Tensorflow 变分自编码器：Fashion MNIST图片的重建与生成

输入为Fashion MNIST图片向量，经过三个全连接层后得到隐向量z的均值与方差，分别用2个输出节点数为20的全连接层表示，fc2的20个输出节点表示20个特征分布的均值向量μ，fc3的20个输出节点表示20个特征分布的方差向量的log值。通过Reparameterization trick采样获得长度为20的隐向量z，并通过fc4/fc5重建出样本图片。

VAE作为生成模型，除了可以重建输入样本，还可以单独使用解码器生成样本。通过从先验分布p(z)中直接采样获取隐向量z，经过解码后可以产生生成的样本。

代码

import tensorflow as tf 
from tensorflow import keras
import numpy as np
from    matplotlib import pyplot as plt
from    PIL import Image


(x_train, y_train), (x_test, y_test) = keras.datasets.fashion_mnist.load_data()
x_train = tf.convert_to_tensor(x_train/255., tf.float32)
x_test = tf.convert_to_tensor(x_test/255., tf.float32) 

batchsz = 100
train_db = tf.data.Dataset.from_tensor_slices(x_train)
test_db = tf.data.Dataset.from_tensor_slices(x_test) 

train_db = train_db.shuffle(batchsz*5).batch(batchsz).repeat(10)
test_db = test_db.batch(batchsz)


class VAE(keras.Model):
    # 变分自编码器
    def __init__(self):
        super(VAE, self).__init__()
        # Encoder网络
        self.fc1 = keras.layers.Dense(128)
        self.fc2 = keras.layers.Dense(20)
        self.fc3 = keras.layers.Dense(20)
        # Decoder网络
        self.fc4 = keras.layers.Dense(128)
        self.fc5 = keras.layers.Dense(784)
    
    def encoder(self, x):
        h = tf.nn.relu(self.fc1(x))
        mu = self.fc2(h)
        log_var = self.fc3(h)
        return mu, log_var

    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 decoder(self, z):
        out = tf.nn.relu(self.fc4(z))
        out = self.fc5(out)
        return out

    def call(self, inputs, training=None):
        mu, log_var = self.encoder(inputs)
        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))
model.summary()


optimizer = keras.optimizers.Adam(learning_rate=1e-3) 
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] 

        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(step, 'kl div: ', float(kl_div), 'loss: ', float(loss))

def save_images(imgs, name):
    new_im = Image.new('L', (280, 280))
    index = 0
    for i in range(0, 280, 28): # 10 行图片阵列
        for j in range(0, 280, 28): # 10 列图片阵列
            im = imgs[index]
            im = Image.fromarray(im, mode='L')
            new_im.paste(im, (i, j)) # 写入对应位置
            index += 1

    # 保存图片阵列
    new_im.save(name) 

z = tf.random.normal((100, 20))
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, 'vaebuild.png')

x = next(iter(test_db))
logits, _, _ = model(tf.reshape(x, [-1,784]))
x_hat = tf.sigmoid(logits)
x_hat = tf.reshape(x_hat, [-1,28,28]) 

x_concat = tf.concat([x[:50], x_hat[:50]], axis=0)
x_concat = x_concat.numpy() * 255. 
x_concat = x_concat.astype(np.uint8)
save_images(x_concat,'10_vae.png')