def compute_output_shape(self, input_shape):
    """Computes the output shape of the layer.

    Args:
      input_shape: Shape tuple (tuple of integers) or list of shape tuples
        (one per output tensor of the layer). Shape tuples can include None for
        free dimensions, instead of an integer.

    Returns:
      output_shape: A tuple representing the output shape.
    """
    input_shape = tf.TensorShape(input_shape).as_list()
    if self.data_format == 'channels_last':
      space = input_shape[1:-1]
      new_space = []
      for i in range(len(space)):
        new_dim = tf_layers_util.conv_output_length(
            space[i],
            self.kernel_size[i],
            padding=self.padding,
            stride=self.strides[i],
            dilation=self.dilation_rate[i])
        new_space.append(new_dim)
      return tf.TensorShape([input_shape[0]] + new_space + [self.filters])
    else:
      space = input_shape[2:]
      new_space = []
      for i in range(len(space)):
        new_dim = tf_layers_util.conv_output_length(
            space[i],
            self.kernel_size[i],
            padding=self.padding,
            stride=self.strides[i],
            dilation=self.dilation_rate[i])
        new_space.append(new_dim)
      return tf.TensorShape([input_shape[0], self.filters] + new_space)
def compute_output_shape(self, input_shape):
    """Computes the output shape of the layer.

    Args:
      input_shape: Shape tuple (tuple of integers) or list of shape tuples
        (one per output tensor of the layer). Shape tuples can include None for
        free dimensions, instead of an integer.

    Returns:
      output_shape: A tuple representing the output shape.
    """
    input_shape = tf.TensorShape(input_shape).as_list()
    if self.data_format == 'channels_last':
      space = input_shape[1:-1]
      new_space = []
      for i in range(len(space)):
        new_dim = tf_layers_util.conv_output_length(
            space[i],
            self.kernel_size[i],
            padding=self.padding,
            stride=self.strides[i],
            dilation=self.dilation_rate[i])
        new_space.append(new_dim)
      return tf.TensorShape([input_shape[0]] + new_space + [self.filters])
    else:
      space = input_shape[2:]
      new_space = []
      for i in range(len(space)):
        new_dim = tf_layers_util.conv_output_length(
            space[i],
            self.kernel_size[i],
            padding=self.padding,
            stride=self.strides[i],
            dilation=self.dilation_rate[i])
        new_space.append(new_dim)
      return tf.TensorShape([input_shape[0], self.filters] + new_space)
def compute_output_shape ( self , input_shape ) : input_shape = tf . TensorShape ( input_shape ) . as_list ( ) if self . data_format == 'channels_last' : space = input_shape [ 1 : - 1 ] new_space = [ ] for i in range ( len ( space ) ) : new_dim = tf_layers_util . conv_output_length ( space [ i ] , self . kernel_size [ i ] , padding = self . padding , stride = self . strides [ i ] , dilation = self . dilation_rate [ i ] ) new_space . append ( new_dim ) return tf . TensorShape ( [ input_shape [ 0 ] ] + new_space + [ self . filters ] ) else : space = input_shape [ 2 : ] new_space = [ ] for i in range ( len ( space ) ) : new_dim = tf_layers_util . conv_output_length ( space [ i ] , self . kernel_size [ i ] , padding = self . padding , stride = self . strides [ i ] , dilation = self . dilation_rate [ i ] ) new_space . append ( new_dim ) return tf . TensorShape ( [ input_shape [ 0 ] , self . filters ] + new_space )
Computes the output shape of the layer.

    Args:
      input_shape: Shape tuple (tuple of integers) or list of shape tuples
        (one per output tensor of the layer). Shape tuples can include None for
        free dimensions, instead of an integer.

    Returns:
      output_shape: A tuple representing the output shape.
Computes the output shape of the layer .