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Build Lenet from Scratch

“The Conv layer is the core building block of a Convolutional Network that does most of the computational heavy lifting.”

This is the demonstration of what convolutional layers do from Stanford cs231n.

See this blog for more details.

It is common to see that a max-pooling layer is inserted between convolutional layer in order to reduce the amount of parameters and computation in the network, including some noise. Intuitively, we downsample the volume of the output of convolutional layers because we only want the most significant features in some certain position.

This is the demonstration of max-pooling layers from Stanford cs231n.

“Left: In this example, the input volume of size [224x224x64] is pooled with filter size 2, stride 2 into output volume of size [112x112x64]. Notice that the volume depth is preserved. Right: The most common downsampling operation is max, giving rise to max pooling, here shown with a stride of 2. That is, each max is taken over 4 numbers (little 2x2 square).”

Neurons have full connections to all activations(flatten th eoutputs) in the previous layer, as seen in regular neural networks. Fully-Connected Layers play classifiers role in the convolutional neural network.

This is the architecture in my code.


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