Conv3D

tensorflow C++ API

tensorflow::ops::Conv3D

Computes a 3-D convolution given 5-Dinputandfiltertensors.

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.

OurConv3Dimplements 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_channelsmust match betweeninputandfilter.
• strides: 1-D tensor of length 5. The stride of the sliding window for each dimension ofinput. Must havestrides[0] = strides[4] = 1.
• padding: The type of padding algorithm to use.

Optional attributes (seeAttrs):

• data_format: 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].

Returns:

• Output: The output tensor.

Conv3D block

Source link : https://github.com/EXPNUNI/enuSpaceTensorflow/blob/master/enuSpaceTensorflow/tf_nn.cpp

Argument:

• Scope scope : A Scope object (A scope is generated automatically each page. A scope is not connected.)
• Input input: connect Input node.
• Input filter: connect Input node.
• gtl::ArraySlice< int > strides: Input strides in value ex)1,2,2,1,1
• StringPiece padding: Input paddingin value ex)SAME
• Conv2D ::Attrs attrs : Input attrs in value. ex) data_format_ = NDHWC;

Return:

• Output output : Output object of Conv3D class object.

Result:

• std::vector(Tensor) result_output : Returned object of executed result by calling session.

Using Method