Keras ML library: how to do weight clipping after gradient updates? TensorFlow backend

Question

I\'m trying to use Keras to implement part of an algorithm that requires weight clipping, i.e. limiting the weight values after a gradient update. I haven\'t found any solutions

Answer 1

While creating the optimizer object set param clipvalue. It will do precisely what you want.

# all parameter gradients will be clipped to
# a maximum value of 0.5 and
# a minimum value of -0.5.
rsmprop = RMSprop(clipvalue=0.5)

and then use this object to for model compiling

model.compile(loss='mse', optimizer=rsmprop)

For more reference check: here.

Also, I prefer to use clipnorm over clipvalue because with clipnorm the optimization remains stable. For example say you have 2 parameters and the gradients came out to be [0.1, 3]. By using clipvalue the gradients will become [0.1, 0.5] ie there are chances that the direction of steepest decent can get changed drastically. While clipnorm don't have similar problem as all the gradients will be appropriately scaled and the direction will be preserved and all the while ensuring the constraint on the magnitude of the gradient.

Edit: The question asks weights clipping not gradient clipping:

Gradiant clipping on weights is not part of keras code. But maxnorm on weights constraints is. Check here.

Having said that it can be easily implemented. Here is a very small example:

from keras.constraints import Constraint
from keras import backend as K

class WeightClip(Constraint):
    '''Clips the weights incident to each hidden unit to be inside a range
    '''
    def __init__(self, c=2):
        self.c = c

    def __call__(self, p):
        return K.clip(p, -self.c, self.c)

    def get_config(self):
        return {'name': self.__class__.__name__,
                'c': self.c}

import numpy as np
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(30, input_dim=100, W_constraint = WeightClip(2)))
model.add(Dense(1))

model.compile(loss='mse', optimizer='rmsprop')

X = np.random.random((1000,100))
Y = np.random.random((1000,1))

model.fit(X,Y)

I have tested the running of the above code, but not the validity of the constraints. You can do so by getting the model weights after training using model.get_weights() or model.layers[idx].get_weights() and checking whether its abiding the constraints.

Note: The constrain is not added to all the model weights .. but just to the weights of the specific layer its used and also W_constraint adds constrain to W param and b_constraint to b (bias) param