卷积神经网络Conv1d输入形状

问题描述

我正在尝试创建一个CNN来对数据进行分类.我的数据是X [N_data，N_features]我想创建一个能够对其进行分类的神经网络.我的问题是关于keras后端的Conv1D的输入形状.

I am trying to create a CNN to classify data. My Data is X[N_data, N_features]I want to create a neural net capable of classifying it. My problem is concerning the input shape of a Conv1D for the keras back end.

我想对一个过滤器重复一遍..假设有10个特征，然后对接下来的10个特征保持相同的权重.对于每个数据，我的卷积层将创建N_features/10个新神经元.我该怎么办?我应该在input_shape中输入什么?

I want to repeat a filter over.. let say 10 features and then keep the same weights for the next ten features.For each data my convolutional layer would create N_features/10 New neurones.How can i do so? What should I put in input_shape?

def cnn_model():
model = Sequential()
model.add(Conv1D(filters=1, kernel_size=10 ,strides=10,
                  input_shape=(1, 1,N_features),kernel_initializer= 'uniform',
                  activation= 'relu'))
model.flatten()
model.add(Dense(N_features/10, init= 'uniform' , activation= 'relu' ))

有什么建议吗?谢谢！

Any advice?thank you!

推荐答案

@Marcin的答案可能有用，但根据此处的文档可能会提出建议:

@Marcin's answer might work, but might suggestion given the documentation here:

将是:

model = Sequential()
model.add(Conv1D(filters=1, kernel_size=10 ,strides=10,
                  input_shape=(None, N_features),kernel_initializer= 'uniform',
                  activation= 'relu'))

请注意，由于输入数据(N_Data，N_features)，我们将示例数设置为未指定(无).在这种情况下，strides参数控制时间步的大小.

Note that since input data (N_Data, N_features), we set the number of examples as unspecified (None). The strides argument controls the size of of the timesteps in this case.

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