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