Computes the gradients of depthwise convolution with respect to the filter.
tf.nn.depthwise_conv2d_backprop_filter(
input, filter_sizes, out_backprop, strides, padding,
data_format='NHWC', dilations=[1, 1, 1, 1], name=None
)
Args |
|---|
input
|
A Tensor. Must be one of the following types: half, bfloat16,
float32, float64. 4-D with shape based on data_format. For example,
if data_format is 'NHWC' then input is a 4-D [batch, in_height,
in_width, in_channels] tensor.
|
filter_sizes
|
A Tensor of type int32. An integer vector representing the
tensor shape of filter, where filter is a 4-D [filter_height,
filter_width, in_channels, depthwise_multiplier] tensor.
|
out_backprop
|
A Tensor. Must have the same type as input. 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 input.
|
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TensorFlow 1 version
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