# tf.nn.conv2d_transpose

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| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.13.1/tensorflow/python/ops/nn_ops.py#L2687-L2771) |

The transpose of `conv2d`.

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

This operation is sometimes called "deconvolution" after
(Zeiler et al., 2010), but is really the transpose (gradient) of
`atrous_conv2d` rather than an actual deconvolution.

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| ## Args ||
|---|---|
| `input` | 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. |
| `filters` | A 4-D `Tensor` with the same type as `input` and shape `[height, width, output_channels, in_channels]`. `filter`'s `in_channels` dimension must match that of `input`. |
| `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` | Either the `string` `"SAME"` or `"VALID"` indicating the type of padding algorithm to use, or a list indicating the explicit paddings at the start and end of each dimension. See [here](https://www.tensorflow.org/api_docs/python/tf/nn#notes_on_padding_2) for more information. When explicit padding is used and data_format is `"NHWC"`, this should be in the form `[[0, 0], [pad_top, pad_bottom], [pad_left, pad_right], [0, 0]]`. When explicit padding used and data_format is `"NCHW"`, this should be in the form `[[0, 0], [0, 0], [pad_top, pad_bottom], [pad_left, pad_right]]`. |
| `data_format` | A string. 'NHWC' and 'NCHW' are supported. |
| `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. |
| `name` | Optional name for the returned tensor. |

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| ## Returns ||
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| A `Tensor` with the same type as `input`. ||

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| ## Raises ||
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| `ValueError` | If input/output depth does not match `filter`'s shape, or if padding is other than `'VALID'` or `'SAME'`. |

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| ## References ||
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| Deconvolutional Networks: [Zeiler et al., 2010](https://ieeexplore.ieee.org/abstract/document/5539957) ([pdf](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.232.4023&rep=rep1&type=pdf)) ||

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