what is the code of deep learning momentum

The code for implementing momentum in deep learning depends on the specific deep learning framework you are using. However, in general, momentum is implemented by adding a fraction of the previous update to the current update during stochastic gradient descent optimization. Here is an example code snippet in Python using the Keras deep learning library:

from keras.optimizers import SGD

sgd = SGD(lr=0.01, momentum=0.9)
model.compile(loss='categorical_crossentropy', optimizer=sgd)

In this example, the momentum parameter is set to 0.9, which means that 90% of the previous update is added to the current update. This helps the optimizer converge faster and avoid getting stuck in local minima.