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

• TensorFlow—Image classification