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index 0000000..ffa2c78 Binary files /dev/null and b/analysis/average_normalized/small_perturbation/linear.png differ diff --git a/analysis/average_normalized/small_perturbation/quadratic.png b/analysis/average_normalized/small_perturbation/quadratic.png new file mode 100644 index 0000000..688c2d6 Binary files /dev/null and b/analysis/average_normalized/small_perturbation/quadratic.png differ diff --git a/analysis/controller_across_epochs.py b/analysis/controller_across_epochs.py index 4f1f028..b1f73bf 100644 --- a/analysis/controller_across_epochs.py +++ b/analysis/controller_across_epochs.py @@ -81,7 +81,7 @@ initial_conditions = { } # Loss functions to iterate over -loss_functions = ["constant", "linear", "quadratic", "exponential", "inverse", "inverse_squared"] +loss_functions = ["constant", "linear", "quadratic", "cubic", "inverse", "inverse_squared", "inverse_cubed"] epoch_start = 0 # Start of the epoch range @@ -94,7 +94,7 @@ if __name__ == "__main__": os.makedirs(full_path, exist_ok=True) # Create directory if it does not exist for loss_function in loss_functions: - controller_dir = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/normalized/average_normalized/{loss_function}/controllers" + controller_dir = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/average_normalized/{loss_function}/controllers" controller_files = sorted([f for f in os.listdir(controller_dir) if f.startswith("controller_") and f.endswith(".pth")]) # Extract epoch numbers and filter based on the defined range and interval epoch_numbers = [int(f.split('_')[1].split('.')[0]) for f in controller_files] diff --git a/analysis/controller_across_epochs_old.py b/analysis/controller_across_epochs_old.py deleted file mode 100644 index 8df518a..0000000 --- a/analysis/controller_across_epochs_old.py +++ /dev/null @@ -1,149 +0,0 @@ -import os -import numpy as np -import torch -import torch.nn as nn -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from multiprocessing import Pool, cpu_count - -# Define PendulumController class -from PendulumController import PendulumController - -# ODE solver (RK4 method) -def pendulum_ode_step(state, dt, desired_theta, controller): - theta, omega, alpha = state - - def compute_torque(th, om, al): - inp = torch.tensor([[th, om, al, desired_theta]], dtype=torch.float32) - with torch.no_grad(): - torque = controller(inp) - torque = torch.clamp(torque, -250, 250) - return float(torque) - - def derivatives(state, torque): - th, om, al = state - a = (g / R) * np.sin(th) + torque / (m * R**2) - return np.array([om, a, 0]) # dtheta, domega, dalpha - - # Compute RK4 steps - torque1 = compute_torque(theta, omega, alpha) - k1 = dt * derivatives(state, torque1) - - state_k2 = state + 0.5 * k1 - torque2 = compute_torque(state_k2[0], state_k2[1], state_k2[2]) - k2 = dt * derivatives(state_k2, torque2) - - state_k3 = state + 0.5 * k2 - torque3 = compute_torque(state_k3[0], state_k3[1], state_k3[2]) - k3 = dt * derivatives(state_k3, torque3) - - state_k4 = state + k3 - torque4 = compute_torque(state_k4[0], state_k4[1], state_k4[2]) - k4 = dt * derivatives(state_k4, torque4) - - new_state = state + (k1 + 2*k2 + 2*k3 + k4) / 6.0 - return new_state - -# Constants -g = 9.81 # Gravity -R = 1.0 # Length of the pendulum -m = 10.0 # Mass -dt = 0.02 # Time step -num_steps = 500 # Simulation time steps - -# Directory containing controller files -loss_function = "quadratic" -controller_dir = f"/home/judson/Neural-Networks-in-GNC/inverted_pendulum/training/normalized/training/{loss_function}/controllers" -#controller_dir = f"C:/Users/Judson/Desktop/New Gitea/Neural-Networks-in-GNC/inverted_pendulum/training/{loss_function}/controllers" -controller_files = sorted([f for f in os.listdir(controller_dir) if f.startswith("controller_") and f.endswith(".pth")]) - -# Sorting controllers by epoch -controller_epochs = [int(f.split('_')[1].split('.')[0]) for f in controller_files] -sorted_controllers = [x for _, x in sorted(zip(controller_epochs, controller_files))] - -# **Epoch Range Selection** -epoch_range = (0, 1000) # Set your desired range (e.g., (0, 5000) or (0, 100)) - -filtered_controllers = [ - f for f in sorted_controllers - if epoch_range[0] <= int(f.split('_')[1].split('.')[0]) <= epoch_range[1] -] - -# **Granularity Control: Select every Nth controller** -N = 1 # Change this value to adjust granularity (e.g., every 5th controller) -selected_controllers = filtered_controllers[::N] # Take every Nth controller within the range - -# Initial condition -# theta0, omega0, alpha0, desired_theta = (-np.pi, -2*np.pi, 0.0, -1.3*np.pi) # Example initial condition -theta0, omega0, alpha0, desired_theta = (-np.pi, 0.0, 0.0, 0.0) # Example initial condition - -# Parallel function must return epoch explicitly -def run_simulation(controller_file): - epoch = int(controller_file.split('_')[1].split('.')[0]) - - # Load controller - controller = PendulumController() - controller.load_state_dict(torch.load(os.path.join(controller_dir, controller_file))) - controller.eval() - - # Run simulation - state = np.array([theta0, omega0, alpha0]) - theta_vals = [] - - for _ in range(num_steps): - theta_vals.append(state[0]) - state = pendulum_ode_step(state, dt, desired_theta, controller) - - return epoch, theta_vals # Return epoch with data - -# Parallel processing -if __name__ == "__main__": - num_workers = min(cpu_count(), 16) # Limit to 16 workers max - print(f"Using {num_workers} parallel workers...") - - with Pool(processes=num_workers) as pool: - results = pool.map(run_simulation, selected_controllers) - - # Sort results by epoch to ensure correct order - results.sort(key=lambda x: x[0]) - epochs, theta_over_epochs = zip(*results) # Unzip sorted results - - # Convert results to NumPy arrays - theta_over_epochs = np.array(theta_over_epochs) - - # Create 3D line plot - fig = plt.figure(figsize=(10, 7)) - ax = fig.add_subplot(111, projection='3d') - - time_steps = np.arange(num_steps) * dt # X-axis (time) - - # Plot each controller as a separate line - for epoch, theta_vals in zip(epochs, theta_over_epochs): - ax.plot( - [epoch] * len(time_steps), # Y-axis (epoch stays constant for each line) - time_steps, # X-axis (time) - theta_vals, # Z-axis (theta evolution) - label=f"Epoch {epoch}" if epoch % (N * 10) == 0 else "", # Label some lines for clarity - ) - - # Labels - ax.set_xlabel("Epoch") - ax.set_ylabel("Time (s)") - ax.set_zlabel("Theta (rad)") - ax.set_title(f"Pendulum Angle Evolution for {loss_function}") - - # Add a horizontal line at desired_theta across all epochs and time steps - epochs_array = np.array([epoch for epoch, _ in zip(epochs, theta_over_epochs)]) - ax.plot( - epochs_array, # X-axis spanning all epochs - [time_steps.max()] * len(epochs_array), # Y-axis at the maximum time step - [desired_theta] * len(epochs_array), # Constant Z-axis value of desired_theta - color='r', linestyle='--', linewidth=2, label='Desired 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16.09824562072754 +average_normalized/inverse_cubed/controllers/controller_999.pth saved with loss: 16.098236083984375 +average_normalized/inverse_cubed/controllers/controller_1000.pth saved with loss: 16.098224639892578 Training complete. Models and logs are saved under respective directories for each loss function. diff --git a/training/old/non-normalized/cubic_time_weight/trainer_cubic_time_weights.py b/training/old/non-normalized/cubic_time_weight/trainer_cubic_time_weights.py deleted file mode 100644 index 7177c30..0000000 --- a/training/old/non-normalized/cubic_time_weight/trainer_cubic_time_weights.py +++ /dev/null @@ -1,173 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import inspect -import time -import csv -import os - -# Specify directory for storing results -output_dir = "training/cubic_time_weight" -controller_output_dir = output_dir + "/controllers" -os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist -os.makedirs(controller_output_dir, exist_ok=True) # Create directory if it doesn't exist - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Device setup -device = torch.device("cpu") - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -state_0 = torch.tensor([ - [1/6 * torch.pi, 0.0, 0.0, 0.0], - [-1/6 * torch.pi, 0.0, 0.0, 0.0], - [2/3 * torch.pi, 0.0, 0.0, 0.0], - [-2/3 * torch.pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * torch.pi, 0.0, 0.0], - [0.0, -1/3 * torch.pi, 0.0, 0.0], - [0.0, 2 * torch.pi, 0.0, 0.0], - [0.0, -2 * torch.pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * torch.pi], - [0.0, 0.0, 0.0, -2 * torch.pi], - [0.0, 0.0, 0.0, 1/2 * torch.pi], - [0.0, 0.0, 0.0, -1/2 * torch.pi], - [0.0, 0.0, 0.0, 1/3 * torch.pi], - [0.0, 0.0, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 0.0], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 0.0], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 1/3 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 2 * torch.pi], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 2 * torch.pi], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 4 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -4 * torch.pi], -], dtype=torch.float32, device=device) - -class PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) - -class PendulumDynamics(nn.Module): - def __init__(self, controller): - super().