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无法在Keras中训练简单的自动编码器

autoencoder signal-processing keras machine-learning python

1

crash · 技术社区 · 8 年前

我正试着在凯拉斯训练一个自动编码器进行信号处理,但不知怎么的,我失败了。

(22836, 128, 6) 其中22836是样本量。

这是我用于自动编码器的示例代码:

X_train, X_test, Y_train, Y_test = load_dataset()

# reshape the input, whose size is (22836, 128, 6)
X_train = X_train.reshape(X_train.shape[0], np.prod(X_train.shape[1:]))
X_test = X_test.reshape(X_test.shape[0], np.prod(X_test.shape[1:]))
# now the shape will be (22836, 768)

### MODEL ###
input_shape = [X_train.shape[1]]
X_input = Input(input_shape)

x = Dense(1000, activation='sigmoid', name='enc0')(X_input)
encoded = Dense(350, activation='sigmoid', name='enc1')(x)
x = Dense(1000, activation='sigmoid', name='dec0')(encoded)
decoded = Dense(input_shape[0], activation='sigmoid', name='dec1')(x)

model = Model(inputs=X_input, outputs=decoded, name='autoencoder')

model.compile(optimizer='rmsprop', loss='mean_squared_error')
print(model.summary())

的输出 model.summary()

Model summary
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_55 (InputLayer)        (None, 768)               0         
_________________________________________________________________
enc0 (Dense)                 (None, 1000)              769000    
_________________________________________________________________
enc1 (Dense)                 (None, 350)               350350    
_________________________________________________________________
dec1 (Dense)                 (None, 1000)              351000    
_________________________________________________________________
dec0 (Dense)                 (None, 768)               768768    
=================================================================
Total params: 2,239,118
Trainable params: 2,239,118
Non-trainable params: 0

培训是通过

# train the model
history = model.fit(x = X_train, y = X_train,
                    epochs=5,
                    batch_size=32,
                    validation_data=(X_test, X_test))

在这里,我只想学习身份函数,它产生:

Train on 22836 samples, validate on 5709 samples
Epoch 1/5
22836/22836 [==============================] - 27s 1ms/step - loss: 0.9481 - val_loss: 0.8862
Epoch 2/5
22836/22836 [==============================] - 24s 1ms/step - loss: 0.8669 - val_loss: 0.8358
Epoch 3/5
22836/22836 [==============================] - 25s 1ms/step - loss: 0.8337 - val_loss: 0.8146
Epoch 4/5
22836/22836 [==============================] - 25s 1ms/step - loss: 0.8164 - val_loss: 0.7960
Epoch 5/5
22836/22836 [==============================] - 25s 1ms/step - loss: 0.8004 - val_loss: 0.7819

prediction = model.predict(X_test)
for i in np.random.randint(0, 100, 7):
    pred = prediction[i, :].reshape(128,6)
    # getting only values for acceleration_x
    pred = pred[:, 0]
    true = X_test[i, :].reshape(128,6)
    # getting only values for acceleration_x
    true = true[:, 0]
    # plot original and reconstructed
    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(20, 6))
    ax1.plot(true, color='green')
    ax2.plot(pred, color='red')

这些情节似乎完全错误:

除了少数几个时期(实际上似乎没有什么区别)之外,你有什么建议吗?

1 回复 | 直到 8 年前

1

3

today 8 年前

您的数据不在[0,1]范围内,为什么要使用 sigmoid 作为最后一层的激活函数?从最后一层删除激活函数(最好使用 relu

同时规范化训练数据。可以使用按功能划分的规格化:

X_mean = X_train.mean(axis=0)
X_train -= X_mean
X_std = X_train.std(axis=0)
X_train /= X_std + 1e-8

别忘了使用计算出的统计数据( X_mean 和 X_std )在推理时间(即测试)中规范化测试数据。