循环自动编码器

如果要为序列构建自动编码器，例如时间序列和文本（例如，用于无监督学习或降维），那么递归神经元可能比密集网络更合适。构建循环自动编码器非常简单直接：编码器通常是序列到向量的RNN，它将输入序列压缩为单个向量。解码器是向量到序列RNN，做相反的处理：

from tensorflow import keras

fashion_mnist = keras.datasets.fashion_mnist
(X_train_all, y_train_all), (X_test, y_test) = fashion_mnist.load_data()
X_valid, X_train = X_train_all[:5000] / 255., X_train_all[5000:] / 255.
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]
recurrent_encoder = keras.models.Sequential([
    keras.layers.LSTM(100, return_sequences=True, input_shape=[None, 28]),
    keras.layers.LSTM(30)
])
recurrent_decoder = keras.models.Sequential([
    keras.layers.RepeatVector(28, input_shape=[30]),
    keras.layers.LSTM(100, return_sequences=True),
    keras.layers.TimeDistributed(keras.layers.Dense(28, activation='sigmoid'))
])
recurrent_ae = keras.models.Sequential([recurrent_encoder, recurrent_decoder])
recurrent_ae.compile(loss='binary_crossentropy', optimizer=keras.optimizers.Adam())
history = recurrent_ae.fit(X_train, X_train, epochs=10, validation_data=(X_valid, X_valid), batch_size=32)

Epoch 1/10
1719/1719 [==============================] - 23s 11ms/step - loss: 0.3433 - val_loss: 0.3127
Epoch 2/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.3047 - val_loss: 0.2962
Epoch 3/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.2943 - val_loss: 0.2896
Epoch 4/10
1719/1719 [==============================] - 18s 11ms/step - loss: 0.2881 - val_loss: 0.2832
Epoch 5/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.2841 - val_loss: 0.2795
Epoch 6/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.2812 - val_loss: 0.2769
Epoch 7/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.2790 - val_loss: 0.2747
Epoch 8/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.2773 - val_loss: 0.2733
Epoch 9/10
1719/1719 [==============================] - 19s 11ms/step - loss: 0.2757 - val_loss: 0.2723
Epoch 10/10
1719/1719 [==============================] - 18s 10ms/step - loss: 0.2745 - val_loss: 0.2711

这种循环自动编码器可以处理任何长度的序列，每个时间步长都具有28个维度，这意味着它可以通过把每个图像视为一系列的行来处理Fashion MNIST图像：在每个时间步长，RNN都将处理一个28像素的行，显然可以对任何类型的序列使用循环自动编码器。在这里使用RepeatVector层作为编码器的第一层，以确保其输入向量在每个时间步长都馈送到解码器

可视化重构

import matplotlib.pyplot as plt


def plot_image(image):
    plt.imshow(image, cmap='binary')
    plt.axis('off')


def show_reconstructions(model, n_images=5):
    reconstructions = model.predict(X_valid[:n_images])
    fig = plt.figure(figsize=(n_images * 1.5, 3))
    for image_index in range(n_images):
        plt.subplot(2, n_images, 1 + image_index)
        plot_image(X_valid[image_index])
        plt.subplot(2, n_images, 1 + n_images + image_index)
        plot_image(reconstructions[image_index])


show_reconstructions(recurrent_ae)

为了强制自动编码器学习有趣的特征，我们限制了编码层的大小，使其成为不完整自动编码器，实际上，还有许多其它类型的约束可以使用，包括使编码层与输入层一样大甚至更大，从而成为一个完整自动编码器

机器学习实战(深度学习部分)

循环自动编码器

循环自动编码器

可视化重构

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