Role 1 * 1 convolution kernel: deep learning 19 interview questions

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　　For example

　　Inception module in the application of

　　Reference material

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Can reduce the amount of calculation can be increased ability nonlinear discriminant

For example

Suppose a height 30, width 40 and a depth of 200 and a three-dimensional tensor 55 height 5, a width of 5 and a depth of convolution same convolution kernel 200, step = 1, the result is a high 30, a width of 40, a depth of a three-dimensional tensor 55, as shown:

Multiplying the convolution calculation process amount of about 5 * 200 * 5 * 30 * 40 * 55 = 330 000 000, the large amount of calculation.

Then we can consider the second convolution, convolution of nuclear first use 1 * 1 on the depth dimension reduction, then rose dimension:

Calculating the convolution process the amount of about:

Step: 1 * 1 * 30 * 200 * 40 * 20 = 4800000

Step: 5 * 5 * 20 * 30 * 40 * 55 = 33000000

The total volume of about multiplication: 37800000

Obviously, to obtain the same end result, use the second method, i.e., first dimension reduction in the depth direction, a second amount of calculation is the first one 37800000/330 000 000 = 0.11.

 

Further, since the layer structure and the introduction of the activation function may introduce additional linear capability.

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Inception module in the application of

In googlenet the inception module on the use of this convolution kernel do dimension reduction 1 * 1 to reduce the amount of calculation ability and increasing non-linear discriminant

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Reference material

"Illustrates the depth of learning and neural networks: from tensor to achieve TensorFlow" _ Zhang Ping

inceptionV1-Going Deeper with Convolutions

"Deep - of - learning - learning - nuclear - Heart - technology - art - and - real - practice"

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