Deep-Learning-with-TensorFlow-book/ch12-自编码器/vae.py

import  os
import  tensorflow as tf
import  numpy as np
from    tensorflow import keras
from    tensorflow.keras import Sequential, layers
from    PIL import Image
from    matplotlib import pyplot as plt



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


def save_images(imgs, name):
    new_im = Image.new('L', (280, 280))

    index = 0
    for i in range(0, 280, 28):
        for j in range(0, 280, 28):
            im = imgs[index]
            im = Image.fromarray(im, mode='L')
            new_im.paste(im, (i, j))
            index += 1

    new_im.save(name)


h_dim = 20
batchsz = 512
lr = 1e-3


(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
x_train, x_test = x_train.astype(np.float32) / 255., x_test.astype(np.float32) / 255.
# we do not need label
train_db = tf.data.Dataset.from_tensor_slices(x_train)
train_db = train_db.shuffle(batchsz * 5).batch(batchsz)
test_db = tf.data.Dataset.from_tensor_slices(x_test)
test_db = test_db.batch(batchsz)

print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)

z_dim = 10

class VAE(keras.Model):

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

        # Encoder
        self.fc1 = layers.Dense(128)
        self.fc2 = layers.Dense(z_dim) # get mean prediction
        self.fc3 = layers.Dense(z_dim)

        # Decoder
        self.fc4 = layers.Dense(128)
        self.fc5 = layers.Dense(784)

    def encoder(self, x):

        h = tf.nn.relu(self.fc1(x))
        # get mean
        mu = self.fc2(h)
        # get variance
        log_var = self.fc3(h)

        return mu, log_var

    def decoder(self, z):

        out = tf.nn.relu(self.fc4(z))
        out = self.fc5(out)

        return out

    def reparameterize(self, mu, log_var):

        eps = tf.random.normal(log_var.shape)

        std = tf.exp(log_var*0.5)

        z = mu + std * eps
        return z

    def call(self, inputs, training=None):

        # [b, 784] => [b, z_dim], [b, z_dim]
        mu, log_var = self.encoder(inputs)
        # reparameterization trick
        z = self.reparameterize(mu, log_var)

        x_hat = self.decoder(z)

        return x_hat, mu, log_var


model = VAE()
model.build(input_shape=(4, 784))
optimizer = tf.optimizers.Adam(lr)

for epoch in range(1000):

    for step, x in enumerate(train_db):

        x = tf.reshape(x, [-1, 784])

        with tf.GradientTape() as tape:
            x_rec_logits, mu, log_var = model(x)

            rec_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=x, logits=x_rec_logits)
            rec_loss = tf.reduce_sum(rec_loss) / x.shape[0]

            # compute kl divergence (mu, var) ~ N (0, 1)
            # https://stats.stackexchange.com/questions/7440/kl-divergence-between-two-univariate-gaussians
            kl_div = -0.5 * (log_var + 1 - mu**2 - tf.exp(log_var))
            kl_div = tf.reduce_sum(kl_div) / x.shape[0]

            loss = rec_loss + 1. * kl_div

        grads = tape.gradient(loss, model.trainable_variables)
        optimizer.apply_gradients(zip(grads, model.trainable_variables))


        if step % 100 == 0:
            print(epoch, step, 'kl div:', float(kl_div), 'rec loss:', float(rec_loss))


    # evaluation
    z = tf.random.normal((batchsz, z_dim))
    logits = model.decoder(z)
    x_hat = tf.sigmoid(logits)
    x_hat = tf.reshape(x_hat, [-1, 28, 28]).numpy() *255.
    x_hat = x_hat.astype(np.uint8)
    save_images(x_hat, 'vae_images/sampled_epoch%d.png'%epoch)

    x = next(iter(test_db))
    x = tf.reshape(x, [-1, 784])
    x_hat_logits, _, _ = model(x)
    x_hat = tf.sigmoid(x_hat_logits)
    x_hat = tf.reshape(x_hat, [-1, 28, 28]).numpy() *255.
    x_hat = x_hat.astype(np.uint8)
    save_images(x_hat, 'vae_images/rec_epoch%d.png'%epoch)