Conv2D

tensorflow C++ API

tensorflow::ops::Conv2D

Addsbiastovalue.

Summary

Given an input tensor of shape[batch, in_height, in_width, in_channels]and a filter / kernel tensor of shape[filter_height, filter_width, in_channels, out_channels], this op performs the following:

1. Flattens the filter to a 2-D matrix with shape [filter_height * filter_width * in_channels, output_channels] .
2. Extracts image patches from the input tensor to form a virtual tensor of shape [batch, out_height, out_width, filter_height * filter_width * in_channels] .
3. For each patch, right-multiplies the filter matrix and the image patch vector.

In detail, with the default NHWC format,

output[b, i, j, k]=
    sum_{di, dj, q} input[b, strides[1]* i + di, strides[2]* j + dj, q]*
                    filter[di, dj, q, k]

Must havestrides[0] = strides[3] = 1. For the most common case of the same horizontal and vertices strides,strides = [1, stride, stride, 1].

Arguments:

• scope: A Scope object
• input: A 4-D tensor. The dimension order is interpreted according to the value ofdata_format, see below for details.
• filter: A 4-D tensor of shape[filter_height, filter_width, in_channels, out_channels]
• strides: 1-D tensor of length 4. The stride of the sliding window for each dimension ofinput. The dimension order is determined by the value ofdata_format, see below for details.
• padding: The type of padding algorithm to use.

Optional attributes (seeAttrs):

• data_format: Specify the data format of the input and output data. With the default format "NHWC", the data is stored in the order of: [batch, height, width, channels]. Alternatively, the format could be "NCHW", the data storage order of: [batch, channels, height, width].

Returns:

• Output: A 4-D tensor. The dimension order is determined by the value ofdata_format, see below for details.

Conv2D 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
• StringPiece padding: Input paddingin value ex)SAME
• Conv2D ::Attrs attrs : Input attrs in value. ex) use_cudnn_on_gpu_ = true;data_format_ = NHWC;

Return:

• Output output : Output object of Conv2D class object.

Result:

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

Using Method