# tf.nn.depthwise_conv2d_backprop_input

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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#L3049-L3118) |

Computes the gradients of depthwise convolution with respect to the input.

#### View aliases


**Compat aliases for migration**

See
[Migration guide](https://www.tensorflow.org/guide/migrate) for
more details.

[`tf.compat.v1.nn.depthwise_conv2d_backprop_input`](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d_backprop_input), [`tf.compat.v1.nn.depthwise_conv2d_native_backprop_input`](https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d_backprop_input)

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    tf.nn.depthwise_conv2d_backprop_input(
        input_sizes,
        filter,
        out_backprop,
        strides,
        padding,
        data_format='NHWC',
        dilations=[1, 1, 1, 1],
        name=None
    )

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| ## Args ||
|---|---|
| `input_sizes` | A `Tensor` of type `int32`. An integer vector representing the shape of `input`, based on `data_format`. For example, if `data_format` is 'NHWC' then `input` is a 4-D `[batch, height, width, channels]` tensor. |
| `filter` | A `Tensor`. Must be one of the following types: `half`, `bfloat16`, `float32`, `float64`. 4-D with shape `[filter_height, filter_width, in_channels, depthwise_multiplier]`. |
| `out_backprop` | A `Tensor`. Must have the same type as `filter`. 4-D with shape based on `data_format`. For example, if `data_format` is 'NHWC' then out_backprop shape is `[batch, out_height, out_width, out_channels]`. Gradients w.r.t. the output of the convolution. |
| `strides` | A list of `ints`. The stride of the sliding window for each dimension of the input of the convolution. |
| `padding` | Controls how to pad the image before applying the convolution. Can be 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` | An optional `string` from: `"NHWC", "NCHW"`. Defaults to `"NHWC"`. Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: \[batch, height, width, channels\]. Alternatively, the format could be "NCHW", the data storage order of: \[batch, channels, height, width\]. |
| `dilations` | An optional list of `ints`. Defaults to `[1, 1, 1, 1]`. 1-D tensor of length 4. The dilation factor for each dimension of `input`. 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 must be 1. |
| `name` | A name for the operation (optional). |

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| ## Returns ||
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| A `Tensor`. Has the same type as `filter`. ||

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