def setup(self):
self.log.info( '..begin setting up train object')
#===================[ build params and deltas list ]==================#
params = []
deltas = []
for i, layer in enumerate(self.model.layers):
layer_name = "{}_{}".format(layer.__class__.__name__, i)
if hasattr(layer, 'params'):
for param in layer.params:
# checked that the param to be updated is shared variable
if is_shared_var(param):
param.name = str(i) + '_' + str(param.name)
param.name += '_' + layer.__class__.__name__
params += [param]
deltas += [shared_zeros(shape=param.shape.eval())]
#=====================[ training params updates ]=====================#
self.log.info("..update params: " + str(params))
train_y_pred, train_layers_stats = self.model.train_fprop(self.model.input_var)
train_cost = self.train_cost(self.model.output_var, train_y_pred).astype(floatX)
gparams = T.grad(train_cost, params)
train_updates = self.learning_method.update(deltas, params, gparams)
#=================[ append updates from each layer ]==================#
for i, layer in enumerate(self.model.layers):
layer_name = "{}_{}".format(layer.__class__.__name__, i)
if hasattr(layer, 'updates') and len(layer.updates) > 0:
self.log.info("..{}: has shared variable updates".format(layer_name))
train_updates += layer.updates
#----[ append updates of stats from each layer to train updates ]-----#
self.train_stats_names, train_stats_vars = split_list(train_layers_stats)
train_stats_vars = [var.astype(floatX) for var in train_stats_vars]
self.train_stats_shared = generate_shared_list(train_stats_vars)
train_stats_updates = merge_lists(self.train_stats_shared, train_stats_vars)
if self.verbose:
train_updates += train_stats_updates
#-------------------------[ train functions ]-------------------------#
self.log.info('..begin compiling functions')
self.training = theano.function(inputs=merge_var(self.model.input_var, self.model.output_var),
outputs=train_cost,
updates=train_updates,
on_unused_input='warn',
allow_input_downcast=True)
self.log.info('..training function compiled')
#=============================[ testing ]=============================#
test_y_pred, test_layers_stats = self.model.test_fprop(self.model.input_var)
#-----[ append updates of stats from each layer to test updates ]-----#
self.test_stats_names, test_stats_vars = split_list(test_layers_stats)
test_stats_vars = [var.astype(floatX) for var in test_stats_vars]
self.test_stats_shared = generate_shared_list(test_stats_vars)
test_stats_updates = []
if self.verbose:
test_stats_updates = merge_lists(self.test_stats_shared, test_stats_vars)
#-------------------------[ test functions ]--------------------------#
test_stopping_error = self.valid_cost(self.model.output_var, test_y_pred).astype(floatX)
test_cost = self.train_cost(self.model.output_var, test_y_pred).astype(floatX)
self.testing = theano.function(inputs=merge_var(self.model.input_var, self.model.output_var),
outputs=(test_stopping_error, test_cost),
updates=test_stats_updates,
on_unused_input='warn',
allow_input_downcast=True)
self.log.info('..testing function compiled')
def setup(self):
self.log.info('..begin setting up train object')
#===================[ build params and deltas list ]==================#
params = []
deltas = []
for i, layer in enumerate(self.model.layers):
layer_name = "{}_{}".format(layer.__class__.__name__, i)
if hasattr(layer, 'params'):
for param in layer.params:
# checked that the param to be updated is shared variable
if is_shared_var(param):
param.name = str(i) + '_' + str(param.name)
param.name += '_' + layer.__class__.__name__
params += [param]
