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Layers

Convolutional Layer

  • Extracts attributes from the input data;

  • The number of neurons is equal to the number of filters (kernels);

  • Each neuron has a different kernel;

  • Each neuron processes a part of the input data (receptive field) at the time. When it is done with a receptive field, it moves to the next one;

  • After a neuron has processed the whole input data, it results in a feature map.

Pooling Layer

  • Reduces the spatial dimension of the input data;

  • Helps reduce the number of parameters and computational complexity in the network;

  • Common methods are average and max pooling.

Dropout Layer

  • Used to prevent overfitting;

  • Randomly drops out (by setting the activation to 0) neurons during training.

Flatten Layer

  • Converts the input into a 1D array.

Dense (Fully Connected) Layer

  • Used to classify the extracted features;

  • Each neuron is connected to all the neurons in the previous layer;

  • Needs a flattened input (1D array).

References

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