def main():
n_samples, step = 100, 50
load_data = LoadHAR(add_pitch=False,
add_roll=False,
add_filter=True,
n_samples=n_samples,
step=step,
normalize=True)
conf = ModelConfiguration()
conf.load_datasets(
[load_data.uci_hapt],
label_limit=18) # , load_data.uci_mhealth, load_data.idash
user_idx = -1
user = None
# Create a time-string for our cv run
if user is not None:
train_idx = conf.users != user
test_idx = conf.users == user
conf.cv = ((train_idx, test_idx), )
else:
conf.cv = LeaveOneLabelOut(conf.users)
for train_index, test_index in conf.cv:
conf.user = user
model = CNN(n_in=(n_samples + 2, conf.n_features),
n_filters=[64, 64, 64, 64],
filter_sizes=[5, 5, 3, 3],
pool_sizes=[2, 2, 2, 2],
conv_dropout=0.5,
n_hidden=[128],
dense_dropout=0.5,
n_out=conf.n_classes,
ccf=False,
trans_func=rectify,
out_func=softmax,
batch_norm=True,
input_noise=0.2,
stats=2)
if len(conf.cv) > 1:
user_idx += 1
conf.user = conf.user_names[user_idx]
# Generate root path and edit
root_path = model.get_root_path()
model.root_path = "%s_cv_%s_%s" % (root_path, conf.d, conf.user)
paths.path_exists(model.root_path)
rmdir(root_path)
train = TrainModel(model=model,
anneal_lr=0.75,
anneal_lr_freq=100,
output_freq=1,
pickle_f_custom_freq=100,
f_custom_eval=None)
train.pickle = False
conf.run(train_index,
test_index,
lr=0.003,
n_epochs=300,
model=model,
train=train,
load_data=load_data)
def run_cnn():
add_pitch, add_roll, add_filter = False, False, True
n_samples, step = 200, 100
shuffle = False
batch_size = 64
(train_set, test_set, valid_set, (sequence_length, n_features, n_classes)), name, users = \
ld.LoadHAR().uci_hapt(add_pitch=add_pitch, add_roll=add_roll, add_filter=add_filter,
n_samples=n_samples, step=step, shuffle=shuffle)
X = np.concatenate((train_set[0], test_set[0]), axis=0)
y = np.concatenate((train_set[1], test_set[1]), axis=0)
d = str(
datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d%H%M%S'))
lol = LeaveOneLabelOut(users)
user = 0
for train_index, test_index in lol:
user += 1
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
train_set = (X_train, y_train)
test_set = (X_test, y_test)
valid_set = test_set
n_train = train_set[0].shape[0]
n_test = test_set[0].shape[0]
n_train_batches = n_train // batch_size
n_test_batches = n_test // batch_size
n_valid_batches = n_test // batch_size
print("n_train_batches: %d, n_test_batches: %d" %
(n_train_batches, n_test_batches))
model = CNN(n_in=(sequence_length, n_features),
n_filters=[64, 64, 64, 64],
filter_sizes=[5, 5, 3, 3],
pool_sizes=[2, 2, 2, 2],
conv_dropout=0.2,
n_hidden=[512],
dropout_probability=0.5,
n_out=n_classes,
downsample=0,
ccf=False,
trans_func=rectify,
out_func=softmax,
batch_size=batch_size,
batch_norm=False)
# Generate root path and edit
root_path = model.get_root_path()
model.root_path = "%s_cv_%s_%d" % (root_path, d, user)
paths.path_exists(model.root_path)
rmdir(root_path)
f_train, f_test, f_validate, train_args, test_args, validate_args = model.build_model(
train_set, test_set, valid_set)
train_args['inputs']['batchsize'] = batch_size
train_args['inputs']['learningrate'] = 0.003
train_args['inputs']['beta1'] = 0.9
train_args['inputs']['beta2'] = 1e-6
test_args['inputs']['batchsize'] = batch_size
validate_args['inputs']['batchsize'] = batch_size
train = TrainModel(model=model,
anneal_lr=0.75,
anneal_lr_freq=100,
output_freq=1,
pickle_f_custom_freq=100,
f_custom_eval=None)
train.pickle = True
train.add_initial_training_notes("")
train.write_to_logger("Dataset: %s" % name)
train.write_to_logger("LOO user: %d" % user)
train.write_to_logger("Training samples: %d" % n_train)
train.write_to_logger("Test samples: %d" % n_test)
train.write_to_logger("Sequence length: %d" % sequence_length)
train.write_to_logger("Step: %d" % step)
train.write_to_logger("Shuffle: %s" % shuffle)
train.write_to_logger("Add pitch: %s\nAdd roll: %s" %
(add_pitch, add_roll))
train.write_to_logger("Add filter separated signals: %s" % add_filter)
train.write_to_logger("Transfer function: %s" % model.transf)
train.train_model(f_train,
train_args,
f_test,
test_args,
f_validate,
validate_args,
n_train_batches=n_train_batches,
n_test_batches=n_test_batches,
n_valid_batches=n_valid_batches,
n_epochs=500)
# Reset logging
handlers = train.logger.handlers[:]
for handler in handlers:
handler.close()
train.logger.removeHandler(handler)
del train.logger
