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