__init__() - self.controller = controller - - def forward(self, t, state): - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - tau = self.controller(input).squeeze(-1) - alpha_desired = (g / R) * torch.sin(theta) + tau / (m * R**2) - return torch.stack([omega, alpha, alpha_desired - alpha, torch.zeros_like(desired_theta)], dim=1) - -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = (t_span ** 3).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 5_001 # 5000 + 1 for 5000 total weight updates -save_every = 1 # How often to save the controller.pth - -# File paths -config_file = os.path.join(output_dir, "training_config.txt") -log_file = os.path.join(output_dir, "training_log.csv") -model_file = "" # Placeholder for the model file, updated in training loop - -# Save configuration details -with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) # Extract and write loss function source code - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - -# Overwrite the log file at the start -with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss", "Elapsed Time (s)"]) - -# Training loop with real-time logging and NaN tracking -start_time = time.time() -with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - - for epoch in range(num_epochs): - epoch_start_time = time.time() - - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - - elapsed_time = time.time() - epoch_start_time - - if torch.isnan(loss): - print(f"NaN detected at epoch {epoch}. Skipping step.") - csv_writer.writerow([epoch, "NaN detected", elapsed_time]) - csvfile.flush() # Ensure real-time writing - optimizer.zero_grad() - continue - - loss.backward() - optimizer.step() - - # Log normal loss - csv_writer.writerow([epoch, loss.item(), elapsed_time]) - csvfile.flush() # Ensure real-time writing - - if epoch % save_every == 0: - print(f"Epoch {epoch}/{num_epochs} | Loss: {loss.item():.6f} | Time: {elapsed_time:.4f} sec") - model_file = os.path.join(controller_output_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - -# Final save -torch.save(controller.state_dict(), model_file) -print(f"No time weight training complete. Model and files saved in directory '{output_dir}'. Model saved as '{model_file}'. Logs saved in '{log_file}' and configuration in '{config_file}'.") \ No newline at end of file diff --git a/training/old/non-normalized/cubic_time_weight/training_config.txt b/training/old/non-normalized/cubic_time_weight/training_config.txt deleted file mode 100644 index 0a8f6fd..0000000 --- a/training/old/non-normalized/cubic_time_weight/training_config.txt +++ /dev/null @@ -1,39 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = (t_span ** 3).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/non-normalized/cubic_time_weight/training_log.csv 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a/training/old/non-normalized/exponential_time_weight/trainer_exponential_time_weights.py +++ /dev/null @@ -1,174 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import inspect -import time -import csv -import os - -# Specify directory for storing results -output_dir = "training/exponential_time_weight" -controller_output_dir = output_dir + "/controllers" -os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist -os.makedirs(controller_output_dir, exist_ok=True) # Create directory if it doesn't exist - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Device setup -device = torch.device("cpu") - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -state_0 = torch.tensor([ - [1/6 * torch.pi, 0.0, 0.0, 0.0], - [-1/6 * torch.pi, 0.0, 0.0, 0.0], - [2/3 * torch.pi, 0.0, 0.0, 0.0], - [-2/3 * torch.pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * torch.pi, 0.0, 0.0], - [0.0, -1/3 * torch.pi, 0.0, 0.0], - [0.0, 2 * torch.pi, 0.0, 0.0], - [0.0, -2 * torch.pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * torch.pi], - [0.0, 0.0, 0.0, -2 * torch.pi], - [0.0, 0.0, 0.0, 1/2 * torch.pi], - [0.0, 0.0, 0.0, -1/2 * torch.pi], - [0.0, 0.0, 0.0, 1/3 * torch.pi], - [0.0, 0.0, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 0.0], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 0.0], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 1/3 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 2 * torch.pi], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 2 * torch.pi], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 4 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -4 * torch.pi], -], dtype=torch.float32, device=device) - -class PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) - -class PendulumDynamics(nn.Module): - def __init__(self, controller): - super().__init__() - self.controller = controller - - def forward(self, t, state): - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - tau = self.controller(input).squeeze(-1) - alpha_desired = (g / R) * torch.sin(theta) + tau / (m * R**2) - return torch.stack([omega, alpha, alpha_desired - alpha, torch.zeros_like(desired_theta)], dim=1) - -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - lamda_exp = 1 - time_weights = torch.exp(lamda_exp * t_span).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller).to(device) - -# Optimizer setup -learning_rate = 1e-2 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 5_001 # 5000 + 1 for 5000 total weight updates -save_every = 1 # How often to save the controller.pth - -# File paths -config_file = os.path.join(output_dir, "training_config.txt") -log_file = os.path.join(output_dir, "training_log.csv") -model_file = "" # Placeholder for the model file, updated in training loop - -# Save configuration details -with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) # Extract and write loss function source code - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - -# Overwrite the log file at the start -with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss", "Elapsed Time (s)"]) - -# Training loop with real-time logging and NaN tracking -start_time = time.time() -with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - - for epoch in range(num_epochs): - epoch_start_time = time.time() - - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - - elapsed_time = time.time() - epoch_start_time - - if torch.isnan(loss): - print(f"NaN detected at epoch {epoch}. Skipping step.") - csv_writer.writerow([epoch, "NaN detected", elapsed_time]) - csvfile.flush() # Ensure real-time writing - optimizer.zero_grad() - continue - - loss.backward() - optimizer.step() - - # Log normal loss - csv_writer.writerow([epoch, loss.item(), elapsed_time]) - csvfile.flush() # Ensure real-time writing - - if epoch % save_every == 0: - print(f"Epoch {epoch}/{num_epochs} | Loss: {loss.item():.6f} | Time: {elapsed_time:.4f} sec") - model_file = os.path.join(controller_output_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - -# Final save -torch.save(controller.state_dict(), model_file) -print(f"No time weight training complete. Model and files saved in directory '{output_dir}'. Model saved as '{model_file}'. Logs saved in '{log_file}' and configuration in '{config_file}'.") \ No newline at end of file diff --git a/training/old/non-normalized/exponential_time_weight/training_config.txt b/training/old/non-normalized/exponential_time_weight/training_config.txt deleted file mode 100644 index 6b1b52c..0000000 --- a/training/old/non-normalized/exponential_time_weight/training_config.txt +++ /dev/null @@ -1,40 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.01 -Weight Decay: 0.0001 - -Loss Function: -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - lamda_exp = 1 - time_weights = torch.exp(lamda_exp * t_span).