def build_lstmgpt(input_shape, gp_input_shape, nb_outputs, batch_size,
nb_train_samples):
nn_params = {
'H_dim': 16,
'H_activation': 'tanh',
'dropout': 0.1,
}
gp_params = {
'cov': 'RQiso',
'hyp_lik': -1.0,
'hyp_cov': [[1.], [1.], [0.0]],
'opt': {},
}
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['2H'], gp_configs['GP']])
loss = [gen_gp_loss(gp) for gp in model.output_gp_layers]
model.compile(optimizer=Adam(1e-2), loss=loss)
return model
def GPLSTM(shift, lr, batch_size, data):
'''
Parameters
----------
shift : Integer
Shift/steps into the future of predicted value.
lr : Float
Learning Rate for model training.
batch_size : Integer
Batch size for training.
data : Dictionnary
Dictionnary containing the training, test and validation data of the model.
Returns
-------
model : Optimized Model
Returns the optimized deep learning model.
'''
# 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 = batch_size
nn_params = {
'H_dim': 4,
'H_activation': 'tanh',
'dropout': 0.0,
}
gp_params = {
'cov': 'SEiso',
'hyp_lik': np.log(0.1),
'hyp_cov': [[1.0], [1.0]],
}
# 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']]) #MSGP
loss = [gen_gp_loss(gp) for gp in model.output_layers]
model.compile(optimizer=Adam(lr), loss=loss)
return model
def ini(self):
self.batch_size = 128
self.epochs = 20
self.nb_train_samples = self.X_train.shape[0]
self.input_shape = self.X_train.shape[1:]
self.nb_outputs = len(self.y_train)
self.gp_input_shape = (1, )
self.nn_params = {
'H_dim': 16,
'H_activation': 'tanh',
'dropout': 0.1,
}
if self.k_type == "RBF":
self.gp_params = {
'cov': 'SEiso',
'hyp_lik': -1.0,
'hyp_cov': [[1.], [0.0]],
'opt': {},
}
elif self.k_type == "RQ":
self.gp_params = {
'cov': 'RQiso',
'hyp_lik': -1.0,
'hyp_cov': [[1.], [1.], [0.0]],
'opt': {},
}
pass
self.nn_configs = load_NN_configs(filename='lstm.yaml',
input_shape=self.input_shape,
output_shape=self.gp_input_shape,
params=self.nn_params)
self.gp_configs = load_GP_configs(
filename='gp.yaml',
nb_outputs=self.nb_outputs,
batch_size=self.batch_size,
nb_train_samples=self.nb_train_samples,
params=self.gp_params)
return
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():
# Load data
X_train, y_train = load_data('actuator', stop=45.)
X_valid, y_valid = load_data('actuator', start=45., stop=55.)
X_test, y_test = load_data('actuator', start=55.)
data = {
'train': (X_train, y_train),
'valid': (X_valid, y_valid),
'test': (X_test, y_test),
}
data = preprocess_data(data,
standardize=True,
multiple_outputs=True,
t_lag=10,
t_future_shift=1,
t_future_steps=1,
t_sw_step=1)
# 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
nb_epoch = 5
# Retrieve model config
nn_configs = load_NN_configs(filename='lstm.yaml',
input_shape=input_shape,
output_shape=gp_input_shape,
H_dim=16,
H_activation='tanh',
dropout=0.1)
gp_configs = load_GP_configs(filename='gp.yaml',
nb_outputs=nb_outputs,
batch_size=batch_size,
nb_train_samples=nb_train_samples,
cov='SEiso',
hyp_lik=-2.0,
hyp_cov=[[-0.7], [0.0]])
# Construct & compile the model
model = assemble('GP-LSTM', [nn_configs['1H'], gp_configs['GP']])
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',
nb_epoch=nb_epoch,
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 build_train_GPLSTM(self):
'''
Define GP-LSTM Architecture and Training.
Returns
-------
history : Dictionnary
Training Information.
y_test : Numpy Array
Input Test Data.
y_pred : Numpy Array
Predicted output.
var : Numpy Array
Predicted Variances.
rmse_predict : Float
Training Metrics.
model : Optimized Model
Optimized Deep Learning Model after Training.
data : Dictionnary
Train, test and validation sets.
'''
data=self.data
# 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,)
nn_params = {
'H_dim': self.hdim,
'H_activation': 'tanh',
'dropout': 0.0,
}
gp_params = {
'cov': 'SEiso',
'hyp_lik': np.log(0.3),
'hyp_cov': [[4.0], [0.1]],
'opt': {'cg_maxit': 2000,'cg_tol': 1e-4,#'deg':3,
'pred_var':-100,
},
}
# 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=self.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_layers]
model.compile(optimizer=Adam(1e-5), loss=loss)
# Callbacks
callbacks = [EarlyStopping(monitor='val_mse', patience=2000)]
# Train the model
history = train(model, data, callbacks=callbacks, gp_n_iter=5,
checkpoint='checkpL3_predmode_'+str(pred_mode)+'_test_'+str(test), checkpoint_monitor='val_mse',
epochs=self.epochs, batch_size=self.batch_size, verbose=1)
# Finetune the model
model.finetune(*data['train'],
batch_size=self.batch_size,
gp_n_iter=100,
verbose=0)
# Test the model
X_test, y_test = data['test']
X_train,y_train=data['train']
y_pred,var = model.predict(X_test,return_var=True, X_tr=X_train, Y_tr=y_train,batch_size=self.batch_size)
var=np.array(var)
rmse_predict = RMSE(y_test, y_pred)
print('Test predict RMSE:', rmse_predict)
print('mean variance:', var.mean())
return history,y_test,y_pred,var,rmse_predict,model,data
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)