(self, nets)
Combines output of multiple convolutional towers into a single tensor.

    It stacks towers one on top another (in height dim) in a 4x1 grid.
    The order is arbitrary design choice and shouldn't matter much.

    Args:
      nets: list of tensors of shape=[batch_size, height, width, num_features].

    Returns:
      A tensor of shape [batch_size, seq_length, features_size].
Combines output of multiple convolutional towers into a single tensor.
Combines output of multiple convolutional towers into a single tensor .
def pool_views_fn(self, nets):
    """Combines output of multiple convolutional towers into a single tensor.

    It stacks towers one on top another (in height dim) in a 4x1 grid.
    The order is arbitrary design choice and shouldn't matter much.

    Args:
      nets: list of tensors of shape=[batch_size, height, width, num_features].

    Returns:
      A tensor of shape [batch_size, seq_length, features_size].
    """
    with tf.variable_scope('pool_views_fn/STCK'):
      net = tf.concat(nets, 1)
      batch_size = net.get_shape().dims[0].value
      feature_size = net.get_shape().dims[3].value
      return tf.reshape(net, [batch_size, -1, feature_size])
def pool_views_fn ( self , nets ) : with tf . variable_scope ( 'pool_views_fn/STCK' ) : net = tf . concat ( nets , 1 ) batch_size = net . get_shape ( ) . dims [ 0 ] . value feature_size = net . get_shape ( ) . dims [ 3 ] . value return tf . reshape ( net , [ batch_size , - 1 , feature_size ] )