I'm still quite novice with regard to convolutional networks. I'm trying to implement multiple Conv1D layers in Keras. Unfortunately, after the very first layer, any subsequent layers throw the following error:

```
tensorflow.python.framework.errors_impl.InvalidArgumentError: Negative dimension size caused by subtracting 8 from 1 for 'conv1d_2/convolution/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1,32], [1,8,32,32].

```

I had thought it may have something to do with the size reduction due to strides, but it still does not work after setting `strides=1` for both Conv1D lines. Here is my code. If the for loop runs, then the error is thrown. 

```py
#State branch
x = Conv1D(layerSize,8,strides=1)(inputState)
x = Activation("relu")(x)

for l in range(conv1Layer-1):
    x = Conv1D(layerSize,8,strides=1)(x)
    x = Activation("relu")(x)

x = MaxPooling1D(pool_size=1)(x)
x = Flatten()(x)
x = Model(inputs=inputState, outputs=x)

```

Any help or advice would be greatly appreciated. Thank you!