137 lines
3.5 KiB
Python
137 lines
3.5 KiB
Python
import argparse
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import numpy as np
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import pandas as pd
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import torch
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from torch import nn
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from torch.utils.data import DataLoader, Dataset
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default_batch_size = 64
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default_epochs = 5
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device = "cuda" if torch.cuda.is_available() else "cpu"
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def hour_to_int(text: str):
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return float(text.replace(':', ''))
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def int_to_hour(num: int):
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return str(num)
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class PlantsDataset(Dataset):
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def __init__(self, file_name):
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df = pd.read_csv(file_name)
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x = np.array([x[0].split(' ')[1] for x in df.iloc[:, 0:1].values])
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y = df.iloc[:, 3].values
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x_processed = np.array([hour_to_int(h) for h in x], dtype='float32')
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self.x_train = torch.from_numpy(x_processed)
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self.y_train = torch.from_numpy(y)
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self.x_train.type(torch.LongTensor)
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def __len__(self):
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return len(self.y_train)
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def __getitem__(self, idx):
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return self.x_train[idx].float(), self.y_train[idx].float()
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class MLP(nn.Module):
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def __init__(self):
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super().__init__()
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self.layers = nn.Sequential(
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nn.Linear(1, 64),
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nn.ReLU(),
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nn.Linear(64, 32),
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nn.ReLU(),
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nn.Linear(32, 1),
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)
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def forward(self, x):
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x = x.view(x.size(0), -1)
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return self.layers(x)
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def train(dataloader, model, loss_fn, optimizer):
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size = len(dataloader.dataset)
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model.train()
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for batch, (X, y) in enumerate(dataloader):
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X, y = X.to(device), y.to(device)
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# Compute prediction error
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pred = model(X)
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loss = loss_fn(pred, y)
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# Backpropagation
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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if batch % 100 == 0:
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loss, current = loss.item(), batch * len(X)
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print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
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def test(dataloader, model, loss_fn):
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num_batches = len(dataloader)
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model.eval()
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test_loss, correct = 0, 0
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with torch.no_grad():
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for X, y in dataloader:
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X, y = X.to(device), y.to(device)
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pred = model(X)
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test_loss += loss_fn(pred, y).item()
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test_loss /= num_batches
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print(f"Avg loss (using {loss_fn}): {test_loss:>8f} \n")
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return test_loss
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def setup_args():
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args_parser = argparse.ArgumentParser(prefix_chars='-')
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args_parser.add_argument('-b', '--batchSize', type=int, default=default_batch_size)
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args_parser.add_argument('-e', '--epochs', type=int, default=default_epochs)
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return args_parser.parse_args()
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def main():
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print(f"Using {device} device")
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args = setup_args()
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batch_size = args.batchSize
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plant_test = PlantsDataset('data/Plant_1_Generation_Data.csv.test')
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plant_train = PlantsDataset('data/Plant_1_Generation_Data.csv.train')
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train_dataloader = DataLoader(plant_train, batch_size=batch_size)
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test_dataloader = DataLoader(plant_test, batch_size=batch_size)
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for i, (data, labels) in enumerate(train_dataloader):
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print(data.shape, labels.shape)
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print(data, labels)
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break
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model = MLP()
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print(model)
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loss_fn = nn.MSELoss()
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optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
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epochs = args.epochs
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for t in range(epochs):
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print(f"Epoch {t + 1}\n-------------------------------")
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train(train_dataloader, model, loss_fn, optimizer)
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test(test_dataloader, model, loss_fn)
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print("Done!")
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torch.save(model.state_dict(), './model_out')
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print("Model saved in ./model_out file.")
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if __name__ == "__main__":
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main()
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