tf.nn.dilation2d( input, filter, strides, rates, padding, name=None )
Defined in tensorflow/python/ops/gen_nn_ops.py
.
See the guide: Neural Network > Morphological filtering
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
.
input
: A Tensor
. Must be one of the following types: float32
, float64
, int32
, uint8
, int16
, int8
, int64
, bfloat16
, uint16
, half
, uint32
, uint64
. 4-D with shape [batch, in_height, in_width, depth]
.filter
: A Tensor
. Must have the same type as input
. 3-D with shape [filter_height, filter_width, depth]
.strides
: A list of ints
that has length >= 4
. The stride of the sliding window for each dimension of the input tensor. Must be: [1, stride_height, stride_width, 1]
.rates
: A list of ints
that has length >= 4
. The input stride for atrous morphological dilation. Must be: [1, rate_height, rate_width, 1]
.padding
: A string
from: "SAME", "VALID"
. The type of padding algorithm to use.name
: A name for the operation (optional).A Tensor
. Has the same type as input
.
© 2018 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/python/tf/nn/dilation2d