deltas += [shared_zeros(shape=param.shape.eval())]
#=====================[ training params updates ]=====================#
self.log.info("..update params: " + str(params))
train_y_pred, train_layers_stats = self.model.train_fprop(
self.model.input_var)
train_cost = self.train_cost(self.model.output_var,
train_y_pred).astype(floatX)
gparams = T.grad(train_cost, params)
train_updates = self.learning_method.update(deltas, params, gparams)
#=================[ append updates from each layer ]==================#
for i, layer in enumerate(self.model.layers):
layer_name = "{}_{}".format(layer.__class__.__name__, i)
if hasattr(layer, 'updates') and len(layer.updates) > 0:
self.log.info(
"..{}: has shared variable updates".format(layer_name))
train_updates += layer.updates
#----[ append updates of stats from each layer to train updates ]-----#
self.train_stats_names, train_stats_vars = split_list(
train_layers_stats)
train_stats_vars = [var.astype(floatX) for var in train_stats_vars]
self.train_stats_shared = generate_shared_list(train_stats_vars)
train_stats_updates = merge_lists(self.train_stats_shared,
train_stats_vars)
if self.verbose:
train_updates += train_stats_updates
#-------------------------[ train functions ]-------------------------#
self.log.info('..begin compiling functions')
self.training = theano.function(inputs=merge_var(
self.model.input_var, self.model.output_var),
outputs=train_cost,
updates=train_updates,
on_unused_input='warn',
allow_input_downcast=True)
self.log.info('..training function compiled')
#=============================[ testing ]=============================#
test_y_pred, test_layers_stats = self.model.test_fprop(
self.model.input_var)
#-----[ append updates of stats from each layer to test updates ]-----#
self.test_stats_names, test_stats_vars = split_list(test_layers_stats)
test_stats_vars = [var.astype(floatX) for var in test_stats_vars]
self.test_stats_shared = generate_shared_list(test_stats_vars)
test_stats_updates = []
if self.verbose:
test_stats_updates = merge_lists(self.test_stats_shared,
test_stats_vars)
#-------------------------[ test functions ]--------------------------#
test_stopping_error = self.valid_cost(self.model.output_var,
test_y_pred).astype(floatX)
test_cost = self.train_cost(self.model.output_var,
test_y_pred).astype(floatX)
self.testing = theano.function(
inputs=merge_var(self.model.input_var, self.model.output_var),
outputs=(test_stopping_error, test_cost),
updates=test_stats_updates,
on_unused_input='warn',
allow_input_downcast=True)
self.log.info('..testing function compiled')
def run(self):
best_valid_error = float(sys.maxint)
valid_error = float(sys.maxint)
train_cost = float(sys.maxint)
valid_cost = float(sys.maxint)
train_stats_values = []
valid_stats_values = []
epoch = 0
error_dcr = 0
self.best_epoch_last_update = 0
self.best_valid_last_update = float(sys.maxint)
train_stats_names = ['train_' + name for name in self.train_stats_names]
valid_stats_names = ['valid_' + name for name in self.test_stats_names]
job_start = time.time()
while (self.continue_learning(epoch, error_dcr, best_valid_error)):
if epoch > 0:
self.log.info("best_epoch_last_update: %d"%self.best_epoch_last_update)
self.log.info("valid_error_decrease: %f"%error_dcr)
self.log.info("best_valid_last_update: %f"%self.best_valid_last_update)
self.log.info("========[ End of Epoch ]========\n\n")
epoch += 1
start_time = time.time()
num_train_examples = 0
total_train_cost = 0.
train_stats_values = np.zeros(len(train_stats_names), dtype=floatX)
num_valid_examples = 0
total_valid_cost = 0.
total_valid_stopping_cost = 0.
