def set_dropout_on(self):
DropoutLayer.SetDropoutOn()
def train_net(config):
# UNPACK CONFIGS
(flag_para_load, train_filenames, val_filenames, train_labels, val_labels,
img_mean) = unpack_configs(config)
# pycuda set up
drv.init()
dev = drv.Device(int(config['gpu'][-1]))
ctx = dev.make_context()
if flag_para_load:
# zmq set up
sock = zmq.Context().socket(zmq.PAIR)
sock.connect('tcp://*****:*****@ iter = ', num_iter
print 'training cost:', cost_ij
if config['print_train_error']:
print 'training error rate:', train_error()
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
############### Test on Validation Set ##################
DropoutLayer.SetDropoutOff()
this_validation_error, this_validation_loss = get_val_error_loss(
rand_arr,
shared_x,
shared_y,
val_filenames,
val_labels,
flag_para_load,
img_mean,
batch_size,
validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue)
print('epoch %i: validation loss %f ' % (epoch, this_validation_loss))
print('epoch %i: validation error %f %%' %
(epoch, this_validation_error * 100.))
val_record.append([this_validation_error, this_validation_loss])
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
learning_rate)
# Save weights
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
def train_net(config, private_config):
# UNPACK CONFIGS
(train_videos_spatial_jhmdb,val_videos_spatial_jhmdb,train_videos_temporal_jhmdb,val_videos_temporal_jhmdb,
train_targets,val_targets,
train_labels_jhmdb,val_labels_jhmdb) = unpack_configs_jhmdb(config,gpu_id=private_config['gpu_id'])
# print('val_len',len(val_videos_spatial_jhmdb),'train_len',len(train_videos_spatial_jhmdb))
if config['modal']=='rgb':
train_videos = list(train_videos_spatial_jhmdb)
test_videos = list(val_videos_spatial_jhmdb)
else:
train_videos = list(train_videos_temporal_jhmdb)
test_videos = list(val_videos_temporal_jhmdb)
print('jhmdb_len',len(train_videos),len(train_labels_jhmdb))#,len(tr_video_length_jhmdb))
flag_para_load =config['para_load']
gpu_send_queue = private_config['queue_gpu_send']
gpu_recv_queue = private_config['queue_gpu_recv']
# pycuda and zmq set up
drv.init()
dev = drv.Device(int(private_config['gpu'][-1]))
ctx = dev.make_context()
sock_gpu = zmq.Context().socket(zmq.PAIR)
if private_config['flag_client']:
sock_gpu.connect('tcp://*****:*****@ iter = ', num_iter
print 'training cost:', cost_ij,'cost_nll:',cost_nll,'cost_attention:',cost_att
if config['print_train_error']:
error_ij = train_error()
gpu_send_queue.put(error_ij)
that_error = gpu_recv_queue.get()
error_ij = (error_ij + that_error) / 2.
if private_config['flag_verbose']:
print 'training error rate:', error_ij
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
if count%20 == 0:
e = time.time()
print "time per 20 iter:", (e - s)
# ############### Test on Validation Set ##################
DropoutLayer.SetDropoutOff()
this_val_error, this_val_loss = get_test_error(config,
shared_x, shared_mask, shared_y,shared_target,shared_use_noise,
shared_conv,test_videos, val_labels_jhmdb,
flag_para_load,
batch_size,num_seq, validate_model_lstm,train_model,
send_queue=load_send_queue, recv_queue=load_recv_queue)
# report validation stats
gpu_send_queue.put(this_val_error)
that_val_error = gpu_recv_queue.get()
this_val_error = (this_val_error + that_val_error) / 2.
gpu_send_queue.put(this_val_loss)
that_val_loss = gpu_recv_queue.get()
this_val_loss = (this_val_loss + that_val_loss) / 2.
if private_config['flag_verbose']:
print('epoch %i: test loss of jhmdb %f ' %
(epoch, this_val_loss))
print('epoch %i: test error of jhmdb %f %%' %
(epoch, this_val_error * 100.))
val_record.append([this_val_error, this_val_loss])
if private_config['flag_save']:
np.save(config['weights_dir'] + 'test_record_jhmdb.npy', val_record)
DropoutLayer.SetDropoutOn()
###########################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx,
val_record, learning_rate)
# Save Weights, only one of them will do
if private_config['flag_save'] :
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
def train_net(config, private_config):
# UNPACK CONFIGS
(flag_para_load, flag_datalayer, train_filenames, val_filenames,
train_labels, val_labels, img_mean) = \
unpack_configs(config, ext_data=private_config['ext_data'],
ext_label=private_config['ext_label'])
gpu_send_queue = private_config['queue_gpu_send']
gpu_recv_queue = private_config['queue_gpu_recv']
# pycuda and zmq set up
drv.init()
dev = drv.Device(int(private_config['gpu'][-1]))
ctx = dev.make_context()
sock_gpu = zmq.Context().socket(zmq.PAIR)
if private_config['flag_client']:
sock_gpu.connect('tcp://*****:*****@ iter = ', num_iter
print 'training cost:', cost_ij
if config['print_train_error']:
error_ij = train_error()
gpu_send_queue.put(error_ij)
that_error = gpu_recv_queue.get()
error_ij = (error_ij + that_error) / 2.
