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tensorflow::ops::Conv3D

#include <nn_ops.h>

Computes a 3-D convolution given 5-D input and filter tensors.

Summary

In signal processing, cross-correlation is a measure of similarity of two waveforms as a function of a time-lag applied to one of them. This is also known as a sliding dot product or sliding inner-product.

Our Conv3D implements a form of cross-correlation.

Arguments:

  • scope: A Scope object
  • input: Shape[batch, in_depth, in_height, in_width, in_channels].
  • filter: Shape[filter_depth, filter_height, filter_width, in_channels, out_channels]. in_channels must match between input and filter.
  • strides: 1-D tensor of length 5. The stride of the sliding window for each dimension of input. Must have strides[0] = strides[4] = 1.
  • padding: The type of padding algorithm to use.

Returns:

Constructors and Destructors
Conv3D(const ::tensorflow::Scope & scope, ::tensorflow::Input input, ::tensorflow::Input filter, const gtl::ArraySlice< int > & strides, StringPiece padding)
Public attributes
output
Public functions
node() const
::tensorflow::Node *
operator::tensorflow::Input() const
operator::tensorflow::Output() const

Public attributes

output

::tensorflow::Output output

Public functions

Conv3D

 Conv3D(
  const ::tensorflow::Scope & scope,
  ::tensorflow::Input input,
  ::tensorflow::Input filter,
  const gtl::ArraySlice< int > & strides,
  StringPiece padding
)

node

::tensorflow::Node * node() const 

operator::tensorflow::Input

operator::tensorflow::Input() const 

operator::tensorflow::Output

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/conv3-d.html