Adam Optimizer

adam.py

"""
The MIT License (MIT)

Copyright (c) 2015 Alec Radford

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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"""    

    def Adam(cost, params, lr=0.0002, b1=0.1, b2=0.001, e=1e-8):
        updates = []
        grads = T.grad(cost, params)
        i = theano.shared(floatX(0.))
        i_t = i + 1.
        fix1 = 1. - (1. - b1)**i_t
        fix2 = 1. - (1. - b2)**i_t
        lr_t = lr * (T.sqrt(fix2) / fix1)
        for p, g in zip(params, grads):
            m = theano.shared(p.get_value() * 0.)
            v = theano.shared(p.get_value() * 0.)
            m_t = (b1 * g) + ((1. - b1) * m)
            v_t = (b2 * T.sqr(g)) + ((1. - b2) * v)
            g_t = m_t / (T.sqrt(v_t) + e)
            p_t = p - (lr_t * g_t)
            updates.append((m, m_t))
            updates.append((v, v_t))
            updates.append((p, p_t))
        updates.append((i, i_t))
        return updates

To your question @stablum: this is how Theano constructs the computation graph. The adam() function should only be called once to define the updates in the computational graph, therefore m and v get initialized to 0 once.

For people who struggle with the provided code and the message "Incompatible broadcastable dimensions.", they may need to modify the theano.shared(p.get_value() ... ) calls by adding the broadcastable=p.broadcastable option. Then the updates will be broadcastable in the same way as the original variables.

One more proposed change:

m = theano.shared(np.zeros(p.get_value().shape).astype(dtype=theano.config.floatX))
v = theano.shared(np.zeros(p.get_value().shape).astype(dtype=theano.config.floatX))

The code above doesn't handle scalar parameters correctly - the p.get_value() * 0. will create a float64, even if p.get_value() returns a float32.

That's right @bspeice. However, I 'm confused of the value of the b1 and b2, their values are set to 0.9 and 0.999 respectively in original paper.

Yes that is a mistake I think

No it is correct, see the update here again.
It is 1 - beta1 as beta1 and beta1 as 1 - beta1...
Which is 1 - 0.1 hence beta1 = 0.9 exactly what paper says, and 1 - 0.001 = 0.999 which is again exactly what paper says. Here they r using original beta1 as 1-beta1 and similarly with beta2.... Hece the confusion.

please i have a question
is this function is the built in function in tensor flow or this function is another function ????