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| Topics Covered | Turnaround Time | Price (USD) |
|---|---|---|
| PyTorch Basics | 1 days | $50 |
| Neural Network Implementation | 5 days | $80 |
| Transfer Learning | 7 days | $100 |
| Model Training and Evaluation | 4 days | $70 |
| Computer Vision with PyTorch | 6 days | $90 |
| Natural Language Processing with PyTorch | 8 days | $110 |
| Hyperparameter Tuning and Optimization | 5 days | $85 |
- PyTorch Assignment Help
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- Creation of Simple Neural Network for Classifying the MNIST Dataset
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Creation of Simple Neural Network for Classifying the MNIST Dataset
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
# Define a simple neural network
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.fc1 = nn.Linear(28*28, 10) # Input layer to output layer
def forward(self, x):
x = x.view(-1, 28*28) # Flatten the input
x = self.fc1(x) # Forward pass
return x
# Load the MNIST dataset
transform = transforms.Compose([transforms.ToTensor()])
train_loader = torch.utils.data.DataLoader(datasets.MNIST('.', train=True, download=True, transform=transform), batch_size=64, shuffle=True)
# Initialize network, loss, and optimizer
model = SimpleNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)
# Training loop (1 epoch for simplicity)
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
print("Training complete!")
Explanation:
- Network Architecture:
- SimpleNN Class: Contains a single fully connected layer (fc1) that maps 784 features (flattened 28x28 images) to 10 output classes.
- Data Loading:
- MNIST Dataset: Uses transforms.ToTensor() to convert images to tensors and loads them in batches.
- Training Process:
- Training Loop: Iterates over the dataset once (1 epoch), performs a forward pass, calculates the loss, and updates the model weights.
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