if private_config['flag_verbose']:
print 'training error rate:', error_ij
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
############### Test on Validation Set ##################
DropoutLayer.SetDropoutOff()
this_val_error, this_val_loss = get_val_error_loss(
rand_arr,
shared_x,
shared_y,
val_filenames,
val_labels,
flag_datalayer,
flag_para_load,
batch_size,
validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue)
# report validation stats
gpu_send_queue.put(this_val_error)
that_val_error = gpu_recv_queue.get()
this_val_error = (this_val_error + that_val_error) / 2.
gpu_send_queue.put(this_val_loss)
that_val_loss = gpu_recv_queue.get()
this_val_loss = (this_val_loss + that_val_loss) / 2.
if private_config['flag_verbose']:
print('epoch %i: validation loss %f ' % (epoch, this_val_loss))
print('epoch %i: validation error %f %%' %
(epoch, this_val_error * 100.))
val_record.append([this_val_error, this_val_loss])
if private_config['flag_save']:
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
learning_rate)
# Save Weights, only one of them will do
if private_config['flag_save']:
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
def train_net(config):
# UNPACK CONFIGS
(flag_para_load, train_filenames, val_filenames,
train_labels, val_labels, img_mean) = unpack_configs(config)
if flag_para_load:
# zmq set up
sock = zmq.Context().socket(zmq.PAIR)
sock.connect('tcp://*****:*****@iter " + str(count)
if count == 1:
s = time.time()
if count == 20:
e = time.time()
print "time per 20 iter:", (e - s)
logger.info("time per 20 iter: %f" % (e - s))
cost_ij = train_model_wrap(train_model, shared_x,
shared_y, rand_arr, img_mean,
count, minibatch_index,
minibatch_range, batch_size,
train_filenames, train_labels,
flag_para_load,
config['batch_crop_mirror'],
send_queue=load_send_queue,
recv_queue=load_recv_queue)
if num_iter % config['print_freq'] == 0:
#print 'training @ iter = ', num_iter
#print 'training cost:', cost_ij
logger.info("training @ iter = %i" % (num_iter))
logger.info("training cost: %lf" % (cost_ij))
if config['print_train_error']:
logger.info('training error rate: %lf' % train_error())
#print 'training error rate:', train_error()
if flag_para_load and (count < len(minibatch_range)):
load_send_queue.put('calc_finished')
############### Test on Validation Set ##################
#"""
DropoutLayer.SetDropoutOff()
# result_list = [ this_validation_error, this_validation_error_top5, this_validation_loss ]
# or
# result_list = [ this_validation_error, this_validation_loss ]
result_list = get_val_error_loss(
#this_validation_error, this_validation_loss = get_val_error_loss(
rand_arr, shared_x, shared_y,
val_filenames, val_labels,
flag_para_load, img_mean,
batch_size, validate_model,
send_queue=load_send_queue,
recv_queue=load_recv_queue,
flag_top_5=flag_top5)
logger.info(('epoch %i: validation loss %f ' %
(epoch, result_list[-1])))
#print('epoch %i: validation loss %f ' %
# (epoch, this_validation_loss))
if flag_top5:
logger.info(('epoch %i: validation error (top 1) %f %%, (top5) %f %%' %
(epoch, result_list[0] * 100., result_list[1] * 100.)))
else:
logger.info(('epoch %i: validation error %f %%' %
(epoch, result_list[0] * 100.)))
#print('epoch %i: validation error %f %%' %
# (epoch, this_validation_error * 100.))
val_record.append(result_list)
#val_record.append([this_validation_error, this_validation_loss])
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx,
val_record, learning_rate)
# Save weights
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
#"""
print('Optimization complete.')
loss, error = validate_model()
validation_losses.append(loss)
validation_errors.append(error)
return (np.mean(validation_losses), np.mean(validation_errors))
(val_loss, val_error) = validate(test_set)
print('epoch %i: validation loss %f ' % (epoch, val_loss))
print('epoch %i: validation error %f %%' % (epoch, val_error * 100.))
val_record.append([val_error, val_loss])
np.save(config['weights_dir'] + 'val_record.npy', val_record)
DropoutLayer.SetDropoutOn()
############################################
# Adapt Learning Rate
step_idx = adjust_learning_rate(config, epoch, step_idx, val_record,
learning_rate)
# Save weights
if epoch % config['snapshot_freq'] == 0:
save_weights(layers, config['weights_dir'], epoch)
np.save(config['weights_dir'] + 'lr_' + str(epoch) + '.npy',
learning_rate.get_value())
save_momentums(vels, config['weights_dir'], epoch)
print('Optimization complete.')
