title: Multiple Conv1D Layers: Negative dimension size caused by subtracting 8 from 1 for 'conv1d_2/convolution/Conv2D
body: <p>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:</p>
  
  <pre><code>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].
  </code></pre>
  
  <p>I had thought it may have something to do with the size reduction due to strides, but it still does not work after setting <code>strides=1</code> for both Conv1D lines. Here is my code. If the for loop runs, then the error is thrown. </p>
  
  <pre class="lang-py prettyprint-override"><code>#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)
  </code></pre>
  
  <p>Any help or advice would be greatly appreciated. Thank you!</p>