TensorFlow2教程-自编码器

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Tensorflow 2.0 教程持续更新 ：https://blog.csdn.net/qq_31456593/article/details/88606284

完整tensorflow2.0教程代码请看tensorflow2.0：中文教程tensorflow2_tutorials_chinese(欢迎star)

入门教程：
TensorFlow 2.0 教程- Keras 快速入门
TensorFlow 2.0 教程-keras 函数api
TensorFlow 2.0 教程-使用keras训练模型
TensorFlow 2.0 教程-用keras构建自己的网络层
TensorFlow 2.0 教程-keras模型保存和序列化

TensorFlow2教程-自编码器

自动编码器的两个主要组成部分; 编码器和解码器
编码器将输入压缩成一小组“编码”（通常，编码器输出的维数远小于编码器输入）
解码器然后将编码器输出扩展为与编码器输入具有相同维度的输出
换句话说，自动编码器旨在“重建”输入，同时学习数据的有限表示（即“编码”）

1.导入数据

(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()

x_train = x_train.reshape((-1, 28*28)) / 255.0
x_test = x_test.reshape((-1, 28*28)) / 255.0

print(x_train.shape, ' ', y_train.shape)
print(x_test.shape, ' ', y_test.shape)

(60000, 784)   (60000,)
(10000, 784)   (10000,)

2.简单的自编码器

code_dim = 32
inputs = layers.Input(shape=(x_train.shape[1],), name='inputs')
code = layers.Dense(code_dim, activation='relu', name='code')(inputs)
outputs = layers.Dense(x_train.shape[1], activation='softmax', name='outputs')(code)

auto_encoder = keras.Model(inputs, outputs)
auto_encoder.summary()

Model: "model"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
inputs (InputLayer)          [(None, 784)]             0         
_________________________________________________________________
code (Dense)                 (None, 32)                25120     
_________________________________________________________________
outputs (Dense)              (None, 784)               25872     
=================================================================
Total params: 50,992
Trainable params: 50,992
Non-trainable params: 0
_________________________________________________________________

keras.utils.plot_model(auto_encoder, show_shapes=True)

encoder = keras.Model(inputs,code)
keras.utils.plot_model(encoder, show_shapes=True)

decoder_input = keras.Input((code_dim,))
decoder_output = auto_encoder.layers[-1](decoder_input)
decoder = keras.Model(decoder_input, decoder_output)
keras.utils.plot_model(decoder, show_shapes=True)

auto_encoder.compile(optimizer='adam',
                    loss='binary_crossentropy')

训练模型

%%time
history = auto_encoder.fit(x_train, x_train, batch_size=64, epochs=100, validation_split=0.1)

Train on 54000 samples, validate on 6000 samples
Epoch 1/100

Epoch 100/100
54000/54000 [==============================] - 2s 45us/sample - loss: 0.6715 - val_loss: 0.6688
CPU times: user 6min 53s, sys: 23.2 s, total: 7min 16s
Wall time: 4min 24s

encoded = encoder.predict(x_test)
decoded = decoder.predict(encoded)

import matplotlib.pyplot as plt
plt.figure(figsize=(10,4))

<Figure size 1000x400 with 0 Axes>

n = 5
for i in range(n):
    ax = plt.subplot(2, n, i+1)
    plt.imshow(x_test[i].reshape(28,28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
    
    ax = plt.subplot(2, n, n+i+1)
    plt.imshow(decoded[i].reshape(28,28))
    plt.gray()
    ax.get_xaxis().set_visible(False)
    ax.get_yaxis().set_visible(False)
plt.show()