def main():
# Load data
X, y = load_data('actuator', use_targets=False)
X_seq, y_seq = data_to_seq(X, y,
t_lag=32, t_future_shift=1, t_future_steps=1, t_sw_step=1)
# Split
train_end = int((45. / 100.) * len(X_seq))
test_end = int((90. / 100.) * len(X_seq))
X_train, y_train = X_seq[:train_end], y_seq[:train_end]
X_test, y_test = X_seq[train_end:test_end], y_seq[train_end:test_end]
X_valid, y_valid = X_seq[test_end:], y_seq[test_end:]
data = {
'train': [X_train, y_train],
'valid': [X_valid, y_valid],
'test': [X_test, y_test],
}
# Re-format targets
for set_name in data:
y = data[set_name][1]
y = y.reshape((-1, 1, np.prod(y.shape[1:])))
data[set_name][1] = [y[:,:,i] for i in xrange(y.shape[2])]
# Model & training parameters
nb_train_samples = data['train'][0].shape[0]
input_shape = list(data['train'][0].shape[1:])
nb_outputs = len(data['train'][1])
gp_input_shape = (1,)
batch_size = 128
epochs = 100
nn_params = {
'H_dim': 16,
'H_activation': 'tanh',
'dropout': 0.1,
}
gp_params = {
'cov': 'SEiso',
'hyp_lik': -2.0,
'hyp_cov': [[-0.7], [0.0]],
'opt': {},
}
# Retrieve model config
nn_configs = load_NN_configs(filename='lstm.yaml',
input_shape=input_shape,
output_shape=gp_input_shape,
params=nn_params)
gp_configs = load_GP_configs(filename='gp.yaml',
nb_outputs=nb_outputs,
batch_size=batch_size,
nb_train_samples=nb_train_samples,
params=gp_params)
# Construct & compile the model
model = assemble('GP-LSTM', [nn_configs['1H'], gp_configs['GP']])
loss = [gen_gp_loss(gp) for gp in model.output_gp_layers]
model.compile(optimizer=Adam(1e-2), loss=loss)
# Callbacks
callbacks = [EarlyStopping(monitor='val_mse', patience=10)]
# Train the model
history = train(model, data, callbacks=callbacks, gp_n_iter=5,
checkpoint='gp_lstm_actuator', checkpoint_monitor='val_mse',
epochs=epochs, batch_size=batch_size, verbose=2)
# Finetune the model
model.finetune(*data['train'],
batch_size=batch_size,
gp_n_iter=100,
verbose=0)
# Test the model
X_test, y_test = data['test']
y_preds = model.predict(X_test)
rmse_predict = RMSE(y_test, y_preds)
print('Test predict RMSE:', rmse_predict)
def main():
dataset = np.loadtxt("traindata.txt", delimiter=",")
X_train = dataset[:, 0:17]
y_train = dataset[:, 17]
dataset = np.loadtxt("testdata.txt", delimiter=",")
X_test = dataset[:, 0:17]
y_test = dataset[:, 17]
dataset = np.loadtxt("validdata.txt", delimiter=",")
X_valid = dataset[:, 0:17]
y_valid = dataset[:, 17]
X_train, X_test, X_valid = standardize_data(X_train, X_test, X_valid)
X_train = X_train.reshape(-1, 17)
X_test = X_test.reshape(-1, 17)
X_valid = X_valid.reshape(-1, 17)
y_valid = y_valid.reshape(-1, 1)
y_test = y_test.reshape(-1, 1)
y_train = y_train.reshape(-1, 1)
X_train, y_train = data_to_seq(X_train,
y_train,
t_lag=32,
t_future_shift=1,
t_future_steps=1,
t_sw_step=1)
X_valid, y_valid = data_to_seq(X_valid,
y_valid,
t_lag=32,
t_future_shift=1,
t_future_steps=1,
t_sw_step=1)
X_test, y_test = data_to_seq(X_test,
y_test,
t_lag=32,
t_future_shift=1,
t_future_steps=1,
t_sw_step=1)
data = {
'train': [X_train, y_train],
'valid': [X_valid, y_valid],
'test': [X_test, y_test],
}
# Model & training parameters
input_shape = list(data['train'][0].shape[1:])
output_shape = list(data['train'][1].shape[1:])
batch_size = 128
epochs = 250
nn_params = {
'H_dim': 512,
'H_activation': 'tanh',
'dropout': 0.5,
}
# Retrieve model config
configs = load_NN_configs(filename='lstm.yaml',
input_shape=input_shape,
output_shape=output_shape,
params=nn_params)
# Construct & compile the model
model = assemble('LSTM', configs['1H'])
model.compile(optimizer=Adam(1e-4), loss='mse')
# Callbacks
#callbacks = [EarlyStopping(monitor='val_nlml', patience=10)]
callbacks = []
# Train the model
history = train(model,
data,
callbacks=callbacks,
checkpoint='lstm',
checkpoint_monitor='val_loss',
epochs=epochs,
batch_size=batch_size,
verbose=1)
# Test the model
X_test, y_test = data['test']
y_preds = model.predict(X_test)
