def testRun():
# MNIST dataset
train_dataset = torchvision.datasets.MNIST(root='/data',
train=True,
transform=transforms.ToTensor(),
download=True)
cv_splits = 3
kfold = KFold(n_splits=cv_splits, shuffle=True, random_state=0)
for fold_idx, (train_idx,
valid_idx) in enumerate(kfold.split(train_dataset)):
print(">> CV fold step ", str(fold_idx))
# cnn model
model = mlp.SimpleMLP(output_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Data loader
train_loader = DataLoader(Subset(train_dataset, train_idx),
batch_size=batch_size,
shuffle=True)
# Training
mnist_trainer = trainer.MLPTrainer(train_loader,
model=model,
cri=criterion,
opt=optimizer,
device=device)
train_result = mnist_trainer.Execute(epochs)
def RunTorchCV():
with mlflow.start_run():
# MNIST dataset
train_dataset = torchvision.datasets.MNIST(
root='/data',
train=True,
transform=transforms.ToTensor(),
download=True)
train_results = {}
valid_results = {}
cv_splits = 3
kfold = KFold(n_splits=cv_splits, shuffle=True, random_state=0)
for fold_idx, (train_idx,
valid_idx) in enumerate(kfold.split(train_dataset)):
print(">> CV fold step ", str(fold_idx))
# cnn model
model = mlp.SimpleMLP(output_classes).to(device)
# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
# Data loader
train_loader = DataLoader(Subset(train_dataset, train_idx),
batch_size=batch_size,
shuffle=True)
valid_loader = DataLoader(Subset(train_dataset, valid_idx),
batch_size=batch_size,
shuffle=False)
# Training
mnist_trainer = trainer.MLPTrainer(train_loader,
model=model,
cri=criterion,
opt=optimizer,
device=device)
train_result = mnist_trainer.Execute(epochs)
trained_model = mnist_trainer.GetModel()
train_results[fold_idx] = train_result
# Validation
mnist_validator = validator.MLPValidator(valid_loader,
model=trained_model,
criterion=criterion,
device=device)
valid_result = mnist_validator.Validate()
valid_results[fold_idx] = valid_result
mlflow.log_param("method_name",
mlp.SimpleMLP(output_classes).__class__.__name__)
mlflow.log_param("output_class", output_classes)
mlflow.log_param("batch_size", batch_size)
mlflow.log_param("learning_rate", learning_rate)
mlflow.log_param("fold_type", kfold.__class__.__name__)
mlflow.log_param("n_splits", cv_splits)
mlflow.log_param("random_state", 0)
mlflow.log_param("criterion", nn.CrossEntropyLoss.__class__.__name__)
mlflow.log_param("optimizer", torch.optim.Adam.__name__)
average_loss = 0
average_acc = 0
for fold_idx, cv_result in train_results.items():
loss = cv_result[cv_splits - 1]["loss"]
acc = cv_result[cv_splits - 1]["accuracy"]
average_loss += loss
average_acc += acc
mlflow.log_metric("fold_" + str(fold_idx) + "_loss", loss)
mlflow.log_metric("fold_" + str(fold_idx) + "_accuracy", acc)
average_loss = average_loss / cv_splits
average_acc = average_acc / cv_splits
mlflow.log_metric("average_loss", average_loss)
mlflow.log_metric("average_acc", average_acc)
return valid_results