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Move script into main fn

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This commit is contained in:
Marcin Kostrzewski 2022-05-05 22:33:34 +02:00
parent 46d7831b98
commit fe63ef269c
1 changed files with 31 additions and 25 deletions
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56
train_model.py
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@ -12,6 +12,9 @@ default_batch_size = 64
default_epochs = 5
device = "cuda" if torch.cuda.is_available() else "cpu"
def hour_to_int(text: str):
return float(text.replace(':', ''))
@ -86,7 +89,7 @@ def test(dataloader, model, loss_fn):
pred = model(X)
test_loss += loss_fn(pred, y).item()
test_loss /= num_batches
print(f"Avg loss: {test_loss:>8f} \n")
print(f"Avg loss (using {loss_fn}): {test_loss:>8f} \n")
def setup_args():
@ -97,35 +100,38 @@ def setup_args():
return args_parser.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using {device} device")
def main():
print(f"Using {device} device")
args = setup_args()
args = setup_args()
batch_size = args.batchSize
batch_size = args.batchSize
plant_test = PlantsDataset('data/Plant_1_Generation_Data.csv.test')
plant_train = PlantsDataset('data/Plant_1_Generation_Data.csv.train')
plant_test = PlantsDataset('data/Plant_1_Generation_Data.csv.test')
plant_train = PlantsDataset('data/Plant_1_Generation_Data.csv.train')
train_dataloader = DataLoader(plant_train, batch_size=batch_size)
test_dataloader = DataLoader(plant_test, batch_size=batch_size)
train_dataloader = DataLoader(plant_train, batch_size=batch_size)
test_dataloader = DataLoader(plant_test, batch_size=batch_size)
for i, (data, labels) in enumerate(train_dataloader):
print(data.shape, labels.shape)
print(data, labels)
break
for i, (data, labels) in enumerate(train_dataloader):
print(data.shape, labels.shape)
print(data, labels)
break
model = MLP()
print(model)
model = MLP()
print(model)
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
epochs = args.epochs
for t in range(epochs):
print(f"Epoch {t + 1}\n-------------------------------")
train(train_dataloader, model, loss_fn, optimizer)
test(test_dataloader, model, loss_fn)
print("Done!")
loss_fn = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
epochs = args.epochs
for t in range(epochs):
print(f"Epoch {t + 1}\n-------------------------------")
train(train_dataloader, model, loss_fn, optimizer)
test(test_dataloader, model, loss_fn)
print("Done!")
torch.save(model.state_dict(), './model_out')
print("Model saved in ./model_out file.")
torch.save(model.state_dict(), './model_out')
print("Model saved in ./model_out file.")
if __name__ == "__main__":
main()