import numpy as np

import tensorflow as tf

class AutoEncoder(object):

    '''

    使用对称结构，解码器重用编码器的权重参数

    '''

    def __init__(self, input_shape, h1_size, lr):

        tf.reset_default_graph()# 重置默认计算图，有时出错后内存还一团糟

        with tf.variable_scope('auto_encoder', reuse=tf.AUTO_REUSE):

            self.W1 = self.weights(shape=(input_shape, h1_size), name='h1')

            self.b1 = self.bias(h1_size)

            self.W2 = tf.transpose(tf.get_variable('h1'))  # 共享参数，使用其转置

            self.b2 = self.bias(input_shape)

        self.lr = lr

        self.input = tf.placeholder(shape=(None, input_shape),

                                    dtype=tf.float32)

        self.h1_out = tf.nn.softplus(tf.matmul(self.input, self.W1) + self.b1)# softplus，类relu

        self.out = tf.matmul(self.h1_out, self.W2) + self.b2

        self.optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)

        self.loss = 0.1 * tf.reduce_sum(

            tf.pow(tf.subtract(self.input, self.out), 2))

        self.train_op = self.optimizer.minimize(self.loss)

        self.sess = tf.Session()

        self.sess.run(tf.global_variables_initializer())

    def fit(self, X, epoches=100, batch_size=128, epoches_to_display=10):

        batchs_per_epoch = X.shape[0] // batch_size

        for i in range(epoches):

            epoch_loss = []

            for j in range(batchs_per_epoch):

                X_train = X[j * batch_size:(j + 1) * batch_size]

                loss, _ = self.sess.run([self.loss, self.train_op],

                                        feed_dict={self.input: X_train})

                epoch_loss.append(loss)

            if i % epoches_to_display == 0:

                print('avg_loss at epoch %d :%f' % (i, np.mean(epoch_loss)))

#         return self.sess.run(W1)

#     权重初始化参考别人的，这个居然很重要！用自己设定的截断正态分布随机没有效果

    def weights(self, shape, name, constant=1):

        fan_in = shape[0]

        fan_out = shape[1]

        low = -constant * np.sqrt(6.0 / (fan_in + fan_out))

        high = constant * np.sqrt(6.0 / (fan_in + fan_out))

        init = tf.random_uniform_initializer(minval=low, maxval=high)

        return tf.get_variable(name=name,

                               shape=shape,

                               initializer=init,

                               dtype=tf.float32)

    def bias(self, size):

        return tf.Variable(tf.constant(0, dtype=tf.float32, shape=[size]))

    def encode(self, X):

        return self.sess.run(self.h1_out, feed_dict={self.input: X})

    def decode(self, h):

        return self.sess.run(self.out, feed_dict={self.h1_out: h})

    def reconstruct(self, X):

        return self.sess.run(self.out, feed_dict={self.input: X})

from keras.datasets import mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()

import random

X_train = X_train.reshape(-1, 784)

# 测试集里随机10个图片用做测试

test_idxs = random.sample(range(X_test.shape[0]), 10)

data_test = X_test[test_idxs].reshape(-1, 784)

# 标准化

import sklearn.preprocessing as prep

processer = prep.StandardScaler().fit(X_train) # 这里还是用全部数据好，这个也很关键！

X_train = processer.transform(X_train)

X_test = processer.transform(data_test)

# 随机5000张图片用做训练

idxs = random.sample(range(X_train.shape[0]), 5000)

data_train = X_train[idxs]

model = AutoEncoder(784, 200, 0.001)  # 学习率对loss影响也有点大

model.fit(data_train, batch_size=128, epoches=200)  # 200轮即可

decoded_test = model.reconstruct(X_test)

import matplotlib.pyplot as plt

%matplotlib inline

shape = (28, 28)

fig, axes = plt.subplots(2,10,

                         figsize=(10, 2),

                         subplot_kw={

                             'xticks': [],

                             'yticks': []

                         },

                         gridspec_kw=dict(hspace=0.1, wspace=0.1))

for i in range(10):

    axes[0][i].imshow(np.reshape(X_test[i], shape))

    axes[1][i].imshow(np.reshape(decoded_test[i], shape))

plt.show()