#include <nn_ops.h>
Computes the grayscale dilation of 4-D input
and 3-D filter
tensors.
The input
tensor has shape [batch, in_height, in_width, depth]
and the filter
tensor has shape [filter_height, filter_width, depth]
, i.e., each input channel is processed independently of the others with its own structuring function. The output
tensor has shape [batch, out_height, out_width, depth]
. The spatial dimensions of the output tensor depend on the padding
algorithm. We currently only support the default "NHWC" data_format
.
In detail, the grayscale morphological 2-D dilation is the max-sum correlation (for consistency with conv2d
, we use unmirrored filters):
output[b, y, x, c] = max_{dy, dx} input[b, strides[1] * y + rates[1] * dy, strides[2] * x + rates[2] * dx, c] + filter[dy, dx, c]
Max-pooling is a special case when the filter has size equal to the pooling kernel size and contains all zeros.
Note on duality: The dilation of input
by the filter
is equal to the negation of the erosion of -input
by the reflected filter
.
Arguments:
[batch, in_height, in_width, depth]
.[filter_height, filter_width, depth]
.[1, stride_height, stride_width, 1]
.[1, rate_height, rate_width, 1]
.Returns:
Output
: 4-D with shape [batch, out_height, out_width, depth]
. Constructors and Destructors | |
---|---|
Dilation2D(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input filter, const gtl::ArraySlice< int > & strides, const gtl::ArraySlice< int > & rates, StringPiece padding) |
Public attributes | |
---|---|
output |
Public functions | |
---|---|
node() const | ::tensorflow::Node * |
operator::tensorflow::Input() const | |
operator::tensorflow::Output() const |
::tensorflow::Output output
Dilation2D( const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input filter, const gtl::ArraySlice< int > & strides, const gtl::ArraySlice< int > & rates, StringPiece padding )
::tensorflow::Node * node() const
operator::tensorflow::Input() const
operator::tensorflow::Output() const
© 2017 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/cc/class/tensorflow/ops/dilation2-d.html