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/non-normalized/exponential_time_weight/training_log.csv b/training/old/non-normalized/exponential_time_weight/training_log.csv deleted file mode 100644 index 9a48828..0000000 --- a/training/old/non-normalized/exponential_time_weight/training_log.csv +++ /dev/null @@ -1,5002 +0,0 @@ -Epoch,Loss,Elapsed Time (s) -0,6674304000.0,1.3667628765106201 -1,5328950272.0,1.2833521366119385 -2,4329774080.0,1.2473316192626953 -3,3662734848.0,1.384526252746582 -4,2942641664.0,1.2597594261169434 -5,2469842176.0,1.2855281829833984 -6,1806706944.0,1.3894639015197754 -7,1330112896.0,1.2864453792572021 -8,836584640.0,1.3381774425506592 -9,704432192.0,1.3072855472564697 -10,691741184.0,1.3708198070526123 -11,692316160.0,1.3020713329315186 -12,681004160.0,1.2474617958068848 -13,636678912.0,1.5199341773986816 -14,527850048.0,1.2627284526824951 -15,565226368.0,1.2658405303955078 -16,600644288.0,1.325063943862915 -17,587484160.0,1.2777490615844727 -18,540748416.0,1.270836591720581 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a/training/old/non-normalized/inverse_time_weight/trainer_inverse_time_weights.py +++ /dev/null @@ -1,173 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import inspect -import time -import csv -import os - -# Specify directory for storing results -output_dir = "training/inverse_time_weight" -controller_output_dir = output_dir + "/controllers" -os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist -os.makedirs(controller_output_dir, exist_ok=True) # Create directory if it doesn't exist - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Device setup -device = torch.device("cpu") - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -state_0 = torch.tensor([ - [1/6 * torch.pi, 0.0, 0.0, 0.0], - [-1/6 * torch.pi, 0.0, 0.0, 0.0], - [2/3 * torch.pi, 0.0, 0.0, 0.0], - [-2/3 * torch.pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * torch.pi, 0.0, 0.0], - [0.0, -1/3 * torch.pi, 0.0, 0.0], - [0.0, 2 * torch.pi, 0.0, 0.0], - [0.0, -2 * torch.pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * torch.pi], - [0.0, 0.0, 0.0, -2 * torch.pi], - [0.0, 0.0, 0.0, 1/2 * torch.pi], - [0.0, 0.0, 0.0, -1/2 * torch.pi], - [0.0, 0.0, 0.0, 1/3 * torch.pi], - [0.0, 0.0, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 0.0], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 0.0], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 1/3 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 2 * torch.pi], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 2 * torch.pi], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 4 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -4 * torch.pi], -], dtype=torch.float32, device=device) - -class PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) - -class PendulumDynamics(nn.Module): - def __init__(self, controller): - super().__init__() - self.controller = controller - - def forward(self, t, state): - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - tau = self.controller(input).squeeze(-1) - alpha_desired = (g / R) * torch.sin(theta) + tau / (m * R**2) - return torch.stack([omega, alpha, alpha_desired - alpha, torch.zeros_like(desired_theta)], dim=1) - -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = (1 / (t_span + 1e-3)).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 5_001 # 5000 + 1 for 5000 total weight updates -save_every = 1 # How often to save the controller.pth - -# File paths -config_file = os.path.join(output_dir, "training_config.txt") -log_file = os.path.join(output_dir, "training_log.csv") -model_file = "" # Placeholder for the model file, updated in training loop - -# Save configuration details -with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) # Extract and write loss function source code - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - -# Overwrite the log file at the start -with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss", "Elapsed Time (s)"]) - -# Training loop with real-time logging and NaN tracking -start_time = time.time() -with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - - for epoch in range(num_epochs): - epoch_start_time = time.time() - - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - - elapsed_time = time.time() - epoch_start_time - - if torch.isnan(loss): - print(f"NaN detected at epoch {epoch}. Skipping step.") - csv_writer.writerow([epoch, "NaN detected", elapsed_time]) - csvfile.flush() # Ensure real-time writing - optimizer.zero_grad() - continue - - loss.backward() - optimizer.step() - - # Log normal loss - csv_writer.writerow([epoch, loss.item(), elapsed_time]) - csvfile.flush() # Ensure real-time writing - - if epoch % save_every == 0: - print(f"Epoch {epoch}/{num_epochs} | Loss: {loss.item():.6f} | Time: {elapsed_time:.4f} sec") - model_file = os.path.join(controller_output_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - -# Final save -torch.save(controller.state_dict(), model_file) -print(f"No time weight training complete. Model and files saved in directory '{output_dir}'. Model saved as '{model_file}'. Logs saved in '{log_file}' and configuration in '{config_file}'.") \ No newline at end of file diff --git a/training/old/non-normalized/inverse_time_weight/training_config.txt b/training/old/non-normalized/inverse_time_weight/training_config.txt deleted file mode 100644 index 1b8949c..0000000 --- a/training/old/non-normalized/inverse_time_weight/training_config.txt +++ /dev/null @@ -1,39 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = (1 / (t_span + 1e-3)).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/non-normalized/inverse_time_weight/training_log.csv b/training/old/non-normalized/inverse_time_weight/training_log.csv deleted file mode 100644 index 944380c..0000000 --- a/training/old/non-normalized/inverse_time_weight/training_log.csv +++ /dev/null @@ -1,5002 +0,0 @@ -Epoch,Loss,Elapsed Time (s) -0,181739.828125,1.3314204216003418 -1,100950.7734375,1.3378369808197021 -2,76864.40625,1.265439748764038 -3,52028.859375,1.3064358234405518 -4,38001.296875,1.2949304580688477 -5,34149.06640625,1.2212328910827637 -6,33869.70703125,1.411001443862915 -7,33113.734375,1.2864582538604736 -8,32848.95703125,1.3085825443267822 -9,32615.2578125,1.3094747066497803 -10,32362.697265625,1.3847272396087646 -11,32094.2578125,1.3138887882232666 -12,31727.27734375,1.283437967300415 -13,31454.09375,1.3611226081848145 -14,31241.083984375,1.2439279556274414 -15,31058.287109375,1.3267590999603271 -16,30896.005859375,1.3888075351715088 -17,30750.26171875,1.4802591800689697 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a/training/old/non-normalized/linear_time_weight/trainer_linear_time_weights.py b/training/old/non-normalized/linear_time_weight/trainer_linear_time_weights.py deleted file mode 100644 index 5da4ecd..0000000 --- a/training/old/non-normalized/linear_time_weight/trainer_linear_time_weights.py +++ /dev/null @@ -1,173 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import inspect -import time -import csv -import os - -# Specify directory for storing results -output_dir = "training/linear_time_weight" -controller_output_dir = output_dir + "/controllers" -os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist -os.makedirs(controller_output_dir, exist_ok=True) # Create directory if it doesn't exist - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Device setup -device = torch.device("cpu") - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -state_0 = torch.tensor([ - [1/6 * torch.pi, 0.0, 0.0, 0.0], - [-1/6 * torch.pi, 0.0, 0.0, 0.0], - [2/3 * torch.pi, 0.0, 0.0, 0.0], - [-2/3 * torch.pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * torch.pi, 0.0, 0.0], - [0.0, -1/3 * torch.pi, 0.0, 0.0], - [0.0, 2 * torch.pi, 0.0, 0.0], - [0.0, -2 * torch.pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * torch.pi], - [0.0, 0.0, 0.0, -2 * torch.pi], - [0.0, 0.0, 0.0, 1/2 * torch.pi], - [0.0, 0.0, 0.0, -1/2 * torch.pi], - [0.0, 0.0, 0.0, 1/3 * torch.pi], - [0.0, 0.0, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 0.0], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 0.0], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 1/3 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 2 * torch.pi], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 2 * torch.pi], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 4 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -4 * torch.pi], -], dtype=torch.float32, device=device) - -class PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) - -class PendulumDynamics(nn.Module): - def __init__(self, controller): - super().