valid_stats_values = np.zeros(len(valid_stats_names), dtype=floatX)
blk = 0
for block in self.dataset:
block_time = time.time()
blk += 1
train_set = block.get_train()
valid_set = block.get_valid()
#====================[ Training Progress ]====================#
if train_set.dataset_size > 0:
self.log.info('..training '+ self.dataset.__class__.__name__
+ ' block %s/%s'%(blk, self.dataset.nblocks))
progbar = Progbar(target=train_set.dataset_size)
blk_sz = 0
for idx in train_set:
cost = self.training(*train_set[idx])
total_train_cost += cost * len(idx)
num_train_examples += len(idx)
train_stats_values += len(idx) * get_shared_values(self.train_stats_shared)
blk_sz += len(idx)
progbar.update(blk_sz)
print
#===================[ Validating Progress ]===================#
if valid_set.dataset_size > 0:
self.log.info('..validating ' + self.dataset.__class__.__name__
+ ' block %s/%s'%(blk, self.dataset.nblocks))
progbar = Progbar(target=valid_set.dataset_size)
blk_sz = 0
for idx in valid_set:
stopping_cost, cost = self.testing(*valid_set[idx])
total_valid_cost += cost * len(idx)
total_valid_stopping_cost += stopping_cost * len(idx)
num_valid_examples += len(idx)
valid_stats_values += len(idx) * get_shared_values(self.test_stats_shared)
blk_sz += len(idx)
progbar.update(blk_sz)
print
self.log.info('block time: %0.2fs'%(time.time()-block_time))
self.log.info(get_mem_usage())
#-------[ Update train best cost and error values ]-------#
if num_train_examples > 0:
train_cost = total_train_cost / num_train_examples
train_stats_values /= num_train_examples
#-------[ Update valid best cost and error values ]-------#
if num_valid_examples > 0:
valid_error = total_valid_stopping_cost / num_valid_examples
valid_cost = total_valid_cost / num_valid_examples
valid_stats_values /= num_valid_examples
if valid_error < best_valid_error:
best_valid_error = valid_error
self.log.info('..best validation error so far')
if self.log.save_model:
self.log._save_model(self.model)
self.log.info('..model saved')
if valid_error < self.best_valid_last_update:
error_dcr = self.best_valid_last_update - valid_error
else:
error_dcr = 0
#==============[ save to database, save epoch error]==============#
if self.log.save_to_database:
self.log._save_to_database(epoch, train_cost, valid_error, best_valid_error)
self.log.info('..sent to database: %s:%s' % (self.log.save_to_database['name'],
self.log.experiment_name))
if self.log.save_epoch_error:
self.log._save_epoch_error(epoch, train_cost, valid_cost, valid_error)
self.log.info('..epoch error saved')
end_time = time.time()
#=====================[ log outputs to file ]=====================#
merged_train = merge_lists(train_stats_names, train_stats_values)
merged_valid = merge_lists(valid_stats_names, valid_stats_values)
outputs = [('epoch', epoch),
('runtime(s)', int(end_time-start_time)),
('train_' + self.train_cost.func_name, train_cost),
('valid_' + self.train_cost.func_name, valid_cost),
('valid_' + self.valid_cost.func_name, valid_error),
('best_valid_' + self.valid_cost.func_name, best_valid_error)]
outputs += merged_train + merged_valid
self.log._log_outputs(outputs)
job_end = time.time()
self.log.info('Job Completed on %s'%time.strftime("%a, %d %b %Y %H:%M:%S", time.gmtime(job_end)))
ttl_time = int(job_end - job_start)
dt = datetime.timedelta(seconds=ttl_time)
self.log.info('Total Time Taken: %s'%str(dt))
self.log.info("========[ End of Job ]========\n\n")
def run(self):
best_valid_error = float(sys.maxint)
valid_error = float(sys.maxint)
train_cost = float(sys.maxint)
valid_cost = float(sys.maxint)
train_stats_values = []
valid_stats_values = []
epoch = 0
error_dcr = 0
self.best_epoch_last_update = 0
self.best_valid_last_update = float(sys.maxint)
train_stats_names = [
'train_' + name for name in self.train_stats_names
]
valid_stats_names = ['valid_' + name for name in self.test_stats_names]
job_start = time.time()
while (self.continue_learning(epoch, error_dcr, best_valid_error)):
if epoch > 0:
self.log.info("best_epoch_last_update: %d" %
self.best_epoch_last_update)
self.log.info("valid_error_decrease: %f" % error_dcr)
self.log.info("best_valid_last_update: %f" %
self.best_valid_last_update)
self.log.info("========[ End of Epoch ]========\n\n")
epoch += 1
start_time = time.time()
num_train_examples = 0
total_train_cost = 0.
