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convolution kernel

Translation of "convolution kernel" in Chinese

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卷积内核

卷积核

The direct convolution kernel includes the following optimizations

直接卷积内核包括以下优化：

Results are written to the output image, and the allocated memory is cleared for the convolution kernel.

结果被写入输出图像，为卷积内核清除分配的内存。

Here x and y are the coordinates of the element in the convolution kernel.

此处，x 和 y 是卷积内核中元素的坐标。

This kernel is then applied to every pixel of the image, with the center element of the convolution kernel placed over the source pixel (Px).

然后将该内核应用于图像的每个像素，卷积内核的中心元素被放置在源像素上（Px ）。

For the DeepBench convolution kernel (Fig. 2) we also include results for the open-source Intel optimized convolution layer implementation using libxsmm.

针对 DeepBench 的卷积内核（图 2 ），我们利用 libxsmm ［3 ］，进行开源英特尔优化的卷积层实施，并记录实施结果。

In JavaScript we need to supply a convolution kernel and its weight

在JavaScript中我们需要提供卷积内核和它的权重

Therefore, the output shape of the convolutional layer is determined by the shape of the input and the shape of the convolution kernel window. In several cases we might want to change the dimensionality of the output

所以卷积层的输出形状由输入形状和卷积核窗口形状决定。本节我们将介绍卷积层的两个超参数，即填充和步幅。它们可以对给定形状的输入和卷积核改变输出形状。

The shape of some layers can change. The convolution layer, for example, adds a dimension and changes the value of length and width according to the characteristics of the convolution kernel (filter).

某些层的形状是会发生变化的。例如卷积层，会增加一个维度，并且根据卷积核（过滤器）的特征改变长宽的值。

If you follow the comments in the code, "BoxBlur.cpp" is setting up offsets, handling calculations when edge conditions make convolution impossible, and applying the convolution kernel to each element for red, blue, and green colors.

如果您遵循了代码中的注释， "BoxBlur.cpp"正在设置偏移，处理计算（当边界条件使卷积无法实现时），以及将卷积内核应用于每个红、蓝、绿色元素。

Intuitively, separable convolutions can be understood as a way to factorize a convolution kernel into two smaller kernels, or as an extreme version of an Inception block.

直观地说，可分离的卷积可以理解为一种将卷积核分解成两个较小的卷积核的方法，或者作为 Inception 块的一个极端版本。