title: What happens if I reshape the input model into a 1d matrix? Will it affect performance or what?
body: <p>I built my one custom op from scratch then I load it into python code. but my custom-op treats the input as a one-dimensional array.
  So my question what happens if I reshape the output of the layer.</p>
  <p>For Example: I have this model and I will load my custom_op and enter the output of Maxpooling1D layer into.
  what will happens if i reshape x before pass it into my custom-op.</p>
  <pre><code>def cnn(maxlen, embed_size, recurrent_units, dropout_rate, recurrent_dropout_rate, dense_size, nb_classes):
      input_layer = Input(shape=(maxlen, embed_size), )
  
  
      x = Dropout(dropout_rate)(input_layer)
      x = Conv1D(filters=recurrent_units, kernel_size=2, padding='same', activation='relu')(x)
      x = MaxPooling1D(pool_size=2)(x)
  
      cuslayer_module = tf.load_op_library('./cuslayer.so')
      y=cuslayer_module.cus_layer(x)
  </code></pre>