I’ve initialized two identical ANN with PyTorch (both as structure and initial parameters), and I’ve noticed that the hyperparameters setting with Ray Tune, returns different results for the two ANN, even if I didn’t have any random initialization.
Someone could explain what I’m doing wrong? I’ll attach the code:
- ANN Initialization:
class Featrues_model(nn.Module):
def __init__(self, n_inputs, dim_hidden, n_outputs):
super().__init__()
self.fc1 = nn.Linear(n_inputs, dim_hidden)
self.fc2 = nn.Linear(dim_hidden, n_outputs)
def forward(self, X):
X = self.fc1(X)
X = self.fc2(X)
return X
features_model_v1 = Featrues_model(len(list_input_variables),5,6)
features_model_v2 = Featrues_model(len(list_input_variables),5,6)
params1 = features_model_v1.named_parameters()
params2 = featrues_model_v2.named_parameters()
dict_params2 = dict(params2)
for name1, param1 in params1:
if name1 in dict_params2:
dict_params2[name1].data.copy_(param1.data)
- Hyperpamameters setting
config = {
"lr": tune.choice([1e-2, 1e-5]),
"weight_decay": tune.choice([1e-2, 1e-5]),
"batch_size": tune.choice([16,64]),
"epochs": tune.choice([10,50])
}
- Train & Validation Dataframe
trainset = df_final.copy()
test_abs = int(len(trainset) * 0.8)
train_subset, val_subset = random_split(
trainset, [test_abs, len(trainset) - test_abs]
)
df_train = df_final.iloc[train_subset.indices]
df_val = df_final.iloc[val_subset.indices]
- Train function design
def setting_model(config, df_train, df_val, model):
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=config["lr"], weight_decay=config["weight_decay"])
BATCH_SIZE = config["batch_size"]
for epoch in range(config["epochs"]):
train_epoch_loss = 0
train_epoch_acc = 0
step = 0
for i in tqdm(range(0, df_train.shape[0], BATCH_SIZE)):
batch_X = np.array(
df_train[list_input_variables].iloc[i:i+BATCH_SIZE]
)
batch_X = torch.Tensor([x for x in batch_X])
batch_Y = np.array(
df_train[list_output_variables].iloc[i:i+BATCH_SIZE]
)
batch_Y = torch.Tensor([int(y) for y in batch_Y])
batch_Y = batch_Y.type(torch.int64)
optimizer.zero_grad()
outputs = model.forward(batch_X)
train_loss = criterion(outputs, batch_Y)
train_acc = multi_acc(outputs, batch_Y)
train_loss.backward()
optimizer.step()
train_epoch_loss += train_loss.item()
train_epoch_acc += train_acc.item()
step += 1
# print statistics
print(f"Epochs: {epoch}")
print(f"Train Loss: {train_epoch_loss/len(df_train)}")
print(f"Train Acc: {train_epoch_acc/step}")
print("\n")
# Validation loss
with torch.no_grad():
X_val = np.array(
df_val[list_input_variables]
)
X_val = torch.Tensor([x for x in X_val])
Y_val = np.array(
df_val[list_output_variables]
)
Y_val = torch.Tensor([int(y) for y in Y_val])
Y_val = Y_val.type(torch.int64)
outputs = model.forward(X_val)
_, predicted = torch.max(outputs.data, 1)
total = Y_val.size(0)
correct = (predicted == Y_val).sum().item()
loss = criterion(outputs, Y_val)
tune.report(loss=(loss.numpy()), accuracy=correct / total)
print(f"Validation Loss: {loss.numpy()/len(df_val)}")
print(f"Validation Acc: {correct / total:.3f}")
print("Finished Training")
- Hyperparameters Tune
result_v1 = tune.run(
partial(setting_model, df_train=df_train, df_val=df_val, model=features_model_v1),
config=config,
fail_fast="raise",
)
result_v2 = tune.run(
partial(setting_model, df_train=df_train, df_val=df_val, model=features_model_v2),
config=config,
fail_fast="raise"
)
- Output
result_v1.get_best_config()
{'lr': 1e-05, 'weight_decay': 1e-05, 'epochs': 1}
result_v2.get_best_config()
{'lr': 0.01, 'weight_decay': 1e-05, 'epochs': 1}