__init__() - self.controller = controller - - def forward(self, t, state): - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - tau = self.controller(input).squeeze(-1) - alpha_desired = (g / R) * torch.sin(theta) + tau / (m * R**2) - return torch.stack([omega, alpha, alpha_desired - alpha, torch.zeros_like(desired_theta)], dim=1) - -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = t_span.view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 5_001 # 5000 + 1 for 5000 total weight updates -save_every = 1 # How often to save the controller.pth - -# File paths -config_file = os.path.join(output_dir, "training_config.txt") -log_file = os.path.join(output_dir, "training_log.csv") -model_file = "" # Placeholder for the model file, updated in training loop - -# Save configuration details -with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) # Extract and write loss function source code - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - -# Overwrite the log file at the start -with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss", "Elapsed Time (s)"]) - -# Training loop with real-time logging and NaN tracking -start_time = time.time() -with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - - for epoch in range(num_epochs): - epoch_start_time = time.time() - - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - - elapsed_time = time.time() - epoch_start_time - - if torch.isnan(loss): - print(f"NaN detected at epoch {epoch}. Skipping step.") - csv_writer.writerow([epoch, "NaN detected", elapsed_time]) - csvfile.flush() # Ensure real-time writing - optimizer.zero_grad() - continue - - loss.backward() - optimizer.step() - - # Log normal loss - csv_writer.writerow([epoch, loss.item(), elapsed_time]) - csvfile.flush() # Ensure real-time writing - - if epoch % save_every == 0: - print(f"Epoch {epoch}/{num_epochs} | Loss: {loss.item():.6f} | Time: {elapsed_time:.4f} sec") - model_file = os.path.join(controller_output_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - -# Final save -torch.save(controller.state_dict(), model_file) -print(f"No time weight training complete. Model and files saved in directory '{output_dir}'. Model saved as '{model_file}'. Logs saved in '{log_file}' and configuration in '{config_file}'.") \ No newline at end of file diff --git a/training/old/non-normalized/linear_time_weight/training_config.txt b/training/old/non-normalized/linear_time_weight/training_config.txt deleted file mode 100644 index 94ad9c5..0000000 --- a/training/old/non-normalized/linear_time_weight/training_config.txt +++ /dev/null @@ -1,39 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = t_span.view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/non-normalized/linear_time_weight/training_log.csv 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a/training/old/non-normalized/no_time_weight/trainer_no_time_weights.py +++ /dev/null @@ -1,170 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import inspect -import time -import csv -import os - -# Specify directory for storing results -output_dir = "training/no_time_weight" -controller_output_dir = output_dir + "/controllers" -os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist -os.makedirs(controller_output_dir, exist_ok=True) # Create directory if it doesn't exist - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Device setup -device = torch.device("cpu") - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -state_0 = torch.tensor([ - [1/6 * torch.pi, 0.0, 0.0, 0.0], - [-1/6 * torch.pi, 0.0, 0.0, 0.0], - [2/3 * torch.pi, 0.0, 0.0, 0.0], - [-2/3 * torch.pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * torch.pi, 0.0, 0.0], - [0.0, -1/3 * torch.pi, 0.0, 0.0], - [0.0, 2 * torch.pi, 0.0, 0.0], - [0.0, -2 * torch.pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * torch.pi], - [0.0, 0.0, 0.0, -2 * torch.pi], - [0.0, 0.0, 0.0, 1/2 * torch.pi], - [0.0, 0.0, 0.0, -1/2 * torch.pi], - [0.0, 0.0, 0.0, 1/3 * torch.pi], - [0.0, 0.0, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 0.0], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 0.0], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 1/3 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 2 * torch.pi], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 2 * torch.pi], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 4 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -4 * torch.pi], -], dtype=torch.float32, device=device) - -class PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) - -class PendulumDynamics(nn.Module): - def __init__(self, controller): - super().__init__() - self.controller = controller - - def forward(self, t, state): - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - tau = self.controller(input).squeeze(-1) - alpha_desired = (g / R) * torch.sin(theta) + tau / (m * R**2) - return torch.stack([omega, alpha, alpha_desired - alpha, torch.zeros_like(desired_theta)], dim=1) - -def loss_fn(state_traj): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - return 1e3 * torch.mean((theta - desired_theta) ** 2) - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 5_001 # 5000 + 1 for 5000 total weight updates -save_every = 1 # How often to save the controller.pth - -# File paths -config_file = os.path.join(output_dir, "training_config.txt") -log_file = os.path.join(output_dir, "training_log.csv") -model_file = "" # Placeholder for the model file, updated in training loop - -# Save configuration details -with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) # Extract and write loss function source code - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - -# Overwrite the log file at the start -with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss", "Elapsed Time (s)"]) - -# Training loop with real-time logging and NaN tracking -start_time = time.time() -with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - - for epoch in range(num_epochs): - epoch_start_time = time.time() - - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj) - - elapsed_time = time.time() - epoch_start_time - - if torch.isnan(loss): - print(f"NaN detected at epoch {epoch}. Skipping step.") - csv_writer.writerow([epoch, "NaN detected", elapsed_time]) - csvfile.flush() # Ensure real-time writing - optimizer.zero_grad() - continue - - loss.backward() - optimizer.step() - - # Log normal loss - csv_writer.writerow([epoch, loss.item(), elapsed_time]) - csvfile.flush() # Ensure real-time writing - - if epoch % save_every == 0: - print(f"Epoch {epoch}/{num_epochs} | Loss: {loss.item():.6f} | Time: {elapsed_time:.4f} sec") - model_file = os.path.join(controller_output_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - -# Final save -torch.save(controller.state_dict(), model_file) -print(f"No time weight training complete. Model and files saved in directory '{output_dir}'. Model saved as '{model_file}'. Logs saved in '{log_file}' and configuration in '{config_file}'.") \ No newline at end of file diff --git a/training/old/non-normalized/no_time_weight/training_config.txt b/training/old/non-normalized/no_time_weight/training_config.txt deleted file mode 100644 index 41f7421..0000000 --- a/training/old/non-normalized/no_time_weight/training_config.txt +++ /dev/null @@ -1,36 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: -def loss_fn(state_traj): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - return 1e3 * torch.mean((theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/non-normalized/no_time_weight/training_log.csv b/training/old/non-normalized/no_time_weight/training_log.csv deleted file mode 100644 index b7c265e..0000000 --- a/training/old/non-normalized/no_time_weight/training_log.csv 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PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - return self.net(x) - -controller = PendulumController() -controller.load_state_dict(torch.load(controller_file_name)) -controller.eval() -print(f"{controller_file_name} loaded.") