tf.nn.conv2d_backprop_filter
tf.nn.conv2d_backprop_filter
tf.nn.conv2d_backprop_filter
conv2d_backprop_filter( input, filter_sizes, out_backprop, strides, padding, use_cudnn_on_gpu=None, data_format=None, name=None )
Defined in tensorflow/python/ops/gen_nn_ops.py
.
See the guide: Neural Network > Convolution
Computes the gradients of convolution with respect to the filter.
Args:
-
input
: ATensor
. Must be one of the following types:half
,float32
. 4-D with shape[batch, in_height, in_width, in_channels]
. -
filter_sizes
: ATensor
of typeint32
. An integer vector representing the tensor shape offilter
, wherefilter
is a 4-D[filter_height, filter_width, in_channels, out_channels]
tensor. -
out_backprop
: ATensor
. Must have the same type asinput
. 4-D with shape[batch, out_height, out_width, out_channels]
. Gradients w.r.t. the output of the convolution. -
strides
: A list ofints
. 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
: Astring
from:"SAME", "VALID"
. The type of padding algorithm to use. -
use_cudnn_on_gpu
: An optionalbool
. Defaults toTrue
. -
data_format
: An optionalstring
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]. -
name
: A name for the operation (optional).
Returns:
A Tensor
. Has the same type as input
. 4-D with shape [filter_height, filter_width, in_channels, out_channels]
. Gradient w.r.t. the filter
input of the convolution.
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_backprop_filter