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("mdoels/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}"')