title: Should a 1D CNN need padding to retain input length?
body: <p>Shouldn't a 1D CNN with stride = 1 and 1 filter have output length equal to input length without the need for padding?</p>
  
  <p>I thought this was the case, but created a Keras model with these specifications that says the output shape is (17902,1) when the input shape is (17910,1). I'm wondering why the dimension has been reduced, since the stride is 1 and it's a 1D convolution. </p>
  
  <pre><code>model = keras.Sequential([
  layers.Conv1D(filters=1,kernel_size=9,strides=1,activation=tf.nn.relu,input_shape=X_train[0].shape)
          ])
  </code></pre>
  
  <p>I expect that the output shape of this model should be (17910,1), but clearly I'm missing a source of reduction in dimension in this conv. layer.</p>