#!/usr/bin/env python import importlib import logging import operator import os import sys from functools import reduce from theano import tensor from blocks import roles from blocks.algorithms import AdaDelta, CompositeRule, GradientDescent, RemoveNotFinite, StepRule from blocks.extensions import Printing, FinishAfter from blocks.extensions.monitoring import DataStreamMonitoring, TrainingDataMonitoring from blocks.extensions.plot import Plot from blocks.extensions.saveload import Dump, LoadFromDump from blocks.filter import VariableFilter from blocks.graph import ComputationGraph, apply_dropout, apply_noise from blocks.main_loop import MainLoop from blocks.model import Model logger = logging.getLogger(__name__) class ElementwiseRemoveNotFinite(StepRule): """A step rule that replaces non-finite coefficients by zeros. Replaces non-finite elements (such as ``inf`` or ``NaN``) in a step (the parameter update of a single shared variable) with a scaled version of the parameters being updated instead. Parameters ---------- scaler : float, optional The scaling applied to the parameter in case the step contains non-finite elements. Defaults to 0.1. Notes ----- This trick was originally used in the GroundHog_ framework. .. _GroundHog: https://github.com/lisa-groundhog/GroundHog """ def __init__(self, scaler=0.1): self.scaler = scaler def compute_step(self, param, previous_step): not_finite = tensor.isnan(previous_step) + tensor.isinf(previous_step) step = tensor.switch(not_finite, self.scaler * param, previous_step) return step, [] if __name__ == "__main__": if len(sys.argv) != 2: print >> sys.stderr, 'Usage: %s config' % sys.argv[0] sys.exit(1) model_name = sys.argv[1] config = importlib.import_module('.%s' % model_name, 'config') logger.info('# Configuration: %s' % config.__name__) for key in dir(config): if not key.startswith('__') and isinstance(getattr(config, key), (int, str, list, tuple)): logger.info(' %20s %s' % (key, str(getattr(config, key)))) model = config.Model(config) model.initialize() stream = config.Stream(config) inputs = stream.inputs() req_vars = model.cost.inputs train_stream = stream.train(req_vars) valid_stream = stream.valid(req_vars) cost = model.cost(**inputs) cg = ComputationGraph(cost) unmonitor = set() if hasattr(config, 'dropout') and config.dropout < 1.0: unmonitor.update(VariableFilter(roles=[roles.COST])(cg.variables)) cg = apply_dropout(cg, config.dropout_inputs(cg), config.dropout) if hasattr(config, 'noise') and config.noise > 0.0: unmonitor.update(VariableFilter(roles=[roles.COST])(cg.variables)) cg = apply_noise(cg, config.noise_inputs(cg), config.noise) cost = cg.outputs[0] cg = Model(cost) logger.info('# Parameter shapes:') parameters_size = 0 for key, value in cg.get_params().iteritems(): logger.info(' %20s %s' % (value.get_value().shape, key)) parameters_size += reduce(operator.mul, value.get_value().shape, 1) logger.info('Total number of parameters: %d in %d matrices' % (parameters_size, len(cg.get_params()))) params = cg.parameters algorithm = GradientDescent( cost=cost, step_rule=CompositeRule([ ElementwiseRemoveNotFinite(), AdaDelta(), #Momentum(learning_rate=config.learning_rate, momentum=config.momentum), ]), params=params) monitored = set([cost] + VariableFilter(roles=[roles.COST])(cg.variables)) - unmonitor plot_vars = [['valid_' + x.name for x in monitored]] logger.info('Plotted variables: %s' % str(plot_vars)) dump_path = os.path.join('model_data', model_name) logger.info('Dump path: %s' % dump_path) extensions=[TrainingDataMonitoring(monitored, prefix='train', every_n_batches=1000), DataStreamMonitoring(monitored, valid_stream, prefix='valid', every_n_batches=1000), Printing(every_n_batches=1000), Plot(model_name, channels=plot_vars, every_n_batches=500), Dump(dump_path, every_n_batches=5000), LoadFromDump(dump_path), #FinishAfter(after_n_batches=2), ] main_loop = MainLoop( model=cg, data_stream=train_stream, algorithm=algorithm, extensions=extensions) main_loop.run() main_loop.profile.report()