# tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)

### tf.nn.conv2d_backprop_input(input_sizes, filter, out_backprop, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None)

See the guide: Neural Network > Convolution

Computes the gradients of convolution with respect to the input.

#### Args:

• input_sizes: A Tensor of type int32. An integer vector representing the shape of input, where input is a 4-D [batch, height, width, channels] tensor.
• filter: A Tensor. Must be one of the following types: half, float32, float64. 4-D with shape [filter_height, filter_width, in_channels, out_channels].
• out_backprop: A Tensor. Must have the same type as filter. 4-D with shape [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. Must be in the same order as the dimension specified with format.
• padding: A string from: "SAME", "VALID". The type of padding algorithm to use.
• use_cudnn_on_gpu: An optional bool. Defaults to True.
• 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, in_height, in_width, in_channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, in_channels, in_height, in_width].
• name: A name for the operation (optional).

#### Returns:

A Tensor. Has the same type as filter. 4-D with shape [batch, in_height, in_width, in_channels]. Gradient w.r.t. the input of the convolution.

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