train_stats_values = np.zeros(len(train_stats_names), dtype=floatX)
num_valid_examples = 0
total_valid_cost = 0.
total_valid_stopping_cost = 0.
valid_stats_values = np.zeros(len(valid_stats_names), dtype=floatX)
blk = 0
for block in self.dataset:
block_time = time.time()
blk += 1
train_set = block.get_train()
valid_set = block.get_valid()
#====================[ Training Progress ]====================#
if train_set.dataset_size > 0:
self.log.info('..training ' +
self.dataset.__class__.__name__ +
' block %s/%s' % (blk, self.dataset.nblocks))
progbar = Progbar(target=train_set.dataset_size)
blk_sz = 0
for idx in train_set:
cost = self.training(*train_set[idx])
total_train_cost += cost * len(idx)
num_train_examples += len(idx)
train_stats_values += len(idx) * get_shared_values(
self.train_stats_shared)
blk_sz += len(idx)
progbar.update(blk_sz)
print
#===================[ Validating Progress ]===================#
if valid_set.dataset_size > 0:
self.log.info('..validating ' +
self.dataset.__class__.__name__ +
' block %s/%s' % (blk, self.dataset.nblocks))
progbar = Progbar(target=valid_set.dataset_size)
blk_sz = 0
for idx in valid_set:
stopping_cost, cost = self.testing(*valid_set[idx])
total_valid_cost += cost * len(idx)
total_valid_stopping_cost += stopping_cost * len(idx)
num_valid_examples += len(idx)
valid_stats_values += len(idx) * get_shared_values(
self.test_stats_shared)
blk_sz += len(idx)
progbar.update(blk_sz)
print
self.log.info('block time: %0.2fs' %
(time.time() - block_time))
self.log.info(get_mem_usage())
#-------[ Update train best cost and error values ]-------#
if num_train_examples > 0:
train_cost = total_train_cost / num_train_examples
train_stats_values /= num_train_examples
#-------[ Update valid best cost and error values ]-------#
if num_valid_examples > 0:
valid_error = total_valid_stopping_cost / num_valid_examples
valid_cost = total_valid_cost / num_valid_examples
valid_stats_values /= num_valid_examples
if valid_error < best_valid_error:
best_valid_error = valid_error
self.log.info('..best validation error so far')
if self.log.save_model:
self.log._save_model(self.model)
self.log.info('..model saved')
if valid_error < self.best_valid_last_update:
error_dcr = self.best_valid_last_update - valid_error
else:
error_dcr = 0
#==============[ save to database, save epoch error]==============#
if self.log.save_to_database:
self.log._save_to_database(epoch, train_cost, valid_error,
best_valid_error)
self.log.info('..sent to database: %s:%s' %
(self.log.save_to_database['name'],
self.log.experiment_name))
if self.log.save_epoch_error:
self.log._save_epoch_error(epoch, train_cost, valid_cost,
valid_error)
self.log.info('..epoch error saved')
end_time = time.time()
#=====================[ log outputs to file ]=====================#
merged_train = merge_lists(train_stats_names, train_stats_values)
merged_valid = merge_lists(valid_stats_names, valid_stats_values)
outputs = [('epoch', epoch),
('runtime(s)', int(end_time - start_time)),
('train_' + self.train_cost.func_name, train_cost),
('valid_' + self.train_cost.func_name, valid_cost),
('valid_' + self.valid_cost.func_name, valid_error),
('best_valid_' + self.valid_cost.func_name,
best_valid_error)]
outputs += merged_train + merged_valid
self.log._log_outputs(outputs)
job_end = time.time()
self.log.info(
'Job Completed on %s' %
time.strftime("%a, %d %b %Y %H:%M:%S", time.gmtime(job_end)))
ttl_time = int(job_end - job_start)
dt = datetime.timedelta(seconds=ttl_time)
self.log.info('Total Time Taken: %s' % str(dt))
self.log.info("========[ End of Job ]========\n\n")