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Time step settings -dt = 0.01 # Fixed time step -T = 20.0 # Total simulation time -num_steps = int(T / dt) -t_eval = np.linspace(0, T, num_steps) - -# Define ODE System (RK4) - Also Returns Torque -def pendulum_ode_step(state, dt, desired_theta): - theta, omega, alpha = state - - def compute_torque(th, om, al): - # Evaluate NN -> torque - inp = torch.tensor([[th, om, al, desired_theta]], dtype=torch.float32) - with torch.no_grad(): - torque = controller(inp) - torque = torch.clamp(torque, -250, 250) - return float(torque) - - def derivatives(state, torque): - th, om, al = state - a = (g / R) * np.sin(th) + torque / (m * R**2) - return np.array([om, a, 0]) # dtheta, domega, dalpha - - # Compute k1 - torque1 = compute_torque(theta, omega, alpha) - k1 = dt * derivatives(state, torque1) - - # Compute k2 - state_k2 = state + 0.5 * k1 - torque2 = compute_torque(state_k2[0], state_k2[1], state_k2[2]) - k2 = dt * derivatives(state_k2, torque2) - - # Compute k3 - state_k3 = state + 0.5 * k2 - torque3 = compute_torque(state_k3[0], state_k3[1], state_k3[2]) - k3 = dt * derivatives(state_k3, torque3) - - # Compute k4 - state_k4 = state + k3 - torque4 = compute_torque(state_k4[0], state_k4[1], state_k4[2]) - k4 = dt * derivatives(state_k4, torque4) - - # Update state using RK4 formula - new_state = state + (k1 + 2*k2 + 2*k3 + k4) / 6.0 - - # Compute weighted final torque - final_torque = (torque1 + 2*torque2 + 2*torque3 + torque4) / 6.0 - - return new_state, final_torque - - - - -# Run Simulations for Different Initial Conditions -# [theta0, omega0, alpha0, desired_theta] -in_sample_cases = [ - # Theta perturbations - (1/6 * np.pi, 0.0, 0.0, 0.0), - (-1/6 * np.pi, 0.0, 0.0, 0.0), - (2/3 * np.pi, 0.0, 0.0, 0.0), - (-2/3 * np.pi, 0.0, 0.0, 0.0), - - # Omega perturbations - (0.0, 1/3 * np.pi, 0.0, 0.0), - (0.0, -1/3 * np.pi, 0.0, 0.0), - (0.0, 2 * np.pi, 0.0, 0.0), - (0.0, -2 * np.pi, 0.0, 0.0), - - # Return to non-zero theta - (0.0, 0.0, 0.0, 2*np.pi), - (0.0, 0.0, 0.0, -2*np.pi), - (0.0, 0.0, 0.0, 1/2 * np.pi), - (0.0, 0.0, 0.0, -1/2 *np.pi), - (0.0, 0.0, 0.0, 1/3 * np.pi), - (0.0, 0.0, 0.0, -1/3 *np.pi), - - # Mix cases - (1/4 * np.pi, 1 * np.pi, 0.0, 0.0), - (-1/4 * np.pi, -1 * np.pi, 0.0, 0.0), - (1/2 * np.pi, -1 * np.pi, 0.0, 1/3 * np.pi), - (-1/2 * np.pi, 1 * np.pi, 0.0, -1/3 *np.pi), - (1/4 * np.pi, 1 * np.pi, 0.0, 2 * np.pi), - (-1/4 * np.pi, -1 * np.pi, 0.0, 2 * np.pi), - (1/2 * np.pi, -1 * np.pi, 0.0, 4 * np.pi), - (-1/2 * np.pi, 1 * np.pi, 0.0, -4 *np.pi), -] - -# Validation in-sample cases -print("Performing in-sample validation") - -losses = [] -final_thetas = [] - -for idx, (theta0, omega0, alpha0, desired_theta) in enumerate(in_sample_cases): - state = np.array([theta0, omega0, alpha0]) - - theta_vals, omega_vals, alpha_vals, torque_vals = [], [], [], [] - - for _ in range(num_steps): - # Save values - theta_vals.append(state[0]) - omega_vals.append(state[1]) - alpha_vals.append(state[2]) - - # Compute ODE step with real state - state, torque = pendulum_ode_step(state, dt, desired_theta) - - # Store torque - torque_vals.append(torque) - - # Convert lists to arrays - theta_vals = np.array(theta_vals) - omega_vals = np.array(omega_vals) - alpha_vals = np.array(alpha_vals) - torque_vals = np.array(torque_vals) - - # Get the final theta of the system at t=t_final - final_theta = theta_vals[-1] - final_thetas.append(final_theta) - - # Calculate this specific condition's loss - loss = 1e3 * np.mean((theta_vals - desired_theta)**2) - losses.append(loss) - - # Plot Results - fig, ax1 = plt.subplots(figsize=(10, 6)) - ax1.plot(t_eval, theta_vals, label="theta", color="blue") - ax1.plot(t_eval, omega_vals, label="omega", color="green") - ax1.plot(t_eval, alpha_vals, label="alpha", color="red") - ax1.axhline(desired_theta, label="Desired Theta", color="black") - - # Draw horizontal lines at theta = 2n*pi (as many as fit within range) - y_min = min(np.min(theta_vals), np.min(omega_vals), np.min(alpha_vals)) - y_max = max(np.max(theta_vals), np.max(omega_vals), np.max(alpha_vals)) - - n_min = int(np.ceil(y_min / (2 * np.pi))) - n_max = int(np.floor(y_max / (2 * np.pi))) - theta_lines = [2 * n * np.pi for n in range(n_min, n_max + 1)] - - ax1.set_xlabel("time [s]") - ax1.set_ylabel("theta, omega, alpha") - ax1.grid(True) - ax1.legend(loc="upper left") - - ax2 = ax1.twinx() - ax2.plot(t_eval, torque_vals, label="torque", color="purple", linestyle="--") - ax2.set_ylabel("Torque [Nm]") - ax2.legend(loc="upper right") - - plt.title(f"IC (theta={theta0:.3f}, omega={omega0:.3f}, alpha={alpha0:.3f})") - plt.tight_layout() - - filename = f"{idx+1}_theta0_{theta0:.3f}_omega0_{omega0:.3f}_alpha0_{alpha0:.3f}_desiredtheta_{desired_theta:.3f}_finaltheta_{final_theta:.3f}.png" - plt.savefig(f"validation/in-sample/{filename}") - plt.close() - - print(f"Saved in-sample validation case {idx+1}") - -# Create a DataFrame for tabular representation -df_losses = pd.DataFrame(in_sample_cases, columns=["theta0", "omega0", "alpha0", "desired_theta"]) -df_losses["final_theta"] = final_thetas -df_losses["loss"] = losses - -# Add run # column -df_losses.insert(0, "Run #", range(1, len(in_sample_cases) + 1)) - -# Print the table -print(df_losses.to_string(index=False)) -print(f"\nAverage Loss for In-Sample Validation: {np.mean(losses):.4f}") - -desired_thetas = np.array([case[3] for case in in_sample_cases]) -final_thetas = np.array(final_thetas) -print(f"\nAverage abs(Final Theta - Desired Theta) for In-Sample Validation: {np.mean(np.abs(final_thetas - desired_thetas)):.4f}") - - -# Out-of-sample validation -print("\nPerforming out-of-sample validation") - -# Out of sample cases previously generated by numpy -out_sample_cases = [ - (-2.198958, -4.428501, 0.450833, 0.000000), - (1.714196, -0.769896, 0.202738, 0.000000), - (0.241195, -5.493715, 0.438996, 0.000000), - (0.030605, 4.901513, -0.479243, 0.000000), - (1.930445, -1.301926, -0.454050, 0.000000), - (-0.676063, 4.246865, 0.036303, 0.000000), - (0.734920, -5.925202, 0.047097, 0.000000), - (-3.074471, -3.535424, 0.315438, 0.000000), - (-0.094486, 6.111091, 0.150525, 0.000000), - (-1.647671, 5.720526, 0.334181, 0.000000), - (-2.611260, 5.087704, 0.045460, -3.610785), - (1.654137, 0.982081, -0.192725, 1.003872), - (-2.394899, 3.550547, -0.430938, 3.261897), - (0.474917, 0.555166, -0.285173, 1.866752), - (-0.640369, -4.678490, -0.340663, 3.150098), - (1.747517, -3.248204, -0.001520, 1.221787), - (2.505283, -2.875006, -0.065617, -3.690269), - (1.337244, 2.221707, 0.044979, -2.459730), - (1.531012, 2.230981, -0.291206, -1.924535), - (-1.065792, 4.320740, 0.075405, -1.550644), -] - - -losses = [] -final_thetas = [] - -for idx, (theta0, omega0, alpha0, desired_theta) in enumerate(out_sample_cases): - state = np.array([theta0, omega0, alpha0]) - - theta_vals, omega_vals, alpha_vals, torque_vals = [], [], [], [] - - for _ in range(num_steps): - # Save values - theta_vals.append(state[0]) - omega_vals.append(state[1]) - alpha_vals.append(state[2]) - - # Compute ODE step with real state - state, torque = pendulum_ode_step(state, dt, desired_theta) - - # Store torque - torque_vals.append(torque) - - # Convert lists to arrays - theta_vals = np.array(theta_vals) - omega_vals = np.array(omega_vals) - alpha_vals = np.array(alpha_vals) - torque_vals = np.array(torque_vals) - - # Get the final theta of the system at t=t_final - final_theta = theta_vals[-1] - final_thetas.append(final_theta) - - # Calculate this specific condition's loss - loss = 1e3 * np.mean((theta_vals - desired_theta)**2) - losses.append(loss) - - # Plot Results - fig, ax1 = plt.subplots(figsize=(10, 6)) - ax1.plot(t_eval, theta_vals, label="theta", color="blue") - ax1.plot(t_eval, omega_vals, label="omega", color="green") - ax1.plot(t_eval, alpha_vals, label="alpha", color="red") - ax1.axhline(desired_theta, label="Desired Theta", color="black") - - # Draw horizontal lines at theta = 2n*pi (as many as fit within range) - y_min = min(np.min(theta_vals), np.min(omega_vals), np.min(alpha_vals)) - y_max = max(np.max(theta_vals), np.max(omega_vals), np.max(alpha_vals)) - - n_min = int(np.ceil(y_min / (2 * np.pi))) - n_max = int(np.floor(y_max / (2 * np.pi))) - theta_lines = [2 * n * np.pi for n in range(n_min, n_max + 1)] - - ax1.set_xlabel("time [s]") - ax1.set_ylabel("theta, omega, alpha") - ax1.grid(True) - ax1.legend(loc="upper left") - - ax2 = ax1.twinx() - ax2.plot(t_eval, torque_vals, label="torque", color="purple", linestyle="--") - ax2.set_ylabel("Torque [Nm]") - ax2.legend(loc="upper right") - - plt.title(f"IC (theta={theta0:.3f}, omega={omega0:.3f}, alpha={alpha0:.3f})") - plt.tight_layout() - - filename = f"{idx+1}_theta0_{theta0:.3f}_omega0_{omega0:.3f}_alpha0_{alpha0:.3f}_desiredtheta_{desired_theta:.3f}_finaltheta_{final_theta:.3f}.png" - plt.savefig(f"validation/out-of-sample/{filename}") - plt.close() - - print(f"Saved out-of-sample validation case {idx+1}") - - -# Create a DataFrame for tabular representation -df_losses = pd.DataFrame(out_sample_cases, columns=["theta0", "omega0", "alpha0", "desired_theta"]) -df_losses["final_theta"] = final_thetas -df_losses["loss"] = losses - -# Add run # column -df_losses.insert(0, "Run #", range(1, len(out_sample_cases) + 1)) - -# Print the table -print(df_losses.to_string(index=False)) -print(f"\nAverage Loss for Out-of-Sample Validation: {np.mean(losses):.4f}") - -desired_thetas = np.array([case[3] for case in out_sample_cases]) -final_thetas = np.array(final_thetas) -print(f"\nAverage abs(Final Theta - Desired Theta) for Out-of-Sample Validation: {np.mean(np.abs(final_thetas - desired_thetas)):.4f}") \ No newline at end of file diff --git a/training/old/non-normalized/quadratic_time_weight/trainer_quadratic_time_weights.py b/training/old/non-normalized/quadratic_time_weight/trainer_quadratic_time_weights.py deleted file mode 100644 index 748c6ff..0000000 --- a/training/old/non-normalized/quadratic_time_weight/trainer_quadratic_time_weights.py +++ /dev/null @@ -1,173 +0,0 @@ -import torch -import torch.nn as nn -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import inspect -import time -import csv -import os - -# Specify directory for storing results -output_dir = "training/quadratic_time_weight" -controller_output_dir = output_dir + "/controllers" -os.makedirs(output_dir, exist_ok=True) # Create directory if it doesn't exist -os.makedirs(controller_output_dir, exist_ok=True) # Create directory if it doesn't exist - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Device setup -device = torch.device("cpu") - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -state_0 = torch.tensor([ - [1/6 * torch.pi, 0.0, 0.0, 0.0], - [-1/6 * torch.pi, 0.0, 0.0, 0.0], - [2/3 * torch.pi, 0.0, 0.0, 0.0], - [-2/3 * torch.pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * torch.pi, 0.0, 0.0], - [0.0, -1/3 * torch.pi, 0.0, 0.0], - [0.0, 2 * torch.pi, 0.0, 0.0], - [0.0, -2 * torch.pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * torch.pi], - [0.0, 0.0, 0.0, -2 * torch.pi], - [0.0, 0.0, 0.0, 1/2 * torch.pi], - [0.0, 0.0, 0.0, -1/2 * torch.pi], - [0.0, 0.0, 0.0, 1/3 * torch.pi], - [0.0, 0.0, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 0.0], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 0.0], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 1/3 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -1/3 * torch.pi], - [1/4 * torch.pi, 1 * torch.pi, 0.0, 2 * torch.pi], - [-1/4 * torch.pi, -1 * torch.pi, 0.0, 2 * torch.pi], - [1/2 * torch.pi, -1 * torch.pi, 0.0, 4 * torch.pi], - [-1/2 * torch.pi, 1 * torch.pi, 0.0, -4 * torch.pi], -], dtype=torch.float32, device=device) - -class PendulumController(nn.Module): - def __init__(self): - super().__init__() - self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) - -class PendulumDynamics(nn.Module): - def __init__(self, controller): - super().__init__() - self.controller = controller - - def forward(self, t, state): - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - tau = self.controller(input).squeeze(-1) - alpha_desired = (g / R) * torch.sin(theta) + tau / (m * R**2) - return torch.stack([omega, alpha, alpha_desired - alpha, torch.zeros_like(desired_theta)], dim=1) - -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = (t_span ** 2).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 5_001 # 5000 + 1 for 5000 total weight updates -save_every = 1 # How often to save the controller.pth - -# File paths -config_file = os.path.join(output_dir, "training_config.txt") -log_file = os.path.join(output_dir, "training_log.csv") -model_file = "" # Placeholder for the model file, updated in training loop - -# Save configuration details -with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) # Extract and write loss function source code - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - -# Overwrite the log file at the start -with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss", "Elapsed Time (s)"]) - -# Training loop with real-time logging and NaN tracking -start_time = time.time() -with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - - for epoch in range(num_epochs): - epoch_start_time = time.time() - - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - - elapsed_time = time.time() - epoch_start_time - - if torch.isnan(loss): - print(f"NaN detected at epoch {epoch}. Skipping step.") - csv_writer.writerow([epoch, "NaN detected", elapsed_time]) - csvfile.flush() # Ensure real-time writing - optimizer.zero_grad() - continue - - loss.backward() - optimizer.step() - - # Log normal loss - csv_writer.writerow([epoch, loss.item(), elapsed_time]) - csvfile.flush() # Ensure real-time writing - - if epoch % save_every == 0: - print(f"Epoch {epoch}/{num_epochs} | Loss: {loss.item():.6f} | Time: {elapsed_time:.4f} sec") - model_file = os.path.join(controller_output_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - -# Final save -torch.save(controller.state_dict(), model_file) -print(f"No time weight training complete. Model and files saved in directory '{output_dir}'. Model saved as '{model_file}'. Logs saved in '{log_file}' and configuration in '{config_file}'.") \ No newline at end of file diff --git a/training/old/non-normalized/quadratic_time_weight/training_config.txt b/training/old/non-normalized/quadratic_time_weight/training_config.txt deleted file mode 100644 index 425fcb0..0000000 --- a/training/old/non-normalized/quadratic_time_weight/training_config.txt +++ /dev/null @@ -1,39 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: -def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] - desired_theta = state_traj[:, :, 3] - - # Make the time weights broadcastable for theta [len(t_span), len(batches)] - time_weights = (t_span ** 2).view(-1, 1) - - return 1e3 * torch.mean(time_weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/non-normalized/quadratic_time_weight/training_log.csv b/training/old/non-normalized/quadratic_time_weight/training_log.csv deleted file mode 100644 index 2f44b7c..0000000 --- a/training/old/non-normalized/quadratic_time_weight/training_log.csv +++ /dev/null @@ -1,5002 +0,0 @@ -Epoch,Loss,Elapsed Time (s) -0,69411304.0,3.16279935836792 -1,25122426.0,1.4662237167358398 -2,17984212.0,1.453263521194458 -3,3202049.75,1.5698456764221191 -4,1145678.75,1.4790716171264648 -5,576381.75,1.5358550548553467 -6,415802.21875,1.590977430343628 -7,412789.1875,1.5077762603759766 -8,385609.125,1.5037009716033936 -9,318881.40625,1.4890923500061035 -10,291000.84375,1.5776374340057373 -11,349153.90625,1.4942121505737305 -12,337103.15625,1.488755464553833 -13,329204.53125,1.5537374019622803 -14,322571.90625,2.434634208679199 -15,315189.375,3.3576526641845703 -16,307509.125,1.690124273300171 -17,300426.71875,1.505688190460205 -18,294216.375,1.893738031387329 -19,288196.9375,1.510542631149292 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self.net = nn.Sequential( - nn.Linear(4, 64), - nn.ReLU(), - nn.Linear(64, 64), - nn.ReLU(), - nn.Linear(64, 1) - ) - - def forward(self, x): - raw_torque = self.net(x) - return torch.clamp(raw_torque, -250, 250) \ No newline at end of file diff --git a/training/old/normalized/PendulumDynamics.py b/training/old/normalized/PendulumDynamics.py deleted file mode 100644 index 806d3e6..0000000 --- a/training/old/normalized/PendulumDynamics.py +++ /dev/null @@ -1,26 +0,0 @@ -import torch -import torch.nn as nn - -class PendulumDynamics(nn.Module): - def __init__(self, controller, m:'float'=1, R:'float'=1, g:'float'=9.81): - super().__init__() - self.controller = controller - self.m: 'float' = m - self.R: 'float' = R - self.g: 'float' = g - - def forward(self, t, state): - # Get the current values from the state - theta, omega, alpha, desired_theta = state[:, 0], state[:, 1], state[:, 2], state[:, 3] - - # Make the input stack for the controller - input = torch.stack([theta, omega, alpha, desired_theta], dim=1) - - # Get the torque (the output of the neural network) - tau = self.controller(input).squeeze(-1) - - # Relax alpha - alpha_desired = (self.g / self.R) * torch.sin(theta) + tau / (self.m * self.R**2) - dalpha = alpha_desired - alpha - - return torch.stack([omega, alpha, dalpha, torch.zeros_like(desired_theta)], dim=1) \ No newline at end of file diff --git a/training/old/normalized/__pycache__/PendulumController.cpython-310.pyc b/training/old/normalized/__pycache__/PendulumController.cpython-310.pyc deleted file mode 100644 index 2ae49c9..0000000 Binary files a/training/old/normalized/__pycache__/PendulumController.cpython-310.pyc and /dev/null differ diff --git a/training/old/normalized/__pycache__/PendulumDynamics.cpython-310.pyc b/training/old/normalized/__pycache__/PendulumDynamics.cpython-310.pyc deleted file mode 100644 index 369dbf0..0000000 Binary files a/training/old/normalized/__pycache__/PendulumDynamics.cpython-310.pyc and /dev/null differ diff --git a/training/old/normalized/__pycache__/initial_conditions.cpython-310.pyc b/training/old/normalized/__pycache__/initial_conditions.cpython-310.pyc deleted file mode 100644 index 2e7c47d..0000000 Binary files