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. 

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' ))

```

Any advice?
thank you!