tf.compat.v1.nn.conv2d_backprop

The transpose of conv2d.

tf.compat.v1.nn.conv2d_transpose(
    value=None, filter=None, output_shape=None, strides=None,
    padding='SAME', data_format='NHWC', name=None, input=None,
    filters=None, dilations=None
)

This operation is sometimes called "deconvolution" after (Zeiler et al., 2010), but is really 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`	An int or list of `ints` that has length `1`, `2` or `4`. The stride of the sliding window for each dimension of `input`. If a single value is given it is replicated in the `H` and `W` dimension. By default the `N` and `C` dimensions are set to 0. The dimension order is determined by the value of `data_format`, see below for details.
`padding`	A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of `tf.nn.convolution` for details.
`data_format`	A string. 'NHWC' and 'NCHW' are supported.
`name`	Optional name for the returned tensor.
`input`	Alias for value.
`filters`	Alias for filter.
`dilations`	An int or list of `ints` that has length `1`, `2` or `4`, defaults to 1. The dilation factor for each dimension of`input`. If a single value is given it is replicated in the `H` and `W` dimension. By default the `N` and `C` dimensions are set to 1. If set to k > 1, there will be k-1 skipped cells between each filter element on that dimension. The dimension order is determined by the value of `data_format`, see above for details. Dilations in the batch and depth dimensions if a 4-d tensor must be 1.

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'`.

References:

Deconvolutional Networks: Zeiler et al., 2010 (pdf)

TensorFlow

tf.compat / v1 / v1.nn.conv2d_backprop_input

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tf.compat / v1 / v1.nn.conv2d_backprop_input

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References: