提交 2d707e32 编写于 作者: H hedaoyuan

Refine the comments.

上级 e967645c
...@@ -29,40 +29,40 @@ enum ColFormat { kCFO = 0, kOCF = 1 }; ...@@ -29,40 +29,40 @@ enum ColFormat { kCFO = 0, kOCF = 1 };
* *
* \param imData Image data. * \param imData Image data.
* \param imShape The shape of imData, * \param imShape The shape of imData,
* [inputChannels, inputHeight, inputWidth]. * [input_channels, input_height, input_width].
* \param colData Column data. * \param colData Column data.
* \param colShape The shape of colData. * \param colShape The shape of colData.
* *
* If the template argument Format is kCFO, the shape of colData is: * If the template argument Format is kCFO, the shape of colData is:
* [inputChannels, filterHeight, filterWidth, outputHeight, outputWidth] * [input_channels, filter_height, filter_width, output_height, output_width]
* So, it is easy to reshape into a convolution matrix for convolution * So, it is easy to reshape into a convolution matrix for convolution
* calculation based on matrix multiplication. * calculation based on matrix multiplication.
* The shape of convolution matrix is [height, width], where the height is equal * The shape of convolution matrix is [height, width], where the height is equal
* inputChannels * filterHeight * filterWidth, and the width is equal * input_channels * filter_height * filter_width, and the width is equal
* outputHeight * outputWidth. * output_height * output_width.
* *
* Reshape: * Reshape:
* shape of colData shape of convolution matrix * shape of colData shape of convolution matrix
* [inputChannels, * [input_channels,
* filterHeight, * filter_height,
* filterWidth, ======> [height, width] * filter_width, ======> [height, width]
* outputHeight, * output_height,
* outputWidth] * output_width]
* *
* If the template argument Format is kOCF, the shape of colData is: * If the template argument Format is kOCF, the shape of colData is:
* [outputHeight, outputWidth, inputChannels, filterHeight, filterWidth] * [output_height, output_width, input_channels, filter_height, filter_width]
* So, it is easy to reshape into a sequence matrix for rnn calculation. * So, it is easy to reshape into a sequence matrix for rnn calculation.
* The shape of sequence matrix is [seqLength, stepSize], where the seqLength * The shape of sequence matrix is [seq_length, step_size], where the seq_length
* is equal outputHeight * outputWidth, and the stepSize is equal * is equal output_height * output_width, and the step_size is equal
* inputChannels * filterHeight * filterWidth. * input_channels * filter_height * filter_width.
* *
* Reshape: * Reshape:
* shape of colData shape of sequence matrix * shape of colData shape of sequence matrix
* [outputHeight, * [output_height,
* outputWidth, * output_width,
* inputChannels, ======> [seqLength, stepSize] * input_channels, ======> [seqLength, stepSize]
* filterHeight, * filter_height,
* filterWidth] * filter_width]
* *
* \note The caller needs to ensure that imShape.inputChannels is equal to * \note The caller needs to ensure that imShape.inputChannels is equal to
* colShape.inputChannels. * colShape.inputChannels.
......
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