The size calculation of the output vector of the inverse convolution operation in TensorFlow

Inverse convolution operation in TensorFlow

Inverse convolution in TensorFlow

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))

Here the Output_shape_tensor, width and height are calculated as follows, and can be designed filter_size and padding according to the desired output.

Size calculation of output tensor

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 + = ma X (filter_size-stride, 0)
  elif padding = = ' full ':
    input_length-= (stride + filter_size-2) return
  Input_ Length

There is no need to specify Output_shape in the Slim.conv2d_transpose, we can set the kernel size according to the requirement

conv2d_transpose (inputs, filters, Kernel_size, strides= (1, 1), padding= ' Vali d ', data_format= ' channels_last ', Activation=none, Use_bias=true, Kernel_initializer=none, bias_initial Izer=tf.zeros_initializer (), Kernel_regularizer=none, Bias_regularizer=none, Activity_regularizer=none, ke Rnel_constraint=none, Bias_constraint=none, Trainable=true, Name=none, Reuse=none)