Defined in generated file: python/ops/gen_nn_ops.py
Computes the gradients of 3-D convolution with respect to the filter.
Aliases:
tf.compat.v1.nn.conv3d_backprop_filter
tf.compat.v1.nn.conv3d_backprop_filter_v2
tf.nn.conv3d_backprop_filter_v2
tf.nn.conv3d_backprop_filter(
input,
filter_sizes,
out_backprop,
strides,
padding,
data_format='NDHWC',
dilations=[1, 1, 1, 1, 1],
name=None
)
Args:
input
: ATensor
. Must be one of the following types:half
,bfloat16
,float32
,float64
. Shape[batch, depth, rows, cols, in_channels]
.filter_sizes
: ATensor
of typeint32
. An integer vector representing the tensor shape offilter
, wherefilter
is a 5-D[filter_depth, filter_height, filter_width, in_channels, out_channels]
tensor.out_backprop
: ATensor
. Must have the same type asinput
. Backprop signal of shape[batch, out_depth, out_rows, out_cols, out_channels]
.strides
: A list ofints
that has length>= 5
. 1-D tensor of length 5. The stride of the sliding window for each dimension ofinput
. Must havestrides[0] = strides[4] = 1
.padding
: Astring
from:"SAME", "VALID"
. The type of padding algorithm to use.data_format
: An optionalstring
from:"NDHWC", "NCDHW"
. Defaults to"NDHWC"
. The data format of the input and output data. With the default format "NDHWC", the data is stored in the order of: [batch, in_depth, in_height, in_width, in_channels]. Alternatively, the format could be "NCDHW", the data storage order is: [batch, in_channels, in_depth, in_height, in_width].dilations
: An optional list ofints
. Defaults to[1, 1, 1, 1, 1]
. 1-D tensor of length 5. The dilation factor for each dimension ofinput
. 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 ofdata_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
.