Updated 2 years ago
~ Evan Marie Carr
www.EvanMarie.com
Steps and Topics:
- MNIST Set
- Dataloader, Transformation
- Multilayer Neural Network
- Activation Function
- Loss and Optimizer
- Training Loop with Batches
- Model Evaluation
- GPU Support
import torch
import torch.nn as nn
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
%matplotlib inline
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
Getting the Data:
batch_size = 100
train_dataset = torchvision.datasets.MNIST(root="./data", train=True,
transform=transforms.ToTensor(),
download=True)
test_dataset = torchvision.datasets.MNIST(root="./data", train=False,
transform=transforms.ToTensor())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
batch_size=batch_size,
shuffle=False)
Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz
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Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz
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