72 lines
2.8 KiB
Python
72 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
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"""Model trainers."""
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import time
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import logging
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from collections import OrderedDict
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import numpy as np
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logger = logging.getLogger(__name__)
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class Trainer(object):
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def __init__(self, model, cost, learning_rule, train_dataset,
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valid_dataset=None, data_monitors=None):
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self.model = model
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self.cost = cost
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self.learning_rule = learning_rule
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self.learning_rule.initialise(self.model.params)
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self.train_dataset = train_dataset
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self.valid_dataset = valid_dataset
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self.data_monitors = OrderedDict([('cost', cost)])
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if data_monitors is not None:
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self.data_monitors.update(data_monitors)
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def do_training_epoch(self):
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for inputs_batch, targets_batch in self.train_dataset:
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activations = self.model.fprop(inputs_batch)
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grads_wrt_outputs = self.cost.grad(activations[-1], targets_batch)
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grads_wrt_params = self.model.grads_wrt_params(
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activations, grads_wrt_outputs)
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self.learning_rule.update_params(grads_wrt_params)
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def monitors(self, dataset, label):
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data_mon_vals = OrderedDict([(key + label, 0.) for key
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in self.data_monitors.keys()])
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for inputs_batch, targets_batch in dataset:
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activations = self.model.fprop(inputs_batch)
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for key, data_monitor in self.data_monitors.items():
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data_mon_vals[key + label] += data_monitor(
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activations[-1], targets_batch)
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for key, data_monitor in self.data_monitors.items():
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data_mon_vals[key + label] /= dataset.num_batches
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return data_mon_vals
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def get_epoch_stats(self):
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epoch_stats = OrderedDict()
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epoch_stats.update(self.monitors(self.train_dataset, '(train)'))
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if self.valid_dataset is not None:
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epoch_stats.update(self.monitors(self.valid_dataset, '(valid)'))
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epoch_stats['cost(param)'] = self.model.params_cost()
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return epoch_stats
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def log_stats(self, epoch, epoch_time, stats):
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logger.info('Epoch {0}: {1:.1f}s to complete\n {2}'.format(
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epoch, epoch_time,
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', '.join(['{0}={1:.2e}'.format(k, v) for (k, v) in stats.items()])
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))
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def train(self, n_epochs, stats_interval=5):
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run_stats = []
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for epoch in range(1, n_epochs + 1):
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start_time = time.clock()
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self.do_training_epoch()
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epoch_time = time.clock() - start_time
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if epoch % stats_interval == 0:
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stats = self.get_epoch_stats()
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self.log_stats(epoch, epoch_time, stats)
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run_stats.append(stats.values())
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return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())}
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