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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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from collections import OrderedDict |
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import inspect |
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import numpy as np |
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import theano |
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from theano import tensor as T |
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from deepy.trainers.optimize import logging |
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from deepy.utils import FLOATX |
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def ada_family_core(params, gparams, learning_rate = 0.01, eps= 1e-6, rho=0.95, method="ADADELTA", |
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beta=0.0, gsum_regularization = 0.0001): |
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""" |
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Optimize by SGD, AdaGrad, or AdaDelta. |
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""" |
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_, _, _, args = inspect.getargvalues(inspect.currentframe()) |
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logging.info("ada_family_core: %s" % str(args.items())) |
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free_parameters = [] |
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if method == "FINETUNING_ADAGRAD": |
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method = "ADAGRAD" |
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gsum_regularization = 0 |
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oneMinusBeta = 1 - beta |
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gsums = [theano.shared(np.zeros_like(param.get_value(borrow=True), dtype=FLOATX), name="gsum_%s" % param.name) if (method == 'ADADELTA' or method == 'ADAGRAD') else None for param in params] |
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xsums = [theano.shared(np.zeros_like(param.get_value(borrow=True), dtype=FLOATX), name="xsum_%s" % param.name) if method == 'ADADELTA' else None for param in params] |
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# Fix for AdaGrad, init gsum to 1 |
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if method == 'ADAGRAD': |
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for gsum in gsums: |
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gsum.set_value(gsum.get_value() ** 0) |
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updates = OrderedDict() |
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# Updates |
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for gparam, param, gsum, xsum in zip(gparams, params, gsums, xsums): |
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if method == 'ADADELTA': |
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updates[gsum] = rho * gsum + (1. - rho) * (gparam **2) |
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dparam = -T.sqrt((xsum + eps) / (updates[gsum] + eps)) * gparam |
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updates[xsum] =rho * xsum + (1. - rho) * (dparam **2) |
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updates[param] = param * oneMinusBeta + dparam |
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elif method == 'ADAGRAD': |
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updates[gsum] = gsum + (gparam **2) - gsum_regularization * gsum |
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updates[param] = param * oneMinusBeta - learning_rate * (gparam / (T.sqrt(updates[gsum] + eps))) |
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else: |
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updates[param] = param * oneMinusBeta - gparam * learning_rate |
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# Add free parameters |
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if method == 'ADADELTA': |
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free_parameters.extend(gsums + xsums) |
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elif method == 'ADAGRAD': |
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free_parameters.extend(gsums) |
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return updates.items(), free_parameters |
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zomux /
deepy
