# tf.nn.conv2d_transpose

<br />

|---|---|
| [![](https://www.tensorflow.org/images/tf_logo_32px.png) TensorFlow 1 version](https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/nn/conv2d_transpose) | [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.2.0/tensorflow/python/ops/nn_ops.py#L2217-L2291) |

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.

<br />

<br />

<br />

| ## 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` | A string, either `'VALID'` or `'SAME'`. The padding algorithm. See the "returns" section of [`tf.nn.convolution`](https://www.tensorflow.org/versions/r2.2/api_docs/python/tf/nn/convolution) for details. |
| `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. |

<br />

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

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

<br />

#### References:

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))