x_test_list.append(x_test[i].flatten().astype(np.float))
# normalize dataset
x_train_list, mi, ma = util.normalize_dataset(x_train_list)
x_test_list, mi, ma = util.normalize_dataset(x_test_list)
# Get time before training
t_start = datetime.datetime.now()
print("Starting timer")
# Initialize network and train
model_torch = PcTorch(NETWORK_ARCHITECTURE)
model_torch.train(x_train_list,
y_train_list,
x_test_list,
y_test_list,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
max_it=INFERENCE_STEPS,
optmizer=OPTIMIZER,
activation=ACTIVATION,
dataset_perc=DATA_PERC,
learning_rate=LR)
# Get time after training
t_end = datetime.datetime.now()
elapsedTime = (t_end - t_start)
dt_sec = elapsedTime.total_seconds()
print(f"Training time per epoch: {dt_sec/EPOCHS}")
x_train_list.append(x_train[i].flatten().astype(np.float))
for i in range(len(x_valid)):
x_valid_list.append(x_valid[i].flatten().astype(np.float))
# Get time before training
t_start = datetime.datetime.now()
print("Starting timer")
# Initialize network and train
model_torch = PcTorch(NETWORK_ARCHITECTURE)
model_torch.train(
x_train_list,
y_train_list,
x_valid_list,
y_valid_list,
batch_size=BATCH_SIZE,
epochs=EPOCHS,
max_it=INFERENCE_STEPS,
optimizer=OPTIMIZER,
activation=ACTIVATION,
dataset_perc = DATA_PERC
)
# Get time after training
t_end = datetime.datetime.now()
elapsedTime = (t_end - t_start )
dt_sec = elapsedTime.total_seconds()
print(f"Training time per epoch: {dt_sec/EPOCHS}")
