NeuralTrainer

Complexity

Total Complexity

79

Size/Duplication

Total Lines	308
Duplicated Lines	0 %

Importance

Changes	12
Bugs	3	Features	0

19 Methods

Rating	Name	Duplication	Size	Complexity
A	_run_test()	0	9	2
A	load_params()	0	10	2
A	set_params()	0	6	4
B	_run_valid()	0	29	6
A	add_iter_callback()	0	6	1
A	valid_step()	0	6	2
A	copy_params()	0	4	3
C	run()	0	22	8
F	train_step()	0	45	14
F	train()	0	43	14
A	save_params()	0	3	1
A	learn()	0	7	1
B	_add_regularization()	0	12	7
A	test_step()	0	6	2
A	_compile_evaluation_func()	0	8	2
A	skip()	0	6	1
A	_run_train()	0	12	3
A	_setup_costs()	0	15	3
B	__init__()	0	35	3

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import sys
import numpy as np
import theano

from deepy.conf import TrainerConfig
from deepy.dataset import Dataset
from deepy.utils import Timer

from abc import ABCMeta, abstractmethod

from logging import getLogger
logging = getLogger(__name__)

class NeuralTrainer(object):
    """
    A base class for all trainers.
    """
    __metaclass__ = ABCMeta

    def __init__(self, network, config=None):
        """
        Basic neural network trainer.
        :type network: deepy.NeuralNetwork
        :type config: deepy.conf.TrainerConfig
        :return:
        """
        super(NeuralTrainer, self).__init__()

        self.config = None
        if isinstance(config, TrainerConfig):
            self.config = config
        elif isinstance(config, dict):
            self.config = TrainerConfig(config)
        else:
            self.config = TrainerConfig()
        # Model and network all refer to the computational graph
        self.model = self.network = network

        self.network.prepare_training()
        self._setup_costs()

        self.evaluation_func = None

        self.validation_frequency = self.config.validation_frequency
        self.min_improvement = self.config.min_improvement
        self.patience = self.config.patience
        self._iter_callbacks = []

        self.best_cost = 1e100
        self.best_iter = 0
        self.best_params = self.copy_params()
        self._skip_batches = 0
        self._progress = 0
        self.last_cost = 0

    def _compile_evaluation_func(self):
        if not self.evaluation_func:
            logging.info("compile evaluation function")
            self.evaluation_func = theano.function(
                self.network.input_variables + self.network.target_variables,
                self.evaluation_variables,
                updates=self.network.updates,
                allow_input_downcast=True, mode=self.config.get("theano_mode", None))

    def skip(self, n_batches):
        """
        Skip N batches in the training.
        """
        logging.info("Skip %d batches" % n_batches)
        self._skip_batches = n_batches

    def _setup_costs(self):
        self.cost = self._add_regularization(self.network.cost)
        self.test_cost = self._add_regularization(self.network.test_cost)
        self.training_variables = [self.cost]
        self.training_names = ['J']
        for name, monitor in self.network.training_monitors:
            self.training_names.append(name)
            self.training_variables.append(monitor)
        logging.info("monitor list: %s" % ",".join(self.training_names))

        self.evaluation_variables = [self.test_cost]
        self.evaluation_names = ['J']
        for name, monitor in self.network.testing_monitors:
            self.evaluation_names.append(name)
            self.evaluation_variables.append(monitor)

    def _add_regularization(self, cost):
        if self.config.weight_l1 > 0:
            logging.info("L1 weight regularization: %f" % self.config.weight_l1)
            cost += self.config.weight_l1 * sum(abs(w).sum() for w in self.network.parameters)
        if self.config.hidden_l1 > 0:
            logging.info("L1 hidden unit regularization: %f" % self.config.hidden_l1)
            cost += self.config.hidden_l1 * sum(abs(h).mean(axis=0).sum() for h in self.network._hidden_outputs)
        if self.config.hidden_l2 > 0:
            logging.info("L2 hidden unit regularization: %f" % self.config.hidden_l2)
            cost += self.config.hidden_l2 * sum((h * h).mean(axis=0).sum() for h in self.network._hidden_outputs)

        return cost

    def set_params(self, targets, free_params=None):
        for param, target in zip(self.network.parameters, targets):
            param.set_value(target)
        if free_params:
            for param, param_value in zip(self.network.free_parameters, free_params):
                param.set_value(param_value)

    def save_params(self, path):
        self.set_params(*self.best_params)
        self.network.save_params(path)

    def load_params(self, path, exclude_free_params=False):
        """
        Load parameters for the training.
        This method can load free parameters and resume the training progress.
        """
        self.network.load_params(path, exclude_free_params=exclude_free_params)
        self.best_params = self.copy_params()
        # Resume the progress
        if self.network.train_logger.progress() > 0:
            self.skip(self.network.train_logger.progress())

    def copy_params(self):
        checkpoint = (map(lambda p: p.get_value().copy(), self.network.parameters),
                      map(lambda p: p.get_value().copy(), self.network.free_parameters))
        return checkpoint

    def add_iter_callback(self, func):
        """
        Add a iteration callback function (receives an argument of the trainer).
        :return:
        """
        self._iter_callbacks.append(func)

    def train(self, train_set, valid_set=None, test_set=None, train_size=None):
        """
        Train the model and return costs.
        """
        iteration = 0
        while True:
            # Test
            if not iteration % self.config.test_frequency and test_set:
                try:
                    self._run_test(iteration, test_set)
                except KeyboardInterrupt:
                    logging.info('interrupted!')
                    break
            # Validate
            if not iteration % self.validation_frequency and valid_set:
                try:

                    if not self._run_valid(iteration, valid_set):
                        logging.info('patience elapsed, bailing out')
                        break
                except KeyboardInterrupt:
                    logging.info('interrupted!')
                    break
            # Train one step
            try:
                costs = self._run_train(iteration, train_set, train_size)
            except KeyboardInterrupt:
                logging.info('interrupted!')
                break
            # Check costs
            if np.isnan(costs[0][1]):
                logging.info("NaN detected in costs, rollback to last parameters")
                self.set_params(*self.checkpoint)
            else:
                iteration += 1
                self.network.epoch_callback()

            yield dict(costs)

        if valid_set and self.config.get("save_best_parameters", True):
            self.set_params(*self.best_params)
        if test_set:
            self._run_test(-1, test_set)

