def fit(self, trX, trY, n_iter=1):
out_shape = self.model[-1].out_shape
n = 0.
t = time()
costs = []
for e in range(n_iter):
epoch_costs = []
for xmb, ymb in self.iterator.iterXY(trX, trY):
c = self._train(xmb, ymb)
epoch_costs.append(c)
n += len(ymb)
if self.verbose >= 2:
n_per_sec = n / (time() - t)
n_left = len(trY)*n_iter - n
time_left = n_left/n_per_sec
sys.stdout.write("\rIter %d Seen %d samples Avg cost %0.4f Examples per second %d Time left %d seconds" % (e, n, np.mean(epoch_costs[-250:]), n_per_sec, time_left))
sys.stdout.flush()
costs.extend(epoch_costs)
n_per_sec = n / (time() - t)
n_left = len(trY)*n_iter - n
time_left = n_left/n_per_sec
status = "Iter %d Seen %d samples Avg cost %0.4f Examples per second %d Time elapsed %d seconds" % (e, n, np.mean(epoch_costs[-250:]), n_per_sec, time() - t)
if self.verbose >= 2:
sys.stdout.write("\r"+status)
sys.stdout.flush()
sys.stdout.write("\n")
elif self.verbose == 1:
print status
return costs
def fit(self, trX, trY, n_iter=1):
out_shape = self.model[-1].out_shape
n = 0.
t = time()
costs = []
for e in range(n_iter):
epoch_costs = []
for xmb, ymb in self.iterator.iterXY(trX, trY):
c = self._train(xmb, ymb)
epoch_costs.append(c)
n += len(ymb)
if self.verbose >= 2:
n_per_sec = n / (time() - t)
n_left = len(trY) * n_iter - n
time_left = n_left / n_per_sec
sys.stdout.write(
"\rIter %d Seen %d samples Avg cost %0.4f Examples per second %d Time left %d seconds"
% (e, n, np.mean(
epoch_costs[-250:]), n_per_sec, time_left))
sys.stdout.flush()
costs.extend(epoch_costs)
n_per_sec = n / (time() - t)
n_left = len(trY) * n_iter - n
time_left = n_left / n_per_sec
status = "Iter %d Seen %d samples Avg cost %0.4f Examples per second %d Time elapsed %d seconds" % (
e, n, np.mean(epoch_costs[-250:]), n_per_sec, time() - t)
if self.verbose >= 2:
sys.stdout.write("\r" + status)
sys.stdout.flush()
sys.stdout.write("\n")
elif self.verbose == 1:
print status
return costs
def fit(self,
trX,
trY,
batch_size=64,
n_epochs=1,
len_filter=LenFilter(),
snapshot_freq=1,
path=None):
"""Train model on given training examples and return the list of costs after each minibatch is processed.
Args:
trX (list) -- Inputs
trY (list) -- Outputs
batch_size (int, optional) -- number of examples in a minibatch (default 64)
n_epochs (int, optional) -- number of epochs to train for (default 1)
len_filter (object, optional) -- object to filter training example by length (default LenFilter())
snapshot_freq (int, optional) -- number of epochs between saving model snapshots (default 1)
path (str, optional) -- prefix of path where model snapshots are saved.
If None, no snapshots are saved (default None)
Returns:
list -- costs of model after processing each minibatch
"""
if len_filter is not None:
trX, trY = len_filter.filter(trX, trY)
trY = standardize_targets(trY, cost=self.cost)
n = 0.
stats = []
t = time()
costs = []
for e in range(n_epochs):
epoch_costs = []
for xmb, ymb in self.iterator.iterXY(trX, trY):
c = self._train(xmb, ymb)
epoch_costs.append(c)
n += len(ymb)
if self.verbose >= 2:
n_per_sec = n / (time() - t)
n_left = len(trY) - n % len(trY)
time_left = n_left / n_per_sec
sys.stdout.write(
"\rEpoch %d Seen %d samples Avg cost %0.4f Time left %d seconds"
% (e, n, np.mean(epoch_costs[-250:]), time_left))
sys.stdout.flush()
costs.extend(epoch_costs)
status = "Epoch %d Seen %d samples Avg cost %0.4f Time elapsed %d seconds" % (
e, n, np.mean(epoch_costs[-250:]), time() - t)
if self.verbose >= 2:
sys.stdout.write("\r" + status)
sys.stdout.flush()
sys.stdout.write("\n")
elif self.verbose == 1:
print status
if path and e % snapshot_freq == 0:
save(self, "{0}.{1}".format(path, e))
return costs
def fit(self, trX, trY, batch_size=64, n_epochs=1, len_filter=LenFilter(), snapshot_freq=1, path=None, callback=None, callback_freq=1):
"""Train model on given training examples and return the list of costs after each minibatch is processed.
Args:
trX (list) -- Inputs
trY (list) -- Outputs
batch_size (int, optional) -- number of examples in a minibatch (default 64)
n_epochs (int, optional) -- number of epochs to train for (default 1)
len_filter (object, optional) -- object to filter training example by length (default LenFilter())
snapshot_freq (int, optional) -- number of epochs between saving model snapshots (default 1)
path (str, optional) -- prefix of path where model snapshots are saved.
If None, no snapshots are saved (default None)
Returns:
list -- costs of model after processing each minibatch
"""
if len_filter is not None:
trX, trY = len_filter.filter(trX, trY)
trY = standardize_targets(trY, cost=self.cost)
n = 0.
stats = []
t = time()
costs = []
for e in range(n_epochs):
epoch_costs = []
for xmb, ymb in self.iterator.iterXY(trX, trY):
if not self.is_padseq_output:
c = self._train(xmb, ymb)
else:
ymb, padsizes = ymb
c = self._train(xmb, ymb, padsizes)
epoch_costs.append(c)
n += len(ymb)
if self.verbose >= 2:
n_per_sec = n / (time() - t)
n_left = len(trY) - n % len(trY)
time_left = n_left/n_per_sec
sys.stdout.write("\rEpoch %d Seen %d samples Avg cost %0.4f Time left %d seconds" % (e, n, np.mean(epoch_costs[-250:]), time_left))
sys.stdout.flush()
costs.extend(epoch_costs)
status = "Epoch %d Seen %d samples Avg cost %0.4f Time elapsed %d seconds" % (e, n, np.mean(epoch_costs[-250:]), time() - t)
if self.verbose >= 2:
sys.stdout.write("\r"+status)
sys.stdout.flush()
sys.stdout.write("\n")
elif self.verbose == 1:
print status
if path and e % snapshot_freq == 0:
save(self, "{0}.{1}".format(path, e))
if callback is not None and e % callback_freq == 0:
try:
callback(e)
except RNN.EarlyStoppingException:
break
return costs