Question

我试图合并2个CNN，然后通过自动编码器取消合并，但我总是收到相同的错误消息，内容为“ ValueError：新数组的总大小必须保持不变”，不确定我的网络出了什么问题。

我将DSTM层替换为LSTM和CNN，并且可以使用。

感谢您的帮助！

from keras.layers import Input, Dense, Conv1D, MaxPooling1D, UpSampling1D, Embedding, Dropout, Flatten, Concatenate, LSTM, BatchNormalization, Reshape
from keras.models import Model
from keras import backend as K
from keras.callbacks import TensorBoard


input_address = Input(shape=(38,))
x_address = Reshape((38, 1))(input_address)
x_address = LSTM(125, activation="tanh", return_sequences=True)(x_address)
x_address = Dropout(0.4)(x_address)
x_address = LSTM(125, activation="tanh", return_sequences=True)(x_address)
x_address = Dropout(0.4)(x_address)
x_address = Dense(100, activation = 'relu')(x_address)
x_address = Conv1D(100, 2, activation='relu', padding='same')(x_address)
x_address = MaxPooling1D(2, padding='same')(x_address)
x_address = Dropout(0.5)(x_address)
x_address = Flatten()(x_address)

input_name = Input(shape=(29,))
x_name = Reshape((29, 1))(input_name)
x_name = LSTM(125, activation="tanh", return_sequences=True)(x_name)
x_name = Dropout(0.4)(x_name)
x_name = LSTM(125, activation="tanh", return_sequences=True)(x_name)
x_name = Dropout(0.4)(x_name)
x_name = Dense(100, activation = 'relu')(x_name)
x_name = Conv1D(100, 2, activation='relu', padding='same')(x_name)
x_name = MaxPooling1D(2, padding='same')(x_name)
x_name = Dropout(0.5)(x_name)
x_name = Flatten()(x_name)

merge = Concatenate(name="concat", axis=1)([x_name, x_address])
encoder = Dense(1, activation = 'relu')(merge)


xd_address = Reshape((19, 100))(encoder)
xd_address = UpSampling1D(2)(xd_address)
xd_address = Conv1D(100, 2, activation='relu', padding='same')(xd_address)
xd_address = Dropout(0.4)(xd_address)
xd_address = LSTM(125, activation="tanh", return_sequences=True)(xd_address)
xd_address = Dropout(0.4)(xd_address)
xd_address = LSTM(125, activation="tanh", return_sequences=True)(xd_address)
xd_address = Flatten()(xd_address)

xd_name = Reshape((15, 100))(encoder)
xd_name = UpSampling1D(2)(xd_name)
xd_name = Conv1D(100, 2, activation='relu', padding='same')(xd_name)
xd_name = Dropout(0.4)(xd_name)
xd_name = LSTM(125, activation="tanh", return_sequences=True)(xd_name)
xd_name = Dropout(0.4)(xd_name)
xd_name = LSTM(125, activation="tanh", return_sequences=True)(xd_name)
xd_name = Flatten()(xd_name)

autoencoder = Model(inputs=[input_name, input_address], outputs=[xd_name, xd_address])
autoencoder.compile(optimizer='adam', loss=['mse', 'mse'])

autoencoder.summary()

autoencoder.fit([name_train, address_train], [name_train, address_train]
                 ,epochs=10
                 ,batch_size=120
                 ,shuffle=True
                 ,validation_data=([name_test, address_test], [name_test, address_test])
                 ,callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

这是我收到的错误消息

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-132-19e453d673b6> in <module>()
     33 
     34 
---> 35 xd_address = Reshape((19, 100))(encoder)
     36 xd_address = UpSampling1D(2)(xd_address)
     37 xd_address = Conv1D(100, 2, activation='relu', padding='same')(xd_address)

~\Anaconda3\lib\site-packages\keras\engine\base_layer.py in __call__(self, inputs, **kwargs)
    472             if all([s is not None
    473                     for s in to_list(input_shape)]):
--> 474                 output_shape = self.compute_output_shape(input_shape)
    475             else:
    476                 if isinstance(input_shape, list):

~\Anaconda3\lib\site-packages\keras\layers\core.py in compute_output_shape(self, input_shape)
    392             # input shape known? then we can compute the output shape
    393             return (input_shape[0],) + self._fix_unknown_dimension(
--> 394                 input_shape[1:], self.target_shape)
    395 
    396     def call(self, inputs):

~\Anaconda3\lib\site-packages\keras\layers\core.py in _fix_unknown_dimension(self, input_shape, output_shape)
    380             output_shape[unknown] = original // known
    381         elif original != known:
--> 382             raise ValueError(msg)
    383 
    384         return tuple(output_shape)

ValueError: total size of new array must be unchanged

Answer 1

您正在重塑大小，以更改原始encoder张量中的数字元素。您需要：

print(encoder)，这将为您提供有关原始形状的提示。
在保留元素数量的同时确保Reshape。

这是一个简短的示例，(100,)不能重塑为(2,45)，因为省略了10个元素，即“新数组的总大小必须保持不变”。

在Keras中将2个CNN合并为自动编码器

1 个答案: