tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME', data_format='NHWC', name=None)

tf.nn.conv2d_transpose(value, filter, output_shape, strides, padding='SAME', data_format='NHWC', name=None)

See the guides: Layers (contrib) > Higher level ops for building neural network layers, Neural Network > Convolution

The transpose of conv2d.

This operation is sometimes called "deconvolution" after Deconvolutional Networks, but is actually the transpose (gradient) of conv2d rather than an actual deconvolution.

Args:

• value: A 4-D Tensor of type float and shape [batch, height, width, in_channels] for NHWC data format or [batch, in_channels, height, width] for NCHW data format.
• filter: A 4-D Tensor with the same type as value and shape [height, width, output_channels, in_channels]. filter's in_channels dimension must match that of value.
• output_shape: A 1-D Tensor representing the output shape of the deconvolution op.
• strides: A list of ints. The stride of the sliding window for each dimension of the input tensor.
• padding: A string, either 'VALID' or 'SAME'. The padding algorithm. See the comment here
• data_format: A string. 'NHWC' and 'NCHW' are supported.
• name: Optional name for the returned tensor.

Returns:

A Tensor with the same type as value.

Raises:

• ValueError: If input/output depth does not match filter's shape, or if padding is other than 'VALID' or 'SAME'.

Defined in tensorflow/python/ops/nn_ops.py.