title: Input shape and Conv1d in Keras
body: <p>The first layer of my neural network is like this:</p>
  
  <pre><code>model.add(Conv1D(filters=40,
                   kernel_size=25,
                   input_shape=x_train.shape[1:],
                   activation='relu',
                   kernel_regularizer=regularizers.l2(5e-6),
                   strides=1))
  </code></pre>
  
  <p>if my input shape is <code>(600,10)</code></p>
  
  <p>i get <code>(None, 576, 40)</code>   as output shape</p>
  
  <p>if my input shape is <code>(6000,1)</code></p>
  
  <p>i get <code>(None, 5976, 40)</code> as output shape</p>
  
  <p>so my question is what exactly is happening here? is the first example simply ignoring 90% of the input?</p>