import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.optim import SGD import matplotlib.pyplot as plt

training_inputs = torch.tensor(np.load("inputs_training.npy")).float() training_targets = torch.tensor(np.load("targets_training.npy")).float() print("Shape training inputs: ", training_inputs.shape) print("Shape training targets: ", training_targets.shape) test_inputs = torch.tensor(np.load("inputs_test.npy")).float() test_targets = torch.tensor(np.load("targets_test.npy")).float() print(test_inputs.shape) class NeuralNetwork(torch.nn.Module): def __init__(self,x): super(NeuralNetwork, self).__init__() #2D input (like Logistic reg) self.weights = nn.ParameterDict({ 'w11': nn.Parameter(torch.zeros(50, len(x))), 'w12': nn.Parameter(torch.zeros(50, len(x))), 'b1': nn.Parameter(torch.zeros(50)), 'w2': nn.Parameter(torch.zeros(20,50)), 'b2': nn.Parameter(torch.zeros(20)), 'w3': nn.Parameter(torch.zeros(len(x),20)), 'b3': nn.Parameter(torch.zeros(len(x))) }) def forward(self, x): xcol1 = x[:,0] xcol2 = x[:,1] x = self.weights['w11'] @ xcol1 + self.weights['w12'] @ xcol2 + self.weights['b1'] #x = self.linear1(x) x = torch.sigmoid(x) x = self.weights['w2'] @ x + self.weights['b2'] x = torch.sigmoid(x) x = self.weights['w3'] @ x + self.weights['b3'] x = torch.sigmoid(x) return x data = training_inputs targets = training_targets model = NeuralNetwork(data) optimizer = torch.optim.SGD(model.parameters(model.parameters()), lr=1e-3) loss_history = [] for step in range(3000): predictions = model(data) loss = torch.nn.MSELoss(reduction="sum")(predictions, targets) loss_history.append(loss) loss.backward() optimizer.step() optimizer.zero_grad() params = list(model.parameters()) w11 = params[0].detach().numpy() w12 = params[1].detach().numpy() b1 = params[2].detach().numpy() w2 = params[3].detach().numpy() b2 = params[4].detach().numpy() w3 = params[4].detach().numpy() b3 = params[5].detach().numpy() param_dict = {'w11':w11, 'w12':w12, 'b1':b1, 'w2':w2, 'b2':b2, 'w3':w3, 'b3':b3} # Plot loss history new_loss_history = [] for item in loss_history: new_loss_history.append(item.detach().numpy()) fig, ax = plt.subplots() ax.plot(new_loss_history) ax.set_xlabel('Training Epoch') ax.set_ylabel('Sum of Squares Loss') ax.set_title('Sum of Squares Loss Over Training Time') ax.legend('Model Loss') fig.show() # Apply to training data and get classification accuracy p_dict = {} for key in param_dict: p_dict[key] = torch.tensor(param_dict[key]) # Running into an error here because I am not sure how to apply the weights again with the training set def apply_weights(x): xcol1 = x[:,0] xcol2 = x[:,1] x = p_dict['w11'] @ xcol1 + p_dict['w12'] @ xcol2 + p_dict['b1'] x = torch.sigmoid(x) x = p_dict['w2'] @ x + p_dict['b2'] x = torch.sigmoid(x) x = p_dict['w3'] @ x + p_dict['b3'] return torch.sigmoid(x) #predictions = apply_weights(data) #loss torch.mean(torch.abs(predictions - targets)**2)

adj = np.load("adjmat.npy") num_rows, num_col = adj.shape print(num_rows) x = [1, 1, 1] Atilde = np.identity(num_rows) + adj #self loops included print(Atilde[:40, 1]) D = np.zeros_like(Atilde) #initialize D matrix for i in range(num_col): list_col = Atilde[:, i] D[i,i] = np.sum(list_col) #Diagonal matrix of node counts #print(D**-0.5) L = D - Atilde print("Graph Laplacian: \n", L) normalized_L = np.identity(num_rows) - np.linalg.inv(D)**(0.5) @ Atilde @ np.linalg.inv(D)**(0.5) print("Normalized Graph Laplacian: \n", normalized_L)