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_))
# save best params
# if valid_ce[-1][1] < 0.25:
res_dir = save_results(train_ce, valid_ce, res_dir, 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(
test_lio_skel(use, test_model, batch, drop, tr.rng, epoch,
tr.batch_size, x_, y_, loader, x_skeleton_))
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
valid_ce.append(test_lio_skel(use, test_model, batch, drop, tr.rng, epoch, tr.batch_size, x_, y_, loader, x_skeleton_))
# save best params
res_dir = save_results(train_ce, valid_ce, res_dir, params=net_convnet3d_grbm_early_fusion.params, out_mean_train=out_mean_all,out_std_train=out_std_all)
if not tr.moved: res_dir = move_results(res_dir)
####################################################################
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
train_ce.append(_avg(ce))
out_mean_all.append(_avg(out_mean_train))
out_std_all.append(_avg(out_std_train))
# validate
valid_ce.append(test_lio_skel(use, test_model, batch, drop, tr.rng, epoch, tr.batch_size, x_, y_, loader, x_skeleton_))
# save best 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))
# save best params
# if valid_ce[-1][1] < 0.25: