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import anndata
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import numpy as np
# Generate random sample data - 2000 samples, 1280 dimensions each
num_samples = 2000
dimension = 1280
# Generate random input vectors X
X = np.random.randn(num_samples, dimension)
# Generate target vectors Y (could be random or a function of X)
# Option 1: Completely random Y
# Y = np.random.randn(num_samples, dimension)
# Option 2: Y as a noisy function of X (more realistic for regression task)
W = np.random.randn(dimension, dimension) * 0.1 # Random weight matrix
b = np.random.randn(dimension) * 0.1 # Random bias
noise = np.random.randn(num_samples, dimension) * 0.05 # Random noise
Y = X @ W + b + noise # Y = XW + b + noise
# Convert data to PyTorch tensors
X_tensor = torch.tensor(X, dtype=torch.float32)
Y_tensor = torch.tensor(Y, dtype=torch.float32)
dataset = TensorDataset(X_tensor, Y_tensor)
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# Option 1: Simple Linear Regression model
class LinearModel(nn.Module):
def __init__(self):
super(LinearModel, self).__init__()
self.linear = nn.Linear(1280, 1280)
def forward(self, x):
return self.linear(x)
# Option 2: Neural Network with hidden layers
class NeuralNetwork(nn.Module):
def __init__(self, hidden_dim=512):
super(NeuralNetwork, self).__init__()
self.network = nn.Sequential(
nn.Linear(1280, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 1280)
)
def forward(self, x):
return self.network(x)
# Choose which model to use
# model = LinearModel()
model = NeuralNetwork()
# Loss function and optimizer
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# Training loop
num_epochs = 50
for epoch in range(num_epochs):
total_loss = 0
for inputs, targets in dataloader:
# Forward pass
outputs = model(inputs)
loss = criterion(outputs, targets)
# Backward pass and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
# Print progress
if (epoch + 1) % 5 == 0:
print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {total_loss/len(dataloader):.4f}')
# After training, to use the model for prediction:
def predict(input_vector):
model.eval()
with torch.no_grad():
input_tensor = torch.tensor(input_vector, dtype=torch.float32)
return model(input_tensor).numpy()
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