def objective(params):
optimize_model = build_lstm_v1.lstm_multi_104b(params, train_data2.shape[2], lstm_h1, H_t2.shape[2], (std_inv/std_inv2)) #Check code here, relu entering
loss_out = NNFunctions.model_optimizer_101(optimize_model, [train_data2, h_input], H_t2, [val_data2, h_val], H_val2, 5)
return {'loss': loss_out, 'status': STATUS_OK}
def objective(params):
optimize_model = build_lstm_v1.lstm_multi_104b(params, train_data2.shape[2], lstm_h1, H_t2.shape[2], (std_inv/std_inv2)) #Check code here, relu entering
loss_out = NNFunctions.model_optimizer_101(optimize_model, [train_data2, h_input], H_t2, [val_data2, h_val], H_val2, 5)
return {'loss': loss_out, 'status': STATUS_OK}
trials = Trials()
best2 = fmin(objective, space2, algo=tpe.suggest, trials=trials, max_evals=10)
#Building Stateful Model
lstm_hidden = hyperopt.space_eval(space2, best2)
print lstm_hidden
tsteps2 = std_inv/std_inv2
#Building model
lstm_model = build_lstm_v1.lstm_multi_104b(lstm_hidden, train_data2.shape[2], lstm_h1, H_t2.shape[2], tsteps2)
save_model = lstm_model
##callbacks for Early Stopping
callbacks = [EarlyStopping(monitor='val_loss', patience=5)]
#parameters for simulation
attempt_max = 5
epoch_max = 100
min_epoch = 10
#Criterion for early stopping
tau = 5
e_mat = numpy.zeros((epoch_max, attempt_max))
e_temp = numpy.zeros((tau, ))