ConvTransposeBackpropFilters
ConvTransposeBackpropFilters#
Versioned name: ConvTransposeBackpropFilters-1
Category: Convolution
Short description: Computes the gradients of a ConvTranspose operation with respect to the filters.
Detailed description:
ConvTransposeBackpropFilters takes the input tensor, the gradient tensor of
output and filter shape (optional) to compute the gradient of filter. The shape
of the filter should either be specified as an input 1D integer tensor or be
determined by the attribute filter_shape
.
Attributes
strides
Description: strides controls the stride along each spatial axis.
Range of values: positive s64 values.
Type: s64[]
Required: yes
pads_begin
Description: pads_begin controls the amount of implicit zero padding of each spatial axis.
Range of values: Non-negative s64 values.
Type: s64[]
Required: yes
Note: the attribute is ignored when auto_pad attribute is specified.
pads_end
Description: pads_end controls the amount of implicit zero padding of each spatial axis.
Range of values: Non-negative s64 values.
Type: s64[]
Required: yes
Note: the attribute is ignored when auto_pad attribute is specified.
dilations
Description: dilations controls the spacing between the kernel points.
Range of values: positive s64 values.
Type: s64[]
Required: yes
auto_pad
Description: auto_pad describes how the padding is calculated.
none (not specified): use explicit padding values from
pads_begin
andpads_end
.same_upper (same_lower) the input is padded to match the output size. In case of odd padding value an extra padding is added at the end (at the beginning).
valid - do not use padding.
Type: string
Default value: none
Required: no
Note: pads_begin and pads_end attributes are ignored when auto_pad is specified.
output_padding
Description: output_padding adds additional amount of padding per each spatial axis in the
output
tensor. It unlocks more elements in the output allowing them to be computed. Elements are added at the higher coordinate indices for the spatial dimensions. Number of elements in output_padding list matches the number of spatial dimensions indata
andoutput
tensors.Range of values: Non-negative s64 values.
Type: s64[]
Default value: all zeros
Required: no
groups
Description: groups denotes the number of groups input channels and output channels are divided into. In_channels and out_channels must both be divisible by groups.
Range of values: A positive s64 value.
Type: s64
Default value: 1
Required: no
data_format
Description: data_format denotes the data format of the input and output data.
Range of values: NXC or NCX (X means HW for 2D ConvTranspose, DHW for 3D ConvTranspose)
Type: string
Default value: NXC
Required: no
filter_format
Description: filter_format denotes the data format of the filter.
Range of values: XIO or OIX (X means HW for 2D ConvTranspose, DHW for 3D ConvTranspose)
Type: string
Default value: XIO
Required: no
filter_shape
Description: filter_shape denotes the shape of the filter.
Type: s64[]
Default value: None
Required: no
Inputs:
1:
input_forward
- original input tensor of ConvTranspose op. Required.Type: T1
2:
output_delta
- the gradient tensor with respect to the output of the ConvTranspose. Required.Type: T1
3:
filter_shape
- 1D integer tensor that specifies shape of the filter. The shape of filter is (out_channels / groups, in_channels, spatial_shape) for OIX format and (spatial_shape, in_channels, out_channels / groups) for XIO format. If specified, filter_shape attribute will be ignored. If not specified, users should pass filter_shape through attribute. In_channels and out_channels must both be divisible by groups. Optional.Type: T2
Outputs:
1:
filter_delta
- gradient tensor with respect to the filter of the ConvTranspose. The format is specified by filter_format attribute.Type: T1
Types:
T1: f32, f16, bf16.
T2: s32
Note: Inputs and outputs have the same data type denoted by T. For example, if input is f32 tensor, then all other tensors have f32 data type.