    @abstractmethod
    def learn(self, *variables):
        """
        Update the parameters and return the cost with given data points.
        :param variables:
        :return:
        """

    def _run_test(self, iteration, test_set):
        """
        Run on test iteration.
        """
        costs = self.test_step(test_set)
        info = ' '.join('%s=%.2f' % el for el in costs)
        message = "test    (iter=%i) %s" % (iteration + 1, info)
        logging.info(message)
        self.network.train_logger.record(message)

    def _run_train(self, iteration, train_set, train_size=None):
        """
        Run one training iteration.
        """
        costs = self.train_step(train_set, train_size)

        if not iteration % self.config.monitor_frequency:
            info = " ".join("%s=%.2f" % item for item in costs)
            message = "monitor (iter=%i) %s" % (iteration + 1, info)
            logging.info(message)
            self.network.train_logger.record(message)
        return costs

    def _run_valid(self, iteration, valid_set, dry_run=False):
        """
        Run one valid iteration, return true if to continue training.
        """
        costs = self.valid_step(valid_set)
        # this is the same as: (J_i - J_f) / J_i > min improvement
        _, J = costs[0]
        marker = ""
        if self.best_cost - J > self.best_cost * self.min_improvement:
            # save the best cost and parameters
            self.best_params = self.copy_params()
            marker = ' *'
            if not dry_run:
                self.best_cost = J
                self.best_iter = iteration

            if self.config.auto_save:
                self.network.train_logger.record_progress(self._progress)
                self.network.save_params(self.config.auto_save, new_thread=True)

        info = ' '.join('%s=%.2f' % el for el in costs)
        iter_str = "iter=%d" % (iteration + 1)
        if dry_run:
            iter_str = "dryrun" + " " * (len(iter_str) - 6)
        message = "valid   (%s) %s%s" % (iter_str, info, marker)
        logging.info(message)
        self.network.train_logger.record(message)
        self.checkpoint = self.copy_params()
        return iteration - self.best_iter < self.patience

    def test_step(self, test_set):
        self._compile_evaluation_func()
        costs = list(zip(
            self.evaluation_names,
            np.mean([self.evaluation_func(*x) for x in test_set], axis=0)))
        return costs

    def valid_step(self, valid_set):
        self._compile_evaluation_func()
        costs = list(zip(
            self.evaluation_names,
            np.mean([self.evaluation_func(*x) for x in valid_set], axis=0)))
        return costs

    def train_step(self, train_set, train_size=None):
        dirty_trick_times = 0
        network_callback = bool(self.network.training_callbacks)
        trainer_callback = bool(self._iter_callbacks)
        cost_matrix = []
        self._progress = 0

        for x in train_set:
            if self._skip_batches == 0:

                if dirty_trick_times > 0:
                    cost_x = self.learn(*[t[:(t.shape[0]/2)] for t in x])
                    cost_matrix.append(cost_x)
                    cost_x = self.learn(*[t[(t.shape[0]/2):] for t in x])
                    dirty_trick_times -= 1
                else:
                    try:
                        cost_x = self.learn(*x)
                    except MemoryError:
                        logging.info("Memory error was detected, perform dirty trick 30 times")
                        dirty_trick_times = 30
                        # Dirty trick
                        cost_x = self.learn(*[t[:(t.shape[0]/2)] for t in x])
                        cost_matrix.append(cost_x)
                        cost_x = self.learn(*[t[(t.shape[0]/2):] for t in x])
                cost_matrix.append(cost_x)
                self.last_cost = cost_x[0]
                if network_callback:
                    self.network.training_callback()
                if trainer_callback:
                    for func in self._iter_callbacks:
                        func(self)
            else:
                self._skip_batches -= 1
            if train_size:
                self._progress += 1
                sys.stdout.write("\x1b[2K\r> %d%% | J=%.2f" % (self._progress * 100 / train_size, self.last_cost))
                sys.stdout.flush()
        self._progress = 0

        if train_size:
            sys.stdout.write("\r")
            sys.stdout.flush()
        costs = list(zip(self.training_names, np.mean(cost_matrix, axis=0)))
        return costs

    def run(self, train_set, valid_set=None, test_set=None, train_size=None, controllers=None):
        """
        Run until the end.
        """
        if isinstance(train_set, Dataset):
            dataset = train_set
            train_set = dataset.train_set()
            valid_set = dataset.valid_set()
            test_set = dataset.test_set()
            train_size = dataset.train_size()

        timer = Timer()
        for _ in self.train(train_set, valid_set=valid_set, test_set=test_set, train_size=train_size):
            if controllers:
                ending = False
                for controller in controllers:
                    if hasattr(controller, 'invoke') and controller.invoke():
                        ending = True
                if ending:
                    break
        timer.report()
        return