model = model_from_json(loaded_model_json)
model.load_weights("model_cnn_bilstm.h5")
eval_obj = Evaluation()
rmse_list = []
mae_list = []
for i in np.arange(0.0, 1.0, 0.1):
x_test_input, labels_test_ = p_obj.augment_rand(X_test_,
l_percent=i,
u_percent=i + 0.02)
#print(i, np.sum(1-labels_test_aug)/(labels_test_aug.shape[0]*labels_test_aug.shape[1]*labels_test_aug.shape[2]))
p_train_ave, p_test_ave = eval_obj.generates_predictions(
x_train=X_train_,
x_test=x_test_input,
scaler_alldata=scaler_alldata,
model=model,
train=False,
test=True)
rmse_, mae_ = eval_obj.evaluations(X_train_nrescaled,
X_test_nrescaled,
p_train_ave,
p_test_ave,
labels_train_aug,
labels_test_,
p_obj.num_sensors,
train=False,
test=True)
rmse_list += [rmse_]
mae_list += [mae_]
eval_obj.plot_(rmse_list, mae_list)
