def train(model, data, functions, params):
"""Generic routine to perform training on the GPU using Theano-compiled
functions and common parameters.
This will run through a specified number of 'epochs', each consisting of
a full pass through the training data. The epochs are broken into batches
as normal for Stochastic Gradient Descent.
functions: A dictionary containing all of the necessary functions for
training. It will at least have 'momentum', 'update', and 'train_E'
functions. 'momentum' updates the delta for each parameter, 'update'
applies the current delta, and 'train_E' gets the current training
cost. For supervised training, 'val_E' will usually be included
so you can keep track of your progress on the validation set.
params: Necessary training params: LR, training_batches, n_epochs, verbose,
validation_batches, error (links to where best error is tracked).
"""
LR = params['LR']
Nb = 0
for chunk_i in range(len(data.b_samples)):
Nb += params['t_batches'][chunk_i]
print "Training {} epochs at LR = {} rho = {}".format(
params['n_epochs'], LR, params['rho'])
print "Using schedule:", sorted(params['LRsched'].items())
# reference augmentation for checking error (centered, no flip)
T_aug = model.ref_aug
# Main training loop
start_time = time.clock()
for epoch in range(params['n_epochs']):
ct = 0
for chunk_i in range(len(data.b_samples)):
data.T[0].set_value(data.raw[chunk_i])
data.T[1].set_value(
np.asarray(data.labels[chunk_i], dtype=data.ltype))
for batch_i in range(params['t_batches'][chunk_i]):
functions['momentum'](batch_i, LR, model.gen_aug())
functions['update']()
if params['verb'] and (ct + batch_i + 1) % int(Nb / 5) == 0:
print '.',
ct += params['t_batches'][chunk_i]
# check the weight distribution
model.param_status(epoch, output=open("wlog", 'a'))
# compute error on test and validation set
c_train_error = [
functions['train_E'](i, T_aug)
for i in xrange(params['t_batches'][-1])
]
if epoch in params['LRsched']:
LR = params['LRsched'][epoch]
err_train = np.mean(c_train_error)
if 'val_E' in functions:
c_val_error = [
functions['val_E'](i, T_aug)
for i in xrange(params['v_batches'])
]
err_val = np.mean(c_val_error)
else:
err_val = err_train
# if we achieved a new best validation score
# save the model and best validation score
if err_val < getattr(params['error'], "best_error"):
if params['verb']:
print 'S',
setattr(params['error'], "best_error", err_val)
model.save_model()
else:
print ' ',
curr_time = NNl.nice_time(time.clock() - start_time)
if 'val_E' in functions:
if params['verb']:
print(
"{} | epoch {: >4}, LR={:.4f}, train: {:.5f}, val: {:.5f}".
format(curr_time, epoch, LR, err_train, err_val))
else:
print '.',
params['logfile'].write("{} {: >4} {:.6f} {:.8f} {:.8f}\n".format(
curr_time, epoch, LR, err_train, err_val))
else:
if params['verb']:
print("{} | epoch {: >4}, LR={:.5f}, train: {:.6f}".format(
curr_time, epoch, LR, err_train))
params['logfile'].write("{} {: >4} {:.6f} {:.8f}\n".format(
curr_time, epoch, LR, err_train))
def train(model, data, functions, params):
"""Generic routine to perform training on the GPU using Theano-compiled
functions and common parameters.
This will run through a specified number of 'epochs', each consisting of
a full pass through the training data. The epochs are broken into batches
as normal for Stochastic Gradient Descent.
functions: A dictionary containing all of the necessary functions for
training. It will at least have 'momentum', 'update', and 'train_E'
functions. 'momentum' updates the delta for each parameter, 'update'
applies the current delta, and 'train_E' gets the current training
cost. For supervised training, 'val_E' will usually be included
so you can keep track of your progress on the validation set.
params: Necessary training params: LR, training_batches, n_epochs, verbose,
validation_batches, error (links to where best error is tracked).
"""
LR = params['LR']
Nb = 0
for chunk_i in range(len(data.b_samples)):
Nb += params['t_batches'][chunk_i]
print "Training {} epochs at LR = {} rho = {}".format(
params['n_epochs'], LR, params['rho'])
print "Using schedule:", sorted(params['LRsched'].items())
# reference augmentation for checking error (centered, no flip)
T_aug = model.ref_aug
# Main training loop
start_time = time.clock()
for epoch in range(params['n_epochs']):
ct = 0
for chunk_i in range(len(data.b_samples)):
data.T[0].set_value(data.raw[chunk_i])
data.T[1].set_value(np.asarray(data.labels[chunk_i], dtype=data.ltype))
for batch_i in range(params['t_batches'][chunk_i]):
functions['momentum'](batch_i, LR, model.gen_aug())
functions['update']()
if params['verb'] and (ct + batch_i + 1) % int(Nb / 5) == 0:
print '.',
ct += params['t_batches'][chunk_i]
# check the weight distribution
model.param_status(epoch, output=open("wlog", 'a'))
# compute error on test and validation set
c_train_error = [functions['train_E'](i, T_aug) for i in xrange(
params['t_batches'][-1])]
if epoch in params['LRsched']:
LR = params['LRsched'][epoch]
err_train = np.mean(c_train_error)
if 'val_E' in functions:
c_val_error = [functions['val_E'](i, T_aug)
for i in xrange(params['v_batches'])]
err_val = np.mean(c_val_error)
else:
err_val = err_train
# if we achieved a new best validation score
# save the model and best validation score
if err_val < getattr(params['error'], "best_error"):
if params['verb']:
print 'S',
setattr(params['error'], "best_error", err_val)
model.save_model()
else:
print ' ',
curr_time = NNl.nice_time(time.clock() - start_time)
if 'val_E' in functions:
if params['verb']:
print("{} | epoch {: >4}, LR={:.4f}, train: {:.5f}, val: {:.5f}"
.format(curr_time, epoch, LR, err_train, err_val))
else:
print '.',
params['logfile'].write("{} {: >4} {:.6f} {:.8f} {:.8f}\n".format(
curr_time, epoch, LR, err_train, err_val))
else:
if params['verb']:
print("{} | epoch {: >4}, LR={:.5f}, train: {:.6f}".format(
curr_time, epoch, LR, err_train))
params['logfile'].write("{} {: >4} {:.6f} {:.8f}\n".format(
curr_time, epoch, LR, err_train))
