Computes the gradients of convolution with respect to the input.
tf.conv2d_backprop_input_v2(
input: Annotated[Any, TV_Conv2DBackpropInputV2_T],
filter: Annotated[Any, TV_Conv2DBackpropInputV2_T],
out_backprop: Annotated[Any, TV_Conv2DBackpropInputV2_T],
strides,
padding: str,
use_cudnn_on_gpu: bool = True,
explicit_paddings=[],
data_format: str = 'NHWC',
dilations=[1, 1, 1, 1],
name=None
) -> Annotated[Any, TV_Conv2DBackpropInputV2_T]
Args |
input
|
A Tensor . Must be one of the following types: half , bfloat16 , float32 , float64 , int32 .
4-D with shape [batch, in_height, in_width, in_channels] .
Only shape of tensor is used.
|
filter
|
A Tensor . Must have the same type as input . 4-D with shape
[filter_height, filter_width, in_channels, out_channels] .
|
out_backprop
|
A Tensor . Must have the same type as input .
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", "EXPLICIT" .
The type of padding algorithm to use.
|
use_cudnn_on_gpu
|
An optional bool . Defaults to True .
|
explicit_paddings
|
An optional list of ints . Defaults to [] .
If padding is "EXPLICIT" , the list of explicit padding amounts. For the ith
dimension, the amount of padding inserted before and after the dimension is
explicit_paddings[2 * i] and explicit_paddings[2 * i + 1] , respectively. If
padding is not "EXPLICIT" , explicit_paddings must be empty.
|
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].
|
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 .
|