a/training/old/normalized/__pycache__/initial_conditions.cpython-310.pyc and /dev/null differ diff --git a/training/old/normalized/average_normalized/exponential/training_log.csv b/training/old/normalized/average_normalized/exponential/training_log.csv deleted file mode 100644 index 9c7f869..0000000 --- a/training/old/normalized/average_normalized/exponential/training_log.csv +++ /dev/null @@ -1,1002 +0,0 @@ -Epoch,Loss -0,1732.9542236328125 -1,2224.334716796875 -2,421.18426513671875 -3,196.14459228515625 -4,160.54200744628906 -5,109.44989776611328 -6,77.70399475097656 -7,65.79100036621094 -8,62.53475570678711 -9,58.31625747680664 -10,56.44154357910156 -11,44.9597282409668 -12,41.1190185546875 -13,40.86893081665039 -14,41.32926559448242 -15,41.91762924194336 -16,42.982608795166016 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--git a/training/old/normalized/average_normalized_trainer.py b/training/old/normalized/average_normalized_trainer.py deleted file mode 100644 index 847d961..0000000 --- a/training/old/normalized/average_normalized_trainer.py +++ /dev/null @@ -1,155 +0,0 @@ -import torch -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import os -import shutil -import csv -import inspect - -from PendulumController import PendulumController -from PendulumDynamics import PendulumDynamics - -# Device setup -device = torch.device("cpu") - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -from initial_conditions import initial_conditions -state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device) - -# Device setup -device = torch.device("cpu") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Specify directory for storing results -output_dir = "average_normalized" -os.makedirs(output_dir, exist_ok=True) - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 1001 - -# Define loss functions -def make_loss_fn(weight_fn): - def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] # Size: [batch_size, t_points] - desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points] - weights = weight_fn(t_span) # Initially Size: [t_points] - - # Reshape or expand weights to match theta dimensions - weights = weights.view(-1, 1) # Now Size: [batch_size, t_points] - - # Calculate the weighted loss - return torch.mean(weights * (theta - desired_theta) ** 2) - - return loss_fn - -# Define and store weight functions with descriptions, normalized by average weight -weight_functions = { - 'constant': { - 'function': lambda t: torch.ones_like(t) / torch.ones_like(t).mean(), - 'description': 'Constant weight: All weights are 1, normalized by the average (remains 1)' - }, - 'linear': { - 'function': lambda t: (t / t.max()) / (t / t.max()).mean(), - 'description': 'Linear weight: Weights increase linearly from 0 to 1, normalized by the average weight' - }, - 'quadratic': { - 'function': lambda t: ((t / t.max()) ** 2) / ((t / t.max()) ** 2).mean(), - 'description': 'Quadratic weight: Weights increase quadratically from 0 to 1, normalized by the average weight' - }, - 'exponential': { - 'function': lambda t: (torch.exp(t / t.max() * 2)) / (torch.exp(t / t.max() * 2)).mean(), - 'description': 'Exponential weight: Weights increase exponentially, normalized by the average weight' - }, - 'inverse': { - 'function': lambda t: (1 / (t / t.max() + 1)) / (1 / (t / t.max() + 1)).mean(), - 'description': 'Inverse weight: Weights decrease inversely, normalized by the average weight' - }, - 'inverse_squared': { - 'function': lambda t: (1 / ((t / t.max() + 1) ** 2)) / (1 / ((t / t.max() + 1) ** 2)).mean(), - 'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by the average weight' - } -} - -# Training loop for each weight function -for name, weight_info in weight_functions.items(): - controller = PendulumController().to(device) - pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device) - optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - loss_fn = make_loss_fn(weight_info['function']) - - # File paths - function_output_dir = os.path.join(output_dir, name) - controllers_dir = os.path.join(function_output_dir, "controllers") - - # Check if controllers directory exists and remove it - if os.path.exists(controllers_dir): - shutil.rmtree(controllers_dir) - os.makedirs(controllers_dir, exist_ok=True) - - config_file = os.path.join(function_output_dir, "training_config.txt") - log_file = os.path.join(function_output_dir, "training_log.csv") - - # Overwrite configuration and log files - with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss"]) - - # Training loop - for epoch in range(num_epochs): - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - loss.backward() - optimizer.step() - - # Logging - with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow([epoch, loss.item()]) - - # Save the model - model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - print(f"{model_file} saved with loss: {loss}") - -print("Training complete. Models and logs are saved under respective directories for each loss function.") \ No newline at end of file diff --git a/training/old/normalized/initial_conditions.py b/training/old/normalized/initial_conditions.py deleted file mode 100644 index 795ecae..0000000 --- a/training/old/normalized/initial_conditions.py +++ /dev/null @@ -1,27 +0,0 @@ -import torch -from torch import pi - -initial_conditions = [ - [1/6 * pi, 0.0, 0.0, 0.0], - [-1/6 * pi, 0.0, 0.0, 0.0], - [2/3 * pi, 0.0, 0.0, 0.0], - [-2/3 * pi, 0.0, 0.0, 0.0], - [0.0, 1/3 * pi, 0.0, 0.0], - [0.0, -1/3 * pi, 0.0, 0.0], - [0.0, 2 * pi, 0.0, 0.0], - [0.0, -2 * pi, 0.0, 0.0], - [0.0, 0.0, 0.0, 2 * pi], - [0.0, 0.0, 0.0, -2 * pi], - [0.0, 0.0, 0.0, 1/2 * pi], - [0.0, 0.0, 0.0, -1/2 * pi], - [0.0, 0.0, 0.0, 1/3 * pi], - [0.0, 0.0, 0.0, -1/3 * pi], - [1/4 * pi, 1 * pi, 0.0, 0.0], - [-1/4 * pi, -1 * pi, 0.0, 0.0], - [1/2 * pi, -1 * pi, 0.0, 1/3 * pi], - [-1/2 * pi, 1 * pi, 0.0, -1/3 * pi], - [1/4 * pi, 1 * pi, 0.0, 2 * pi], - [-1/4 * pi, -1 * pi, 0.0, 2 * pi], - [1/2 * pi, -1 * pi, 0.0, 4 * pi], - [-1/2 * pi, 1 * pi, 0.0, -4 * pi], -] \ No newline at end of file diff --git a/training/old/normalized/max_normalized/constant/training_log.csv b/training/old/normalized/max_normalized/constant/training_log.csv deleted file mode 100644 index 6ffd862..0000000 --- a/training/old/normalized/max_normalized/constant/training_log.csv +++ /dev/null @@ -1,1001 +0,0 @@ -Epoch,Loss -0,1054.400634765625 -1,761.4313354492188 -2,547.9044799804688 -3,227.15199279785156 -4,148.319580078125 -5,90.2012939453125 -6,66.56349182128906 -7,59.21733093261719 -8,57.360450744628906 -9,45.62622833251953 -10,40.03177261352539 -11,37.519203186035156 -12,34.1077766418457 -13,26.68117904663086 -14,21.619043350219727 -15,20.32463264465332 -16,17.90155601501465 -17,16.429712295532227 -18,14.186295509338379 -19,13.313838005065918 -20,12.446106910705566 -21,11.496231079101562 -22,9.760787963867188 -23,9.12032699584961 -24,8.557716369628906 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+++ /dev/null @@ -1,41 +0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: - def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] # Size: [batch_size, t_points] - desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points] - weights = weight_fn(t_span) # Initially Size: [t_points] - - # Reshape or expand weights to match theta dimensions - weights = weights.view(-1, 1) # Now Size: [batch_size, t_points] - - # Calculate the weighted loss - return torch.mean(weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/normalized/max_normalized/inverse/training_log.csv b/training/old/normalized/max_normalized/inverse/training_log.csv deleted file mode 100644 index db32781..0000000 --- a/training/old/normalized/max_normalized/inverse/training_log.csv +++ /dev/null @@ -1,1001 +0,0 @@ -Epoch,Loss 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+0,0 @@ -Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: - def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] # Size: [batch_size, t_points] - desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points] - weights = weight_fn(t_span) # Initially Size: [t_points] - - # Reshape or expand weights to match theta dimensions - weights = weights.view(-1, 1) # Now Size: [batch_size, t_points] - - # Calculate the weighted loss - return torch.mean(weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/normalized/max_normalized/inverse_squared/training_log.csv b/training/old/normalized/max_normalized/inverse_squared/training_log.csv deleted file mode 100644 index 1aa8af5..0000000 --- a/training/old/normalized/max_normalized/inverse_squared/training_log.csv +++ /dev/null @@ -1,1001 +0,0 @@ -Epoch,Loss 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Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: - def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] # Size: [batch_size, t_points] - desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points] - weights = weight_fn(t_span) # Initially Size: [t_points] - - # Reshape or expand weights to match theta dimensions - weights = weights.view(-1, 1) # Now Size: [batch_size, t_points] - - # Calculate the weighted loss - return torch.mean(weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/normalized/max_normalized/linear/training_log.csv b/training/old/normalized/max_normalized/linear/training_log.csv deleted file mode 100644 index 37206cb..0000000 --- a/training/old/normalized/max_normalized/linear/training_log.csv +++ /dev/null @@ -1,1001 +0,0 @@ -Epoch,Loss -0,805.1015625 -1,417.7646484375 -2,62.550086975097656 -3,26.70936393737793 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-Random Seed: 4529 -Time Span: 0 to 10, Points: 1000 -Learning Rate: 0.1 -Weight Decay: 0.0001 - -Loss Function: - def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] # Size: [batch_size, t_points] - desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points] - weights = weight_fn(t_span) # Initially Size: [t_points] - - # Reshape or expand weights to match theta dimensions - weights = weights.view(-1, 1) # Now Size: [batch_size, t_points] - - # Calculate the weighted loss - return torch.mean(weights * (theta - desired_theta) ** 2) - -Training Cases: -[theta0, omega0, alpha0, desired_theta] -[0.5235987901687622, 0.0, 0.0, 0.0] -[-0.5235987901687622, 0.0, 0.0, 0.0] -[2.094395160675049, 0.0, 0.0, 0.0] -[-2.094395160675049, 0.0, 0.0, 0.0] -[0.0, 1.0471975803375244, 0.0, 0.0] -[0.0, -1.0471975803375244, 0.0, 0.0] -[0.0, 6.2831854820251465, 0.0, 0.0] -[0.0, -6.2831854820251465, 0.0, 0.0] -[0.0, 0.0, 0.0, 6.2831854820251465] -[0.0, 0.0, 0.0, -6.2831854820251465] -[0.0, 0.0, 0.0, 1.5707963705062866] -[0.0, 0.0, 0.0, -1.5707963705062866] -[0.0, 0.0, 0.0, 1.0471975803375244] -[0.0, 0.0, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 0.0] -[-0.7853981852531433, -3.1415927410125732, 0.0, 0.0] -[1.5707963705062866, -3.1415927410125732, 0.0, 1.0471975803375244] -[-1.5707963705062866, 3.1415927410125732, 0.0, -1.0471975803375244] -[0.7853981852531433, 3.1415927410125732, 0.0, 6.2831854820251465] -[-0.7853981852531433, -3.1415927410125732, 0.0, 6.2831854820251465] -[1.5707963705062866, -3.1415927410125732, 0.0, 12.566370964050293] -[-1.5707963705062866, 3.1415927410125732, 0.0, -12.566370964050293] diff --git a/training/old/normalized/max_normalized/quadratic/training_log.csv b/training/old/normalized/max_normalized/quadratic/training_log.csv deleted file mode 100644 index 28798d4..0000000 --- a/training/old/normalized/max_normalized/quadratic/training_log.csv +++ /dev/null @@ -1,1001 +0,0 @@ -Epoch,Loss -0,669.622802734375 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-996,0.030686872079968452 -997,0.030654583126306534 -998,0.030622314661741257 -999,0.030590124428272247 diff --git a/training/old/normalized/max_normalized_trainer.py b/training/old/normalized/max_normalized_trainer.py deleted file mode 100644 index 72f3730..0000000 --- a/training/old/normalized/max_normalized_trainer.py +++ /dev/null @@ -1,155 +0,0 @@ -import torch -import torch.optim as optim -from torchdiffeq import odeint -import numpy as np -import os -import shutil -import csv -import inspect - -from PendulumController import PendulumController -from PendulumDynamics import PendulumDynamics - -# Device setup -device = torch.device("cpu") - -# Initial conditions (theta0, omega0, alpha0, desired_theta) -from initial_conditions import initial_conditions -state_0 = torch.tensor(initial_conditions, dtype=torch.float32, device=device) - -# Device setup -device = torch.device("cpu") - -# Constants -m = 10.0 -g = 9.81 -R = 1.0 - -# Time grid -t_start, t_end, t_points = 0, 10, 1000 -t_span = torch.linspace(t_start, t_end, t_points, device=device) - -# Specify directory for storing results -output_dir = "training" -os.makedirs(output_dir, exist_ok=True) - -# Use a previously generated random seed -random_seed = 4529 - -# Set the seeds for reproducibility -torch.manual_seed(random_seed) -np.random.seed(random_seed) - -# Print the chosen random seed -print(f"Random seed for torch and numpy: {random_seed}") - -# Initialize controller and dynamics -controller = PendulumController().to(device) -pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device) - -# Optimizer setup -learning_rate = 1e-1 -weight_decay = 1e-4 -optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - -# Training parameters -num_epochs = 1001 - -# Define loss functions -def make_loss_fn(weight_fn): - def loss_fn(state_traj, t_span): - theta = state_traj[:, :, 0] # Size: [batch_size, t_points] - desired_theta = state_traj[:, :, 3] # Size: [batch_size, t_points] - weights = weight_fn(t_span) # Initially Size: [t_points] - - # Reshape or expand weights to match theta dimensions - weights = weights.view(-1, 1) # Now Size: [batch_size, t_points] - - # Calculate the weighted loss - return torch.mean(weights * (theta - desired_theta) ** 2) - - return loss_fn - -# Define and store weight functions with descriptions -weight_functions = { - 'constant': { - 'function': lambda t: torch.ones_like(t) / torch.ones_like(t).max(), - 'description': 'Constant weight: All weights are 1, normalized by the max (remains 1)' - }, - 'linear': { - 'function': lambda t: (t / t.max()) / (t / t.max()).max(), - 'description': 'Linear weight: Weights increase linearly from 0 to 1, normalized by the max weight' - }, - 'quadratic': { - 'function': lambda t: ((t / t.max()) ** 2) / ((t / t.max()) ** 2).max(), - 'description': 'Quadratic weight: Weights increase quadratically from 0 to 1, normalized by the max weight' - }, - 'exponential': { - 'function': lambda t: (torch.exp(t / t.max() * 2)) / (torch.exp(t / t.max() * 2)).max(), - 'description': 'Exponential weight: Weights increase exponentially, normalized by the max weight' - }, - 'inverse': { - 'function': lambda t: (1 / (t / t.max() + 1)) / (1 / (t / t.max() + 1)).max(), - 'description': 'Inverse weight: Weights decrease inversely, normalized by the max weight' - }, - 'inverse_squared': { - 'function': lambda t: (1 / ((t / t.max() + 1) ** 2)) / (1 / ((t / t.max() + 1) ** 2)).max(), - 'description': 'Inverse squared weight: Weights decrease inversely squared, normalized by the max weight' - } -} - -# Training loop for each weight function -for name, weight_info in weight_functions.items(): - controller = PendulumController().to(device) - pendulum_dynamics = PendulumDynamics(controller, m, R, g).to(device) - optimizer = optim.Adam(controller.parameters(), lr=learning_rate, weight_decay=weight_decay) - loss_fn = make_loss_fn(weight_info['function']) - - # File paths - function_output_dir = os.path.join(output_dir, name) - controllers_dir = os.path.join(function_output_dir, "controllers") - - # Check if controllers directory exists and remove it - if os.path.exists(controllers_dir): - shutil.rmtree(controllers_dir) - os.makedirs(controllers_dir, exist_ok=True) - - config_file = os.path.join(function_output_dir, "training_config.txt") - log_file = os.path.join(function_output_dir, "training_log.csv") - - # Overwrite configuration and log files - with open(config_file, "w") as f: - f.write(f"Random Seed: {random_seed}\n") - f.write(f"Time Span: {t_start} to {t_end}, Points: {t_points}\n") - f.write(f"Learning Rate: {learning_rate}\n") - f.write(f"Weight Decay: {weight_decay}\n") - f.write("\nLoss Function:\n") - f.write(inspect.getsource(loss_fn)) - f.write("\nTraining Cases:\n") - f.write("[theta0, omega0, alpha0, desired_theta]\n") - for case in state_0.cpu().numpy(): - f.write(f"{case.tolist()}\n") - - with open(log_file, "w", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow(["Epoch", "Loss"]) - - # Training loop - for epoch in range(num_epochs): - optimizer.zero_grad() - state_traj = odeint(pendulum_dynamics, state_0, t_span, method='rk4') - loss = loss_fn(state_traj, t_span) - loss.backward() - optimizer.step() - - # Logging - with open(log_file, "a", newline="") as csvfile: - csv_writer = csv.writer(csvfile) - csv_writer.writerow([epoch, loss.item()]) - - # Save the model - model_file = os.path.join(controllers_dir, f"controller_{epoch}.pth") - torch.save(controller.state_dict(), model_file) - print(f"{model_file} saved with loss: {loss}") - -print("Training complete. Models and logs are saved under respective directories for each loss function.") \ No newline at end of file