When using Keras, it's possible to customize the SGD optimizer by directly instantiating the SGD class and using it while compiling the model:
from keras.optimizers import SGD...sgd = SGD(lr=0.0001, momentum=0.8, nesterov=True)model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
The class SGD accepts the parameter lr (the learning rate η with a default set to 0.01), momentum (the parameter μ), nesterov (a boolean indicating whether employing the Nesterov momentum), and an optional decay parameter to indicate whether the learning rate must be decayed over the updates with the following formula:
