def train(self, train_set, valid_set, test_set, optimizer, epochs):
fit = self.compile(train_set, optimizer)
from layers import LSTM
fit_state = []
for layer in self.layers:
if isinstance(layer, LSTM):
fit_state.append(layer.h)
fit_state.append(layer.c)
val = self.compile(valid_set)
val_state = []
for layer in self.layers:
if isinstance(layer, LSTM):
val_state.append(layer.h)
val_state.append(layer.c)
for epoch in range(1, epochs + 1):
progress_bar = TrainProgressBar(epoch, train_set.batches,
valid_set.batches)
# fit
fit_results = []
for batch in range(train_set.batches / 2):
fit_results.append(fit(batch))
progress_bar.fit_update(fit_results)
# reset fit state
for state in fit_state:
state.set_value(state.get_value() * 0.)
# validate
val_results = []
for batch in range(valid_set.batches):
val_results.append(val(batch))
progress_bar.val_update(val_results)
# reset val state
for state in val_state:
state.set_value(state.get_value() * 0.)
self.fit_results.append(fit_results)
self.val_results.append(val_results)
print
# fit
fit_results = []
for batch in range(train_set.batches / 2, train_set.batches):
fit_results.append(fit(batch))
progress_bar.fit_update(fit_results)
# reset fit state
for state in fit_state:
state.set_value(state.get_value() * 0.)
# validate
val_results = []
for batch in range(valid_set.batches):
val_results.append(val(batch))
progress_bar.val_update(val_results)
# reset val state
for state in val_state:
state.set_value(state.get_value() * 0.)
self.fit_results.append(fit_results)
self.val_results.append(val_results)
import numpy as np
if epoch >= self.decay_epoch:
self.optimizer.lr.set_value(np.cast[np.float32](
self.optimizer.lr.get_value() * self.decay_rate))
print 'lr', self.optimizer.lr.get_value()
# static test
test = self.compile(test_set)
test_results = []
for batch in range(test_set.batches):
test_results.append(test(batch))
import numpy as np
print 'Static pp({})'.format(np.mean(test_results))
# reset learning rate
self.optimizer.lr.set_value(np.cast[np.float32](1.0))
# dynamic test
test = self.compile(test_set, optimizer)
test_results = []
for batch in range(test_set.batches):
test_results.append(test(batch))
import numpy as np
print 'Dynamic pp({})'.format(np.mean(test_results))
def test_progress_bar():
from utils import TrainProgressBar
progress_bar = TrainProgressBar(1, 10, 10)
progress_bar.fit_update(zip(range(10), range(10)))
progress_bar.val_update(zip(range(5), range(5)))
def train(self, train_set, valid_set, test_set, optimizer, epochs):
fit = self.compile(train_set, optimizer)
from layers import LSTM
fit_state = []
for layer in self.layers:
if isinstance(layer, LSTM):
fit_state.append(layer.h)
fit_state.append(layer.c)
val = self.compile(valid_set)
val_state = []
for layer in self.layers:
if isinstance(layer, LSTM):
val_state.append(layer.h)
val_state.append(layer.c)
for epoch in range(1, epochs + 1):
progress_bar = TrainProgressBar(epoch, train_set.batches, valid_set.batches)
# fit
fit_results = []
for batch in range(train_set.batches / 2):
fit_results.append(fit(batch))
progress_bar.fit_update(fit_results)
# reset fit state
for state in fit_state:
state.set_value(state.get_value() * 0.)
# validate
val_results = []
for batch in range(valid_set.batches):
val_results.append(val(batch))
progress_bar.val_update(val_results)
# reset val state
for state in val_state:
state.set_value(state.get_value() * 0.)
self.fit_results.append(fit_results)
self.val_results.append(val_results)
print
# fit
fit_results = []
for batch in range(train_set.batches / 2, train_set.batches):
fit_results.append(fit(batch))
progress_bar.fit_update(fit_results)
# reset fit state
for state in fit_state:
state.set_value(state.get_value() * 0.)
# validate
val_results = []
for batch in range(valid_set.batches):
val_results.append(val(batch))
progress_bar.val_update(val_results)
# reset val state
for state in val_state:
state.set_value(state.get_value() * 0.)
self.fit_results.append(fit_results)
self.val_results.append(val_results)
import numpy as np
if epoch >= self.decay_epoch:
self.optimizer.lr.set_value(np.cast[np.float32](self.optimizer.lr.get_value() * self.decay_rate))
print 'lr', self.optimizer.lr.get_value()
# static test
test = self.compile(test_set)
test_results = []
for batch in range(test_set.batches):
test_results.append(test(batch))
import numpy as np
print 'Static pp({})'.format(np.mean(test_results))
# reset learning rate
self.optimizer.lr.set_value(np.cast[np.float32](1.0))
# dynamic test
test = self.compile(test_set, optimizer)
test_results = []
for batch in range(test_set.batches):
test_results.append(test(batch))
import numpy as np
print 'Dynamic pp({})'.format(np.mean(test_results))
