#! -*- coding: utf-8 -*-

'''用Keras实现的CVAE

目前只保证支持Tensorflow后端

#来自

https://github.com/keras-team/keras/blob/master/examples/variational_autoencoder.py

'''

from __future__ import print_function

import numpy as np

import matplotlib.pyplot as plt

from scipy.stats import norm

from keras.layers import Input, Dense, Lambda

from keras.models import Model

from keras import backend as K

from keras import metrics

from keras.datasets import mnist

from keras.utils import to_categorical

batch_size = 100

original_dim = 784 # 28*28的配比，代表了图片的像素点

latent_dim = 2 # 隐变量取2维只是为了方便后面画图

intermediate_dim = 256 # 是什么？

epochs = 100

epsilon_std = 1.0

num_classes = 10

# 加载MNIST数据集

(x_train, y_train_), (x_test, y_test_) = mnist.load_data()

x_train = x_train.astype('float32') / 255. # 做RGB的转化

x_test = x_test.astype('float32') / 255.

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:])))

y_train = to_categorical(y_train_, num_classes)

y_test = to_categorical(y_test_, num_classes)

x = Input(shape=(original_dim,))

h = Dense(intermediate_dim, activation='relu')(x) # 第一个隐藏层，以x为输入，降维到intermediate_dim维

# 算p(Z|X)的均值和方差

z_mean = Dense(latent_dim)(h) # 第二个隐藏层，降维到latent_dim维，计算分布均值

z_log_var = Dense(latent_dim)(h) # 并行的网路怎么组合？？

y = Input(shape=(num_classes,)) # 输入类别

yh = Dense(latent_dim)(y) # 这里就是直接构建每个类别的均值

# 重参数技巧

def sampling(args):

return z_mean + K.exp(z_log_var / 2) * epsilon

# 重参数层，相当于给输入加入噪声

z = Lambda(sampling, output_shape=(latent_dim,))([z_mean, z_log_var])

# 解码层，也就是生成器部分

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) # 估计的是每一个变量维度输出的均值

# 建立模型

vae = Model([x, y], [x_decoded_mean, yh])

# xent_loss是重构loss，kl_loss是KL loss

xent_loss = original_dim * metrics.binary_crossentropy(x, x_decoded_mean)

# 只需要修改K.square(z_mean)为K.square(z_mean - yh)，也就是让隐变量向类内均值看齐

kl_loss = - 0.5 * K.sum(1 + z_log_var - K.square(z_mean - yh) - K.exp(z_log_var), axis=-1)

vae_loss = K.mean(xent_loss + kl_loss)

# add_loss是新增的方法，用于更灵活地添加各种loss

vae.add_loss(vae_loss)

vae.compile(optimizer='rmsprop')

vae.summary()

vae.fit([x_train, y_train],

shuffle=True,

epochs=epochs,

batch_size=batch_size,

validation_data=([x_test, y_test], None))

# 构建encoder，然后观察各个数字在隐空间的分布

encoder = Model(x, z_mean)

x_test_encoded = encoder.predict(x_test, batch_size=batch_size)

plt.figure(figsize=(6, 6))

plt.scatter(x_test_encoded[:, 0], x_test_encoded[:, 1], c=y_test_)

plt.colorbar()

plt.show()

# 构建生成器

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)

# 输出每个类的均值向量

mu = Model(y, yh)

mu = mu.predict(np.eye(num_classes))

# 观察能否通过控制隐变量的均值来输出特定类别的数字

n = 15 # figure with 15x15 digits

digit_size = 28

figure = np.zeros((digit_size * n, digit_size * n))

output_digit = 9 # 指定输出数字

# 用正态分布的分位数来构建隐变量对

grid_x = norm.ppf(np.linspace(0.05, 0.95, n)) + mu[output_digit][1]

grid_y = norm.ppf(np.linspace(0.05, 0.95, n)) + mu[output_digit][0]

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, cmap='Greys_r')

plt.show()