Notes - convolution Network: All connection pooling nuclear convolution kernel

to sum up:

全连接侧重 特征的精确位置
卷积层侧重 特征的相对位置

Thoughts: Each train can learn the same local characteristics do, and why

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Convolution kernel

• A convolution kernel is a local feature
• Therefore, output of the convolution layer may depend less precise location features (compared to the fully connected layers)

Why do you say:
Because the right to fully connected layers of heavy and location are closely related
but closely associated with the right weight and features a convolution layer itself

• Advantages: small amount of calculation, high generalization

Understand the internal logic

Ikekakaku

• A filter
• Role: to make features more focused

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One more convolution kernel used what's the use:

1.提取出更多的局部特征
2.减轻池化层的信息损失

data

On a sample image (upsampling) and downsampling (subsampled)
typically convolutional neural network architecture