[英]Keras Conv1d input shape problem, Input 0 of layer conv1d is incompatible with the layer: : expected min_ndim=3, found ndim=2
[英]Error 'Input 0 is incompatible with layer conv1d_48: expected ndim=3, found ndim=2' when adding Conv1D layer
我正在尝试构建以下模型:
model = Sequential()
model.add(Embedding(input_dim = num_top_words, output_dim = 64, input_length = input_length))
model.add(LSTM(100, activation = 'relu'))
model.add(Conv1D(64, kernel_size = 5, activation = 'relu'))
model.add(MaxPooling1D())
model.add(Dense(5, activation = 'softmax'))
model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
但是运行它时出现以下错误:
Input 0 is incompatible with layer conv1d_48: expected ndim=3, found ndim=2
指出以下行存在错误:
model.add(Conv1D(64, kernel_size = 5, activation = 'relu'))
可能是什么问题?
问题在于,当前LSTM
层的输出形状为(None, 100)
,但是,正如错误所暗示的,像LSTM
层一样的Conv1D
层希望输入3D形状(None, n_steps, n_features)
。 因此,解决此问题的一种方法是将return_sequences=True
传递到LSTM层以获取每个时间步的输出,因此其输出将为3D:
model.add(LSTM(100, activation = 'relu', return_sequences=True))
或者,您可以将Conv1D
和MaxPooling1D
层放在LSTM
层之前(这可能比当前体系结构更好,因为Conv1D
加上池化层的一种用法是减小LSTM层的输入维,从而降低计算复杂度):
model.add(Conv1D(64, kernel_size = 5, activation = 'relu'))
model.add(MaxPooling1D())
model.add(LSTM(100, activation = 'relu'))
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