tf.nn.depthwise_conv2d_backprop_input
Computes the gradients of depthwise convolution with respect to the input.
tf.nn.depthwise_conv2d_backprop_input(
input_sizes, filter, out_backprop, strides, padding,
data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)
| 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. 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). |
| Returns |
|---|
A Tensor. Has the same type as filter. |