09.variational_autoencoder.py

import numpy as np
import matplotlib.pyplot as plt
from keras.layers import Input, Dense, Lambda
from keras.models import Model
from keras import backend as K
from keras import objectives
from keras.datasets import mnist
batch_size = 100
original_dim = 784
latent_dim = 2
intermediate_dim = 256
nb_epoch = 50
epsilon_std = 1.0
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train/=255
x_test/=255
#np.prob返回数组元素在给定轴上的乘积。reshape将3D转化成2D
x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
def sampling(args):
    z_mean, z_log_var = args
    epsilon = K.random_normal(shape=(batch_size, latent_dim),mean=0.0, stddev=1.0, dtype=None, seed=None)
    return z_mean + K.exp(z_log_var / 2) * epsilon
def vae_loss(x, x_decoded_mean):
    xent_loss = original_dim * objectives.binary_crossentropy(x, x_decoded_mean)
    kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.exp(z_log_var), axis=-1)
    return xent_loss + kl_loss


# 建立模型
#Input用于实例化一个张量,batch_shape相当于多个input_shape
x = Input(batch_shape=(batch_size, original_dim))
h = Dense(intermediate_dim, activation='relu')(x)
z_mean = Dense(latent_dim)(h)
encoder = Model(x, z_mean)#编码器

z_log_var = Dense(latent_dim)(h)
z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])#Lambda是匿名函数
decoder_h = Dense(intermediate_dim, activation='relu')
decoder_mean = Dense(original_dim, activation='sigmoid')
h_decoded = decoder_h(z)
x_decoded_mean = decoder_mean(h_decoded)
#可以用Model直接建模型，也可以逐个model.add()
vae = Model(x, x_decoded_mean)#解码器
vae.compile(optimizer='rmsprop', loss=vae_loss)
vae.fit(x_train, x_train,
        shuffle=True,
        epochs=nb_epoch,
        batch_size=batch_size,
        validation_data=(x_test, x_test))

#  显示一个数字类的二维图。
x_test_encoded = encoder.predict(x_test, batch_size=batch_size)
#figsize是图像的长和宽
plt.figure(figsize=(6, 6))
# 散点图，前两个参数是数据，c参数是散点颜色
plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test)
#颜色渐变条
plt.colorbar()
plt.show()

#   构建一个可以从学习分布中采样的数字生成器
decoder_input = Input(shape=(latent_dim,))
_h_decoded = decoder_h(decoder_input)
_x_decoded_mean = decoder_mean(_h_decoded)
generator = Model(decoder_input, _x_decoded_mean)

#   显示数字的二维流形
n = 15  # 用15x15数字图
digit_size = 28
figure = np.zeros((digit_size * n, digit_size * n))
# 我们将在[-15, 15 ]标准偏差( 等距离)中采样n个点。
grid_x = np.linspace(-15, 15, n)
grid_y = np.linspace(-15, 15, n)
#enumerate用于枚举
for i, yi in enumerate(grid_x):
    for j, xi in enumerate(grid_y):
        z_sample = np.array([[xi, yi]])
        x_decoded = generator.predict(z_sample)
        digit = x_decoded[0].reshape(digit_size, digit_size)
        figure[i * digit_size: (i + 1) * digit_size,
               j * digit_size: (j + 1) * digit_size] = digit

plt.figure(figsize=(10, 10))
plt.imshow(figure)
plt.show()