tensorflow中的逆卷积操作 输出向量的尺寸计算

tensorflow中的逆卷积操作

在tensorflow中逆卷积

outputs = nn.conv2d_transpose(
        inputs,
        self.kernel,
        output_shape_tensor,
        strides,
        padding=self.padding.upper(),
        data_format=utils.convert_data_format(self.data_format, ndim=4))

这里的output_shape_tensor, width 和height的计算方法如下，可以根据需要的输出，设计filter_size 和padding.

输出tensor的大小计算

def deconv_output_length(input_length, filter_size, padding, stride):
  """Determines output length of a transposed convolution given input length.

  Arguments:
      input_length: integer.
      filter_size: integer.
      padding: one of "same", "valid", "full".
      stride: integer.

  Returns:
      The output length (integer).
  """
  if input_length is None:
    return None
  input_length *= stride
  if padding == 'valid':
    input_length += max(filter_size - stride, 0)
  elif padding == 'full':
    input_length -= (stride + filter_size - 2)
  return input_length

在slim.conv2d_transpose中 不需要指定output_shape, 我们可以根据需求设定kernel的大小

conv2d_transpose(
    inputs,
    filters,
    kernel_size,
    strides=(1, 1),
    padding='valid',
    data_format='channels_last',
    activation=None,
    use_bias=True,
    kernel_initializer=None,
    bias_initializer=tf.zeros_initializer(),
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    trainable=True,
    name=None,
    reuse=None
)