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 .
|