Y_train = RNN_Demo.predict(train_input)
Y_train = Y_train[0, :, 0]
# get test_result
Y_pred = RNN_Demo.predict(test_input)
Y_pred = Y_pred[0, :, 0]
# Y_target = test_target
# get prediction loss
MSE_loss = nn.MSELoss()
Y_pred_torch = Variable(torch.from_numpy(Y_pred).float(),
requires_grad=False)
Y_target_torch = test_target
MSE_pred = MSE_loss(Y_pred_torch, Y_target_torch)
MSE_pred = MSE_pred.data.numpy()
# # print forecast
# for i in range(len(test)):
# print('Predicted=%f, Expected=%f' % ( y_pred[i], raw_values[-len(test)+i]))
plot_fig_name = './Results/'+Cell + '_L' + \
str(Num_layers) + '_H' + str(Hidden_size) + \
'_I' + str(Num_iters)+'_'+Optim_method
plot_regression_result(Train_target=train_target_plot,
Test_target=test_target_plot,
Train_pred=Y_train,
Test_pred=Y_pred,
Loss_pred=MSE_pred,
Fig_name=plot_fig_name)
def RNN_Benchmark(input_dim, Hidden_size, output_dim, Num_layers, RNN_Cell,
Num_iters, Optim_method, Learning_rate, Print_interval,
Plot_interval, train_input, train_target, validate_input,
validate_target, test_input, test_target):
RNN_Demo = RNNModel(input_dim=1,
hidden_size=Hidden_size,
output_dim=1,
num_layers=Num_layers,
cell=RNN_Cell,
num_iters=Num_iters,
optim_method=Optim_method,
learning_rate=Learning_rate,
print_interval=Print_interval,
plot_interval=Plot_interval).cuda()
# ========================================================================================
Save_Road = './Results/DS1'
RNN_Demo.fit_validate(train_input,
train_target,
validate_input,
validate_target,
save_road=Save_Road)
# RNN_Demo.fit(train_input, train_target,View_interval)
# save the model
# model_save_road='./Model/Model' + '_L' + str(Num_layers) + '_H' + str(Hidden_size) + '_I' + str(Num_iters)+Optim_method+'.pkl'
# torch.save(RNN_Demo,model_save_road)
# RNN_Demo=Model_ViewList[0]
# Train_ViewList = Model_ViewList[1]
# ---------------------------------------------------------------------------------------
# begin to forcast
print('\n------------------------------------------------')
print('Forecasting Testing Data')
print('------------------------------------------------')
Y_train = RNN_Demo.predict(train_input)
Y_train = Y_train[0, :, 0]
# get test_result
Y_pred = RNN_Demo.predict(test_input)
Y_pred = Y_pred[0, :, 0]
# Y_target = test_target
# get prediction loss
MSE_loss = nn.MSELoss()
Y_pred_torch = torch.from_numpy(Y_pred).float()
Y_target_torch = test_target
MSE_pred = MSE_loss(Y_pred_torch, Y_target_torch)
MSE_pred = MSE_pred.data.numpy()
RMSE_pred = np.sqrt(MSE_pred)
# # print forecast
# for i in range(len(test)):
# print('Predicted=%f, Expected=%f' % ( y_pred[i], raw_values[-len(test)+i]))
plot_fig_name = Save_Road+'_Pred_'+RNN_Cell + '_L' + \
str(Num_layers) + '_H' + str(Hidden_size) + \
'_E' + str(Num_iters)+'_'+Optim_method
train_target_plot = train_target.data.numpy()[0, :, 0]
test_target_plot = test_target.data.numpy()[0, :, 0]
plot_regression_result(Train_target=train_target_plot,
Test_target=test_target_plot,
Train_pred=Y_train,
Test_pred=Y_pred,
Loss_pred=RMSE_pred,
Fig_name=plot_fig_name)