QuickStart Model Tutorial Finished

Finished up first neural network tutorial with pytorch

.gitattributes

@@ -0,0 +1 @@
+*.pth filter=lfs diff=lfs merge=lfs -text

.gitignore

@@ -0,0 +1,4 @@
+data
+venv
+.vscode
+

README.md

@@ -0,0 +1,15 @@
+# Pytorch Tutorials 
+## Learning about pytorch
+
+![Pytorch logo](https://cdn.icon-icons.com/icons2/2699/PNG/512/pytorch_logo_icon_169823.png)
+
+![Neural network image](https://tikz.net/wp-content/uploads/2021/12/neural_networks-001.png)
+
+
+### Resources
+
+[https://pytorch.org/docs/stable/index.html](https://pytorch.org/docs/stable/index.html)
+
+- **Following tutorials from:**  
+[pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html](https://pytorch.org/tutorials/beginner/basics/quickstart_tutorial.html)
+

quickStart/FashionLabeler.py

@@ -0,0 +1,84 @@
+import torch 
+import random
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# Download training data from open datasets
+training_data = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor(),
+)
+
+# Download test data from open datasets
+test_data = datasets.FashionMNIST(
+    root="data",
+    train=False,
+    download=True,
+    transform=ToTensor(),
+)
+
+batch_size = 64
+
+# Create data loaders.
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+for X, y in test_dataloader:
+    print(f"Shape of X [N, C, H, W]: {X.shape}")
+    print(f"Shape of y: {y.shape} {y.dtype}")
+    break
+
+
+# Get cpu or gpu device for training 
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+print(f"Using {device} device")
+
+# Define model
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512,10)
+        )
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+
+model = NeuralNetwork()
+model.load_state_dict(torch.load("FashionLabeling_model.pth"))
+
+classes = [
+    "T-shirt/top",
+    "Trouser",
+    "Pullover",
+    "Dress",
+    "Coat",
+    "Sandal",
+    "Shirt",
+    "Sneaker",
+    "Bag",
+    "Ankle boot"
+]
+
+
+model.eval()
+# pick a random datapoint and test the model with it.
+data = random.choice(test_data)
+x, y = data[0] , data[1]
+
+with torch.no_grad():
+    pred = model(x)
+    predicted, actual = classes[pred[0].argmax(0)], classes[y]
+    print(f'Predicted: "{predicted}", Actual: "{actual}"')
+

quickStart/TrainFashionLabeler.py

@@ -0,0 +1,105 @@
+import torch 
+from torch import nn
+from torch.utils.data import DataLoader
+from torchvision import datasets
+from torchvision.transforms import ToTensor
+
+# Download training data from open datasets
+training_data = datasets.FashionMNIST(
+    root="data",
+    train=True,
+    download=True,
+    transform=ToTensor(),
+)
+
+# Download test data from open datasets
+test_data = datasets.FashionMNIST(
+    root="data",
+    train=False,
+    download=True,
+    transform=ToTensor(),
+)
+
+batch_size = 64
+
+# Create data loaders.
+train_dataloader = DataLoader(training_data, batch_size=batch_size)
+test_dataloader = DataLoader(test_data, batch_size=batch_size)
+
+for X, y in test_dataloader:
+    print(f"Shape of X [N, C, H, W]: {X.shape}")
+    print(f"Shape of y: {y.shape} {y.dtype}")
+    break
+
+
+# Get cpu or gpu device for training 
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+
+print(f"Using {device} device")
+
+# Define model
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.flatten = nn.Flatten()
+        self.linear_relu_stack = nn.Sequential(
+            nn.Linear(28*28, 512),
+            nn.ReLU(),
+            nn.Linear(512, 512),
+            nn.ReLU(),
+            nn.Linear(512,10)
+        )
+    def forward(self, x):
+        x = self.flatten(x)
+        logits = self.linear_relu_stack(x)
+        return logits
+        
+model = NeuralNetwork().to(device=device)
+print(model)
+loss_fn = nn.CrossEntropyLoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
+
+def train(dataloader, model, loss_fn, optimizer):
+    size = len(dataloader.dataset)
+    model.train()
+    for batch, (X, y) in enumerate(dataloader):
+        X, y = X.to(device), y.to(device)
+
+        # Compute prediction error
+        pred = model(X)
+        loss = loss_fn(pred, y)
+
+        # Backpropegation
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+
+        if batch % 100 == 0:
+            loss, current = loss.item(), (batch + 1) * len(X)
+            print(f"loss: {loss:>7f} [{current:>5d}|{size:>5d}]")
+
+def test(dataloader, model, loss_fn):
+    size = len(dataloader.dataset)
+    num_batches = len(dataloader)
+    model.eval()
+    test_loss, correct = 0,0
+    with torch.no_grad():
+        for X, y in dataloader:
+            X, y = X.to(device), y.to(device)
+            pred = model(X)
+            test_loss += loss_fn(pred, y).item()
+            correct += (pred.argmax(1)== y).type(torch.float).sum().item()
+    test_loss /= num_batches
+    correct /= size
+    print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f}\n")
+
+epochs = 40
+for t in range(epochs):
+    print(f"Epoch {t+1}\n-------------------------------")
+    train(train_dataloader, model, loss_fn=loss_fn, optimizer=optimizer)
+    test(test_dataloader, model, loss_fn=loss_fn)
+print("Done!")
+
+
+torch.save(model.state_dict(), "model.pth")
+print("Saved PyTorch Model State to model.pth")

requirements.txt

@@ -0,0 +1,6 @@
+--index-url https://download.pytorch.org/whl/cu117
+torch
+--index-url https://download.pytorch.org/whl/cu117
+torchvision
+--index-url https://download.pytorch.org/whl/cu117
+torchaudio

working_install_test.py

@@ -0,0 +1,6 @@
+import torch
+
+x = torch.rand(5,3)
+print("Random Tensor data: ", x)
+
+print("Cuda available: ", torch.cuda.is_available())