#导入相关的包

import numpy as np

np.random.seed(1337)  # for reproducibility

from keras.datasets import mnist

from keras.models import Model #采用通用模型

from keras.layers import Dense, Input #只用到全连接层

import matplotlib.pyplot as plt

#读取数据

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

x_train = X_train.astype('float32') / 255. - 0.5       # minmax_normalized

x_test = X_test.astype('float32') / 255. - 0.5         # minmax_normalized

x_train = X_train.reshape((x_train.shape[0], -1))

x_test = X_test.reshape((x_test.shape[0], -1))

print(x_train.shape) #(60000, 784)

print(x_test.shape) #(10000, 784)

print(X_train.shape)  # (60000, 28, 28)

# in order to plot in a 2D figure

encoding_dim = 2

# this is our input placeholder

input_img = Input(shape=(784,))

# encoder layers编码层

encoded = Dense(128, activation='relu')(input_img)

encoded = Dense(64, activation='relu')(encoded)

encoded = Dense(10, activation='relu')(encoded)

encoder_output = Dense(encoding_dim)(encoded)

# decoder layers解码层

decoded = Dense(10, activation='relu')(encoder_output)

decoded = Dense(64, activation='relu')(decoded)

decoded = Dense(128, activation='relu')(decoded)

decoded = Dense(784, activation='tanh')(decoded)

#构建模型

#包括编码层也包括解码层

autoencoder = Model(input = input_img,output = decoded)

#只包括编码层

encoder = Model(input = input_img,output = encoder_output)

#编译模型
autoencoder.compile(optimizer='adam', loss='mse')

autoencoder.fit(x_train, x_train,

                epochs=20,

                batch_size=256,

                shuffle=True)

encoded_imgs = encoder.predict(x_test)

plt.scatter(encoded_imgs[:, 0], encoded_imgs[:, 1], c=y_test) #c表示颜色维度

plt.colorbar()

plt.show()