def objective(params):
optimize_model = build_lstm_v1.lstm_model_110(
params, train_data2.shape[2],
(std_inv / std_inv2)) #Check code here, relu entering
loss_out = NNFunctions.model_optimizer_101(optimize_model, train_data2,
H_t2, val_data2, H_val2, 5)
return {'loss': loss_out, 'status': STATUS_OK}
def objective(params):
optimize_model = build_lstm_v1.lstm_model_110(params, train_data.shape[2], 24)
# for epochs in range(5):
for ep in range(20):
optimize_history = optimize_model.fit(train_data, H_t, batch_size=1, nb_epoch=1,
validation_data=(val_data, H_val), shuffle=False)
optimize_model.reset_states()
loss_v = optimize_history.history['val_loss']
print loss_v
loss_out = loss_v[-1]
return {'loss': loss_out, 'status': STATUS_OK}
loss_out = loss_v[-1]
return {'loss': loss_out, 'status': STATUS_OK}
trials = Trials()
best = fmin(objective, space, algo=tpe.suggest, trials=trials, max_evals=20)
#Building Stateful Model
lstm_hidden = hyperopt.space_eval(space, best)
print lstm_hidden
tsteps = 24
out_dim = 24
lstm_model = build_lstm_v1.lstm_model_110(lstm_hidden, train_data.shape[2],
tsteps)
save_model = lstm_model
##callbacks for Early Stopping
callbacks = [EarlyStopping(monitor='val_loss', patience=3)]
#parameters for simulation
attempt_max = 5
epoch_max = 200
min_epoch = 20
#Criterion for early stopping
tau = 10
e_mat = numpy.zeros((epoch_max, attempt_max))
e_temp = numpy.zeros((tau, ))
def objective(params):
#optimize_model = build_lstm_v1.lstm_multi_101(params, train_data2.shape[2], lstm_h1, 60) #Check code here, relu entering
optimize_model = build_lstm_v1.lstm_model_110(params, train_data2.shape[2], 60)
loss_out = NNFunctions.model_optimizer_101(optimize_model, train_data2, H_t2, val_data2, H_val2, 6)
return {'loss': loss_out, 'status': STATUS_OK}
def objective(params):
optimize_model = build_lstm_v1.lstm_model_110(params, train_data.shape[2], 24)
loss_out = NNFunctions.model_optimizer_101(optimize_model, train_data, H_t, val_data, H_val, 20)
return {'loss': loss_out, 'status': STATUS_OK}
