Deep-Learning-Gravity

5 dimensional Space import torch import torch.nn as nn import torch.optim as optim from sklearn.preprocessing import StandardScaler from sklearn.model_selection import train_test_split

Assume input_data is a 2D tensor (100 samples, 5 features)

input_data = torch.randn(100, 5) target_data = torch.randn(100, 1)

Normalize input_data

scaler = StandardScaler() input_data = torch.tensor(scaler.fit_transform(input_data), dtype=torch.float32)

Split the data into training and validation sets

input_train, input_val, target_train, target_val = train_test_split(input_data, target_data, test_size=0.2, random_state=42)

Define the General Relativity-inspired neural network

class GeneralRelativityModel(nn.Module): def init(self, input_size, hidden_size, output_size): super(GeneralRelativityModel, self).init() self.layer1 = nn.Linear(input_size, hidden_size) self.relu = nn.ReLU() self.layer2 = nn.Linear(hidden_size, output_size)

def forward(self, x):
    x = self.layer1(x)
    x = self.relu(x)
    x = self.layer2(x)
    return x

Check if GPU is available and move the model to GPU

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") input_size = 5 hidden_size = 10 output_size = 1

gravity_model = GeneralRelativityModel(input_size, hidden_size, output_size).to(device)

Define a loss function and optimizer

criterion = nn.MSELoss() optimizer = optim.Adam(gravity_model.parameters(), lr=0.001)

Training loop

num_epochs = 1000 input_train = input_train.to(device) target_train = target_train.to(device) input_val = input_val.to(device) target_val = target_val.to(device)

for epoch in range(num_epochs): # Forward pass outputs = gravity_model(input_train) loss = criterion(outputs, target_train)

# Backward pass and optimization
optimizer.zero_grad()
loss.backward()
optimizer.step()

# Validation loss
val_outputs = gravity_model(input_val)
val_loss = criterion(val_outputs, target_val)

# Print progress
if (epoch + 1) % 100 == 0:
    print(f'Epoch [{epoch+1}/{num_epochs}], Training Loss: {loss.item():.4f}, Validation Loss: {val_loss.item():.4f}')

Evaluate the model on test data if available

test_outputs = gravity_model(test_input)

test_loss = criterion(test_outputs, test_target)

RADj375/Deep-Learning-Gravity