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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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import theano |
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import theano.tensor as T |
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from deepy.conf import TrainerConfig |
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from deepy.trainers.base import NeuralTrainer |
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from deepy.trainers.optimize import optimize_updates |
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from logging import getLogger |
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logging = getLogger(__name__) |
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class GeneralNeuralTrainer(NeuralTrainer): |
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""" |
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General neural network trainer. |
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""" |
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def __init__(self, network, config=None, method=None): |
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if method: |
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logging.info("changing optimization method to '%s'" % method) |
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if not config: |
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config = TrainerConfig() |
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elif isinstance(config, dict): |
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config = TrainerConfig(config) |
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config.method = method |
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super(GeneralNeuralTrainer, self).__init__(network, config) |
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logging.info('compiling %s learning function', self.__class__.__name__) |
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self._learning_func = None |
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def learn(self, *variables): |
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if not self._learning_func: |
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self._learning_func = self.learning_function() |
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return self._learning_func(*variables) |
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def learning_updates(self): |
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""" |
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Return updates in the training. |
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""" |
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params = self.network.parameters |
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# Freeze parameters |
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if self.config.fixed_parameters: |
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logging.info("fixed parameters: %s" % ", ".join(map(str, self.config.fixed_parameters))) |
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params = [p for p in params if p not in self.config.fixed_parameters] |
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gradients = T.grad(self.cost, params) |
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updates, free_parameters = optimize_updates(params, gradients, self.config) |
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self.network.free_parameters.extend(free_parameters) |
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logging.info("Added %d free parameters for optimization" % len(free_parameters)) |
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return updates |
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def learning_function(self): |
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""" |
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Get the learning function. |
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:param func: |
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:return: |
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""" |
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network_updates = list(self.network.updates) + list(self.network.training_updates) |
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learning_updates = list(self.learning_updates()) |
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update_list = network_updates + learning_updates |
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logging.info("network updates: %s" % " ".join(map(str, [x[0] for x in network_updates]))) |
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logging.info("learning updates: %s" % " ".join(map(str, [x[0] for x in learning_updates]))) |
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variables = self.network.input_variables + self.network.target_variables |
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givens = None |
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return theano.function( |
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variables, |
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map(lambda v: theano.Out(v, borrow=True), self.training_variables), |
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updates=update_list, allow_input_downcast=True, |
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mode=self.config.get("theano_mode", None), |
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givens=givens) |
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class SGDTrainer(GeneralNeuralTrainer): |
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""" |
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SGD trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(SGDTrainer, self).__init__(network, config, "SGD") |
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class AdaDeltaTrainer(GeneralNeuralTrainer): |
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""" |
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AdaDelta trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(AdaDeltaTrainer, self).__init__(network, config, "ADADELTA") |
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class AdaGradTrainer(GeneralNeuralTrainer): |
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""" |
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AdaGrad trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(AdaGradTrainer, self).__init__(network, config, "ADAGRAD") |
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class FineTuningAdaGradTrainer(GeneralNeuralTrainer): |
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""" |
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AdaGrad trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(FineTuningAdaGradTrainer, self).__init__(network, config, "FINETUNING_ADAGRAD") |
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class AdamTrainer(GeneralNeuralTrainer): |
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""" |
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AdaGrad trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(AdamTrainer, self).__init__(network, config, "ADAM") |
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class RmspropTrainer(GeneralNeuralTrainer): |
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""" |
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RmsProp trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(RmspropTrainer, self).__init__(network, config, "RMSPROP") |
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class MomentumTrainer(GeneralNeuralTrainer): |
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""" |
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Momentum trainer. |
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""" |
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def __init__(self, network, config=None): |
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super(MomentumTrainer, self).__init__(network, config, "MOMENTUM") |
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class FakeTrainer(GeneralNeuralTrainer): |
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""" |
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Fake Trainer does nothing. |
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""" |
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def learning_updates(self): |
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return [] |
zomux /
deepy
