如何在运行编译模型时更改keras中图层的尺寸？

Question

我有一个简单的keras模型如下，模型摘要正如我所期望的那样并且模型已经完成，但是，我无法弄清楚如何匹配输入和输出维度以避免错误。

x = Input(shape=(784,1,),name='input')
h = LSTM(128,return_sequences=False,name='lstm')(x)
z = Dense(2,name='z_mu')(h)
x_pred = Dense(128, input_dim=latent_dim, activation='relu',name='seq_1')(z)
x_pred = Dense(original_dim, activation='sigmoid',name='seq_2')(x_pred)
model = Model(inputs=[x, eps], outputs=x_pred)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
vae.summary()


n_samples = 1000
imgs = io.loadh('all_digital_digits_4.h5')['arr_0']   # ((10,n_samples,28,28))
x_train = np.reshape(imgs,(10*n_samples,28,28))
x_train = np.reshape(imgs,(-1,original_dim,1,))
x_test = np.reshape(imgs,(-1,original_dim,1,))

model.fit(x_train,
    x_train,
    shuffle=True,
    epochs=epochs,
    batch_size=batch_size,
    validation_data=(x_test, x_test))

我收到此错误：

我有一个简单的keras模型如下，模型摘要正如我所期望的那样并且模型已经完成，但是，我无法弄清楚如何匹配输入和输出维度以避免错误。

Layer (type)                 Output Shape              Param #   
=================================================================
input (InputLayer)           (None, 784, 1)            0         
_________________________________________________________________
lstm (LSTM)                  (None, 128)               66560     
_________________________________________________________________
z_mu (Dense)                 (None, 2)                 258       
_________________________________________________________________
seq_1 (Dense)                (None, 128)               384       
_________________________________________________________________
seq_2 (Dense)                (None, 784)               101136    
=================================================================
Total params: 168,338
Trainable params: 168,338
Non-trainable params: 0
_________________________________________________________________

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-12-b1befb367cba> in <module>()
 72         epochs=epochs,
 73         batch_size=batch_size,
---> 74         validation_data=(x_test, x_test))
 75 
 76 encoder = Model(x, z_mu)

/Users/asr2031/programs/anaconda/lib/python2.7/site-     packages/keras/engine/training.pyc in fit(self, x, y, batch_size, epochs, verbose,   callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)
1572             class_weight=class_weight,
1573             check_batch_axis=False,
 -> 1574             batch_size=batch_size)
1575         # Prepare validation data.
1576         do_validation = False

/Users/asr2031/programs/anaconda/lib/python2.7/site-  packages/keras/engine/training.pyc in _standardize_user_data(self, x, y,    sample_weight, class_weight, check_batch_axis, batch_size)
1409                                     output_shapes,
1410                                     check_batch_axis=False,
 -> 1411                                     exception_prefix='target')
1412         sample_weights = _standardize_sample_weights(sample_weight,
1413                                                      self._feed_output_names)

/Users/asr2031/programs/anaconda/lib/python2.7/site-packages/keras/engine/training.pyc in _standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
 139                                  ' to have ' + str(len(shapes[i])) +
 140                                  ' dimensions, but got array with shape '  +
 --> 141                                  str(array.shape))
 142             for j, (dim, ref_dim) in enumerate(zip(array.shape, shapes[i])):
 143                 if not j and not check_batch_axis:

ValueError: Error when checking target: expected seq_2 to have 2 dimensions,   but got array with shape (10000, 784, 1)

任何想法在这里发生了什么？

Answer 1

看起来您想要实现自动编码器，因为您为输入和目标传递了相同的值。由于(batch_size,sequence_length,features)，输入处的lstm期望3维作为(batch_size,features)，输出只是两维return_sequences=False。 lstm只返回序列的最后一个元素。如果您想要输出整个序列，请使用：

x = Input(shape=(784,1,),name='input')
h = LSTM(128,return_sequences=True,name='lstm')(x)
z = TimeDistributed(Dense(2),name='z_mu')(h)
x_pred = TimeDistributed(Dense(128, input_dim=latent_dim, activation='relu'),name='seq_1')(z)
x_pred = TimeDistributed(Dense(original_dim, activation='sigmoid'),name='seq_2')(x_pred)
model = Model(inputs=[x, eps], outputs=x_pred)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
vae.summary()

这应该有效。请注意，由于lstm现在返回序列，因此需要像TimeDistributed图层一样来复制Dense图层。另一种解决方案是使用Lambda层来重塑lstm输出。最终，这取决于你想要达到的目标。如果您提供更多详细信息，我可以评论要使用的结构。