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y_one_hot # gradient for output layer + dw3 = (self.a2.T @ dz3) / m + db3 = np.sum(dz3, axis=0, keepdims=True) / m + + dz2 = (dz3 @ self.W3.T) * self.relu_deriv(self.z2) # gradient for layer 2 + dw2 = (self.a1.T @ dz2) / m + db2 = np.sum(dz2, axis=0, keepdims=True) / m + + dz1 = (dz2 @ self.W2.T) * self.relu_deriv(self.z1) # gradient for layer 1 + dw1 = (self.x.T @ dz1) / m + db1 = np.sum(dz1, axis=0, keepdims=True) / m + + # updates weights and biases using gradient descent + self.W3 -= lr * dw3 + self.b3 -= lr * db3 + self.W2 -= lr * dw2 + self.b2 -= lr * db2 + self.W1 -= lr * dw1 + self.b1 -= lr * db1 + + @staticmethod + def relu(x): + # ReLU activation + return np.maximum(0, x) + + @staticmethod + def relu_deriv(x): + # derivation of ReLU activation for backpropagation + return (x > 0).astype(float) + + @staticmethod + def softmax(x): + # softmax function normalizes outputs to probabilities + e_x = np.exp(x - np.max(x, axis=1, keepdims=True)) # exponentiates inputs + return e_x / np.sum(e_x, axis=1, keepdims=True) # normalizes to get probabilities + + @staticmethod + def one_hot_encode(y, num_classes): + # converts labels to one-hot encoded format + return np.eye(num_classes)[y] + + @staticmethod + def cross_entropy_loss(y, y_hat): + # computes cross-entropy loss between true labels and predicted probabilities + m = y.shape[0] + m = y.shape[0] + eps = 1e-12 + y_hat_clipped = np.clip(y_hat, eps, 1. - eps) + log_probs = -np.log(y_hat_clipped[np.arange(m), y]) + return np.mean(log_probs) + + def fit(self, x_train, y_train, x_val, y_val, lr, epochs, batch_size): + train_losses = [] + val_accuracies = [] + + for epoch in range(1, epochs + 1): + perm = np.random.permutation(x_train.shape[0]) # Shuffle the training data + x_train_shuffled, y_train_shuffled = x_train[perm], y_train[perm] + + epoch_loss = 0.0 + num_batches = int(np.ceil(x_train.shape[0] / batch_size)) + + for i in range(num_batches): + start = i * batch_size + end = start + batch_size + x_batch = x_train_shuffled[start:end] # batch of inputs + y_batch = y_train_shuffled[start:end] # batch of labels + + # Forward pass, backward pass, and weight update + self.forward(x_batch) + self.backward(y_batch, lr) + + epoch_loss += self.cross_entropy_loss(y_batch, self.a3) # updating the epoch loss + + epoch_loss /= num_batches # average loss is defined + train_losses.append(epoch_loss) + + val_pred = self.predict(x_val) + val_acc = np.mean(val_pred == y_val) + val_accuracies.append(val_acc) \ + + print(f"Epoch {epoch:02d} | Training Loss: {epoch_loss:.4f} | Value Accuracy: {val_acc:.4f}") + + self.plot_graph(train_losses, val_accuracies) + return val_accuracies[-1] + + def plot_graph(self, train_losses, val_accuracies): + if not os.path.exists('results'): + os.makedirs('results') # creates results director + + fig, ax1 = plt.subplots() # initializes the plot + + ax1.set_xlabel('Epochs') + ax1.set_ylabel('Training Loss', color='tab:blue') + ax1.plot(range(1, len(train_losses) + 1), train_losses, color='tab:blue', label='Training Loss') + ax1.tick_params(axis='y', labelcolor='tab:blue') # defines loss subplot + + ax2 = ax1.twinx() + ax2.set_ylabel('Validation Accuracy', color='tab:orange') + ax2.plot(range(1, len(val_accuracies) + 1), val_accuracies, color='tab:orange', label='Validation Accuracy') + ax2.tick_params(axis='y', labelcolor='tab:orange') # defines accuracy subplot + + plt.title('Training Loss and Validation Accuracy over Epochs') + + result_path = 'results/hyperparameter-training-output.png' # defines the file name + fig.savefig(result_path) + print(f"Graph saved to: {result_path}") + + def predict(self, x): # predicts class labels for the input data + probs = self.forward(x) # forwards pass to get probabilities + return np.argmax(probs, axis=1) # returns the class with highest probability + + # acquiring the FashionMNIST dataset + train_set = datasets.FashionMNIST(root='.', train=True, download=True) + test_set = datasets.FashionMNIST(root='.', train=False, download=True) + + # preprocessing the data by flattening images and normalizing them. + x_train = train_set.data.numpy().reshape(-1, 28 * 28).astype(np.float32) / 255.0 + y_train = train_set.targets.numpy() + + x_test = test_set.data.numpy().reshape(-1, 28 * 28).astype(np.float32) / 255.0 + y_test = test_set.targets.numpy() + + # MLP initialization + mlp = MLP( + input_size=28 * 28, + hidden_size1=256, + hidden_size2=256, + output_size=10, + weight_scale=1e-2 + ) + + # trains the model + mlp.fit( + x_train=x_train, + y_train=y_train, + x_val=x_test, + y_val=y_test, + lr=learn_rate, + epochs=100, + batch_size=batch_sizes + ) + + # tests the model + test_pred = mlp.predict(x_test) + test_acc = np.mean(test_pred == y_test) + print("Test:" + str(learn_rate) + " "+ str(batch_sizes)) + print(f"\nFinal test accuracy: {test_acc:.4f}") \ No newline at end of file diff --git a/results/experiment-8.png b/results/experiment-8.png index c8cc6d7..0365ed5 100644 Binary files a/results/experiment-8.png and b/results/experiment-8.png differ diff --git a/results/hyperparameter-training-output.png b/results/hyperparameter-training-output.png new file mode 100644 index 0000000..c2fb3a2 Binary files /dev/null and b/results/hyperparameter-training-output.png differ