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}