132 lines
4.8 KiB
Python
132 lines
4.8 KiB
Python
import torch
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import numpy as np
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import torch.nn as nn
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import torch.optim as optim
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from torch.utils.data import Dataset, TensorDataset, DataLoader
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import argparse
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from sacred import Experiment
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from sacred.observers import MongoObserver, FileStorageObserver
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ex = Experiment("426206", interactive=False, save_git_info=False)
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ex.observers.append(FileStorageObserver('my_runs'))
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
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class LayerLinearRegression(nn.Module):
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def __init__(self):
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super().__init__()
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# Instead of our custom parameters, we use a Linear layer with single input and single output
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self.linear = nn.Linear(1, 1)
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def forward(self, x):
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# Now it only takes a call to the layer to make predictions
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return self.linear(x)
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# parser = argparse.ArgumentParser(description='Program do uczenia modelu')
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# parser.add_argument('-l', '--lr', type=float, default=1e-3, help="Współczynik uczenia (lr)", required=False)
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# parser.add_argument('-e', '--epochs', type=int, default=100, help="Liczba epok", required=False)
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# args = parser.parse_args()
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#python3 dlgssdpytorch.py with lr=0.01 n_epochs=10
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@ex.config
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def my_config():
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lr = 1e-3
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n_epochs = 100
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@ex.capture
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def train(lr, n_epochs, _run):
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train_dataset = torch.load('train_dataset.pt')
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#val_dataset = torch.load('val_dataset.pt')
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train_loader = DataLoader(dataset=train_dataset)
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#val_loader = DataLoader(dataset=val_dataset)
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model = LayerLinearRegression()
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# Checks model's parameters
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#print(model.state_dict())
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loss_fn = nn.MSELoss(reduction='mean')
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optimizer = optim.SGD(model.parameters(), lr=lr)
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def make_train_step(model, loss_fn, optimizer):
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# Builds function that performs a step in the train loop
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def train_step(x, y):
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# Sets model to TRAIN mode
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model.train()
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# Makes predictions
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yhat = model(x)
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# Computes loss
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loss = loss_fn(y, yhat)
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# Computes gradients
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loss.backward()
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# Updates parameters and zeroes gradients
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optimizer.step()
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optimizer.zero_grad()
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# Returns the loss
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return loss.item()
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# Returns the function that will be called inside the train loop
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return train_step
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# Creates the train_step function for our model, loss function and optimizer
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train_step = make_train_step(model, loss_fn, optimizer)
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training_losses = []
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validation_losses = []
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#print(model.state_dict())
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# For each epoch...
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for epoch in range(n_epochs):
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_run.log_scalar("Epoch", str(epoch))
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losses = []
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# Uses loader to fetch one mini-batch for training
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for x_batch, y_batch in train_loader:
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# NOW, sends the mini-batch data to the device
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# so it matches location of the MODEL
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# x_batch = x_batch.to(device)
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# y_batch = y_batch.to(device)
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# One stpe of training
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loss = train_step(x_batch, y_batch)
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losses.append(loss)
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training_loss = np.mean(losses)
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training_losses.append(training_loss)
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_run.log_scalar("MSE", str(training_loss))
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# After finishing training steps for all mini-batches,
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# it is time for evaluation!
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# Ewaluacja jest już tutaj nie potrzebna bo odbywa sie w evaluation.py. Można jednak włączyć podgląd ewaluacji dla poszczególnych epok.
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# # We tell PyTorch to NOT use autograd...
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# # Do you remember why?
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# with torch.no_grad():
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# val_losses = []
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# # Uses loader to fetch one mini-batch for validation
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# for x_val, y_val in val_loader:
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# # Again, sends data to same device as model
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# # x_val = x_val.to(device)
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# # y_val = y_val.to(device)
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# model.eval()
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# # Makes predictions
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# yhat = model(x_val)
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# # Computes validation loss
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# val_loss = loss_fn(y_val, yhat)
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# val_losses.append(val_loss.item())
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# validation_loss = np.mean(val_losses)
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# validation_losses.append(validation_loss)
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# print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
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print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t")
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torch.save({
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'model_state_dict': model.state_dict(),
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'optimizer_state_dict': optimizer.state_dict(),
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'loss': lr,
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}, 'model.pt')
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@ex.automain
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def my_main(lr, n_epochs, _run):
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train(lr, n_epochs, _run)
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ex.run()
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ex.add_artifact('model.pt')
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