for epoch in xrange(tr.n_epochs):
ce = []
print_params(params)
####################################################################
####################################################################
print "\n%s\n\t epoch %d \n%s"%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
time_start = time()
for i in range(loader.n_iter_train):
#load data
time_start_iter = time()
loader.next_train_batch(x_, y_, x_skeleton_)
tr.batch_size = y_.get_value(borrow=True).shape[0]
ce.append(_batch(train_model, tr.batch_size, batch, True, apply_updates))
timing_report(i, time()-time_start_iter, tr.batch_size, res_dir)
print "\t| "+ training_report(ce[-1]) + ", finish total of: 0." + str(i*1.0/loader.n_iter_train)
# End of Epoch
####################################################################
####################################################################
print "\n%s\n\t End of epoch %d, \n printing some debug info.\n%s" \
%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
# print insp_
train_ce.append(_avg(ce))
# validate
valid_ce.append(test_lio_skel(use, test_model, batch, drop, tr.rng, epoch, tr.batch_size, x_, y_, loader, x_skeleton_))
def _mini_batch(model, mini_batch, batch, is_train, apply_updates=None):
global insp_
ce = []
for i in xrange(batch.mini / batch.micro):
if not is_train:
output = model(mini_batch, i)
ce.append([output[0], output[1]])
return _avg(ce)
def _batch(model, batch_size, batch, is_train=True, apply_updates=None):
ce = []
for i in xrange(batch_size / batch.mini):
ce.append(_mini_batch(model, i, batch, is_train, apply_updates))
return _avg(ce)
ce = []
first_test_file = True
for i in range(loader.n_iter_valid):
if first_test_file:
augm = False
first_test_file = False
else:
augm = True
# load_data(file, rng, epoch, batch_size, x_, y_)
loader.next_train_batch(x_, y_)
#load_data(file, rng, epoch)
ce.append(_batch(test_model, tr.batch_size, batch, is_train=False))
print ce
print_params(params)
####################################################################
####################################################################
print "\n%s\n\t epoch %d \n%s" % ('-' * 30, epoch, '-' * 30)
####################################################################
####################################################################
time_start = time()
for i in range(loader.n_iter_train):
#load data
time_start_iter = time()
loader.next_train_batch(x_, y_, x_skeleton_)
print('tr.batch_size_before=%d' % tr.batch_size)
tr.batch_size = y_.get_value(borrow=True).shape[0]
print('tr.batch_size_after=%d' % tr.batch_size)
ce.append(
_batch(train_model, tr.batch_size, batch, True, apply_updates)[0])
print "the %d iteration,time used:%d" % (i, time() - time_start_iter)
#timing_report(i, time()-time_start_iter, tr.batch_size, res_dir)
print "\t| " + training_report(ce[-1]) + ", finish total of: 0." + str(
i * 1.0 / loader.n_iter_train)
# End of Epoch
####################################################################
####################################################################
print "\n%s\n\t End of epoch %d, \n printing some debug info.\n%s" \
%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
# print insp_
train_ce.append(_avg(ce))
# validate
valid_ce.append(
out_mean_train = []
out_std_train = []
print_params(params)
####################################################################
####################################################################
print "\n%s\n\t epoch %d \n%s"%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
time_start = time()
print loader.n_iter_train
for i in range(loader.n_iter_train):
#load data
time_start_iter = time()
loader.next_train_batch(x_, y_, x_skeleton_)
#tr.batch_size = y_.get_value(borrow=True).shape[0]
ce_temp, out_mean_temp, out_std_temp = _batch(train_model, tr.batch_size, batch, True, apply_updates)
#print out_mean_train, out_std_train
ce.append(ce_temp)
out_mean_train.append(out_mean_temp)
out_std_train.append(out_std_temp)
print "Training: No.%d iter of Total %d, %d s"% (i,loader.n_iter_train, time()-time_start_iter) \
+ "\t| negative_log_likelihood "+ training_report(ce[-1])
# End of Epoch
####################################################################
####################################################################
print "\n%s\n\t End of epoch %d, \n printing some debug info.\n%s" \
%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
print ce
learning_rate_map = linspace(lr.start, lr.stop, tr.n_epochs)
for epoch in xrange(tr.n_epochs):
learning_rate.set_value(float32(learning_rate_map[epoch]))
ce = []
out_mean_train = []
out_std_train = []
print_params(net_convnet3d_grbm_early_fusion.params)
####################################################################
print "\n%s\n\t epoch %d \n%s"%('-'*30, epoch, '-'*30)
time_start = time()
for i in range(loader.n_iter_train):
#load data
time_start_iter = time()
loader.next_train_batch(x_, y_, x_skeleton_)
ce_temp, out_mean_temp, out_std_temp = _batch(train_model, tr.batch_size, batch, True, apply_updates)
ce.append(ce_temp)
out_mean_train.append(out_mean_temp)
out_std_train.append(out_std_temp)
print "Training: No.%d iter of Total %d, %d s"% (i,loader.n_iter_train, time()-time_start_iter) \
+ "\t| negative_log_likelihood "+ training_report(ce[-1])
# End of Epoch
####################################################################
print "\n%s\n\t End of epoch %d, \n printing some debug info.\n%s" \
%('-'*30, epoch, '-'*30)
train_ce.append(_avg(ce))
out_mean_all.append(_avg(out_mean_train))
out_std_all.append(_avg(out_std_train))
# validate
ce = []
print_params(params)
####################################################################
####################################################################
print "\n%s\n\t epoch %d \n%s"%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
for i in range(loader.n_iter_train):
time_start = time()
#load
# load_data(train_file, tr.rng, epoch, tr.batch_size, x_, y_)
loader.next_train_batch(x_, y_)
# print "loading time", time()-time_start
# train
tr.batch_size = y_.get_value(borrow=True).shape[0]
ce.append(_batch(train_model, tr.batch_size, batch, True, apply_updates))
if epoch==0: timing_report(i, time()-time_start, tr.batch_size, res_dir)
print "\t| "+ training_report(ce[-1])
# End of Epoch
#-------------------------------
####################################################################
####################################################################
print "\n%s\n\t End of epoch %d, \n printing some debug info.\n%s" \
%('-'*30, epoch, '-'*30)
####################################################################
####################################################################
# print insp_
train_ce.append(_avg(ce))
# validate
valid_ce.append(test_lio(file_info.valid, use, test_model, batch, drop, tr.rng, epoch, tr.batch_size, x_, y_,loader))
def _mini_batch(model, mini_batch, batch, is_train, apply_updates =None ):
global insp_
ce = []
for i in xrange(batch.mini/batch.micro):
if not is_train:
output = model(mini_batch, i)
ce.append([output[0], output[1]])
return _avg(ce)
def _batch(model, batch_size, batch, is_train=True, apply_updates=None):
ce = []
for i in xrange(batch_size/batch.mini): ce.append(_mini_batch(model, i, batch, is_train, apply_updates))
return _avg(ce)
ce = []
first_test_file = True
for i in range(loader.n_iter_valid):
if first_test_file:
augm = False
first_test_file = False
else: augm = True
# load_data(file, rng, epoch, batch_size, x_, y_)
loader.next_train_batch(x_, y_)
#load_data(file, rng, epoch)
ce.append(_batch(test_model, tr.batch_size, batch, is_train=False))
print ce