rmse_predict = RMSE(y_test, y_preds)
print('Test RMSE:', rmse_predict)
def main():
# Load data
X, y = load_data('actuator', use_targets=False)
X_seq, y_seq = data_to_seq(X,
y,
t_lag=32,
t_future_shift=1,
t_future_steps=1,
t_sw_step=1)
# Split
train_end = int((45. / 100.) * len(X_seq))
test_end = int((90. / 100.) * len(X_seq))
X_train, y_train = X_seq[:train_end], y_seq[:train_end]
X_test, y_test = X_seq[train_end:test_end], y_seq[train_end:test_end]
X_valid, y_valid = X_seq[test_end:], y_seq[test_end:]
data = {
'train': [X_train, y_train],
'valid': [X_valid, y_valid],
'test': [X_test, y_test],
}
# Model & training parameters
input_shape = list(data['train'][0].shape[1:])
output_shape = list(data['train'][1].shape[1:])
batch_size = 16
epochs = 100
nn_params = {
'H_dim': 32,
'H_activation': 'tanh',
'dropout': 0.1,
}
# Retrieve model config
configs = load_NN_configs(filename='lstm.yaml',
input_shape=input_shape,
output_shape=output_shape,
params=nn_params)
# Construct & compile the model
model = assemble('LSTM', configs['1H'])
model.compile(optimizer=Adam(1e-1), loss='mse')
# Callbacks
callbacks = [EarlyStopping(monitor='val_loss', patience=10)]
# Train the model
history = train(model,
data,
callbacks=callbacks,
checkpoint='lstm_actuator',
checkpoint_monitor='val_loss',
epochs=epochs,
batch_size=batch_size,
verbose=2)
# Test the model
X_test, y_test = data['test']
y_preds = model.predict(X_test)
rmse_predict = RMSE(y_test, y_preds)
print('Test RMSE:', rmse_predict)
def main():
# Load data
X, y = load_data('actuator', use_targets=False)
X_seq, y_seq = data_to_seq(X,
y,
t_lag=32,
t_future_shift=1,
t_future_steps=1,
t_sw_step=1)
# Split
train_end = int((45. / 100.) * len(X_seq))
test_end = int((90. / 100.) * len(X_seq))
X_train, y_train = X_seq[:train_end], y_seq[:train_end]
X_test, y_test = X_seq[train_end:test_end], y_seq[train_end:test_end]
X_valid, y_valid = X_seq[test_end:], y_seq[test_end:]
data = {
'train': [X_train, y_train],
'valid': [X_valid, y_valid],
'test': [X_test, y_test],
}
# Re-format targets
for set_name in data:
y = data[set_name][1]
y = y.reshape((-1, 1, np.prod(y.shape[1:])))
data[set_name][1] = [y[:, :, i] for i in xrange(y.shape[2])]
# Model & training parameters
nb_train_samples = data['train'][0].shape[0]
input_shape = data['train'][0].shape[1:]
nb_outputs = len(data['train'][1])
gp_input_shape = (1, )
batch_size = 128
epochs = 5
nn_params = {
'H_dim': 16,
'H_activation': 'tanh',
'dropout': 0.1,
}
gp_params = {
'cov': 'SEiso',
'hyp_lik': -2.0,
'hyp_cov': [[-0.7], [0.0]],
'opt': {
'cg_maxit': 500,
'cg_tol': 1e-4
},
'grid_kwargs': {
'eq': 1,
'k': 1e2
},
'update_grid': False, # when using manual grid, turn off grid updates
}
# Retrieve model config
nn_configs = load_NN_configs(filename='lstm.yaml',
input_shape=input_shape,
output_shape=gp_input_shape,
params=nn_params)
gp_configs = load_GP_configs(filename='gp.yaml',
nb_outputs=nb_outputs,
batch_size=batch_size,
nb_train_samples=nb_train_samples,
params=gp_params)
# Specify manual grid for MSGP (100 equidistant points per input dimension).
# Note: each np.ndarray in the xg must be a column vector.
gp_configs['MSGP']['config']['grid_kwargs']['xg'] = (
gp_input_shape[0] * [np.linspace(-1.0, 1.0, 100)[:, None]])
# Construct & compile the model
model = assemble('GP-LSTM', [nn_configs['1H'], gp_configs['MSGP']])
loss = [gen_gp_loss(gp) for gp in model.output_layers]
model.compile(optimizer=Adam(1e-2), loss=loss)
# Callbacks
callbacks = [EarlyStopping(monitor='val_mse', patience=10)]
# Train the model
history = train(model,
data,
callbacks=callbacks,
gp_n_iter=5,
checkpoint='lstm',
checkpoint_monitor='val_mse',
epochs=epochs,
batch_size=batch_size,
verbose=2)
# Finetune the model
model.finetune(*data['train'],
batch_size=batch_size,
gp_n_iter=100,
verbose=0)
# Test the model
X_test, y_test = data['test']
y_preds = model.predict(X_test)
rmse_predict = RMSE(y_test, y_preds)
print('Test predict RMSE:', rmse_predict)