1, x2, x3])
return model
generator = generator_model()
discriminator = discriminator_model()
binary_cross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)
category_cross_entropy = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
def discriminator_loss(real_output, real_cat_out, fake_output, label, con_out, cond_in):
real_loss = binary_cross_entropy(tf.ones_like(real_output), real_output)
fake_loss = binary_cross_entropy(tf.zeros_like(fake_output), fake_output)
cat_loss = category_cross_entropy(label, real_cat_out)
con_loss = tf.reduce_mean(tf.square(con_out - cond_in))
total_loss = real_loss + fake_loss + cat_loss + con_loss
return total_loss
def generator_loss(fake_output, fake_cat_out, label, con_out, cond_in):
fake_loss = binary_cross_entropy(tf.ones_like(fake_output), fake_output)
cat_loss = category_cross_entropy(label, fake_cat_out)
con_loss = tf.reduce_mean(tf.square(con_out - cond_in))
return fake_loss + cat_loss + con_loss
generator_optimizer = tf.keras.optimizers.Adam(1e-5)
discriminator_optimizer = tf.keras.optimizers.Adam(1e-5)
@tf.function
def train_step(images, labels):
batchsize = labels.shape[0]
noise = tf.random.normal([batchsize, noise_dim])
cond = tf.random.uniform([batchsize, noise_dim])
with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
generated_images = generator((noise, cond, labels), training=True)
real_output, real_cat_out, _ = discriminator(images, training=True)
fake_output, fake_cat_out, con_out = discriminator(generated_images, training=True)
gen_loss = generator_loss(fake_output, fake_cat_out, labels, con_out, cond)
disc_loss = discriminator_loss(real_output, real_cat_out, fake_output, labels,
con_out, cond)
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))
num = 10
noise_seed = tf.random.normal([num, noise_dim])
cat_seed = np.random.randint(0, 10, size=(num, 1))
print(cat_seed.T)
def generate_and_save_images(model, test_noise_input, test_cat_input, epoch):
print('Epoch:', epoch+1)
# Notice `training` is set to False.
# This is so all layers run in inference mode (batchnorm).
cond_seed = tf.random.uniform([num, con_dim])
predictions = model((test_noise_input, cond_seed, test_cat_input), training=False)
predictions = tf.squeeze(predictions)
fig = plt.figure(figsize=(10, 1))
for i in range(predictions.shape[0]):
plt.subplot(1, 10, i+1)
plt.imshow((predictions[i, :, :] + 1)/2, cmap='gray')
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, label_batch in dataset:
train_step(image_batch, label_batch)
if epoch%10 == 0:
generate_and_save_images(generator,
noise_seed,
cat_seed,
epoch)
generate_and_save_images(generator,
noise_seed,
cat_seed,
epoch)
EPOCHS = 200
train(dataset, EPOCHS)
generator.save('generate_infogan.h5')
num = 10
noise_seed = tf.random.normal([num, noise_dim])
cat_seed = np.arange(10).reshape(-1, 1)
print(cat_seed.T)
