diff --git a/mlp.py b/mlp.py
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+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 11 21:10:52 2024
+
+@author: danjo
+"""
+import numpy as np
+from read_cifar import *
+import matplotlib.pyplot as plt
+
+
+def sigmoid(x):
+ return 1 / (1 + np.exp(-x))
+
+
+def learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate):
+
+ N_out = len(targets) #number of training examples
+
+ # Forward pass
+ a0 = data # the data are the input of the first layer
+ z1 = np.matmul(a0, w1) + b1 # input of the hidden layer
+ a1 = sigmoid(z1) # output of the hidden layer (sigmoid activation function)
+ z2 = np.matmul(a1, w2) + b2 # input of the output layer
+ a2 = sigmoid(z2) # output of the output layer (sigmoid activation function)
+ predictions = a2 # the predicted values are the outputs of the output layer
+
+ # Compute loss (MSE)
+ loss = np.mean(np.square(predictions - targets))
+ print(f'loss: {loss}')
+ print('shape a1', a1.shape)
+ print('shape w1', w1.shape)
+ print('shape b1', b1.shape)
+
+ print('shape a2', a2.shape)
+ print('shape w2', w2.shape)
+ print('shape b2', b2.shape)
+
+ # Backpropagation
+ delta_a2 = 2 / N_out * (a2 - targets)
+ print('shape delta_a2', delta_a2.shape)
+ delta_z2 = delta_a2 * (a2 * (1 - a2))
+ print('shape delta_z2', delta_z2.shape)
+ delta_w2 = np.dot(a1.T, delta_z2)
+ print('shape delta_w2', delta_w2.shape)
+ delta_b2 = delta_z2
+
+ delta_a1 = np.dot(delta_z2, w2.T)
+ print('shape delta_a1', delta_a1.shape)
+ delta_z1 = delta_a1 * (a1 * (1- a1))
+ print('shape delta_z1', delta_z1.shape)
+ delta_w1 = np.dot(a0.T, delta_z1)
+ print('shape delta_w1', delta_w2.shape)
+ delta_b1 = delta_z1
+
+ # Update weights and biases
+ w2 -= learning_rate * delta_w2
+ b2 -= learning_rate * np.sum(delta_b2, axis = 0, keepdims = True)
+
+ w1 -= learning_rate * delta_w1
+ b1 -= learning_rate * np.sum(delta_b1, axis = 0, keepdims = True)
+
+ return w1, b1, w2, b2, loss
+
+def one_hot(labels):
+ num_classes = int(np.max(labels) + 1) #num_classes = 10
+ one_hot_matrix = np.eye(num_classes)[labels]
+ return one_hot_matrix
+
+def softmax_stable(x):
+ #We use this function to avoid computing big numbers
+ return(np.exp(x - np.max(x, axis=1, keepdims=True)) / np.exp(x - np.max(x, axis=1, keepdims=True)).sum())
+
+def cross_entropy_loss(y_pred, y_true_one_hot):
+ epsilon = 1e-10
+ loss = - np.sum( y_true_one_hot * np.log(y_pred + epsilon) ) / len(y_pred)
+ return loss
+
+
+def learn_once_cross_entropy(w1, b1, w2, b2, data, labels_train, learning_rate):
+
+ N_out = len(data) #number of training examples
+
+ # Forward pass
+ a0 = data # the data are the input of the first layer
+ z1 = np.matmul(a0, w1) + b1 # input of the hidden layer
+ a1 = sigmoid(z1) # output of the hidden layer (sigmoid activation function)
+ z2 = np.matmul(a1, w2) + b2 # input of the output layer
+ a2 = softmax_stable(z2) # output of the output layer (sigmoid activation function)
+ predictions = a2 # the predicted values are the outputs of the output layer
+ # print('a0', a0[:2])
+ # print('w1', w1[:2])
+ # print('z1', z1[:2])
+ # print('a1', a1[:2])
+ # print('z2', z2[:2])
+ # print('a2', a2[:2])
+
+ # Compute loss (cross-entropy loss)
+ y_true_one_hot = one_hot(labels_train)
+ loss = cross_entropy_loss(predictions, y_true_one_hot)
+
+ # Backpropagation
+ delta_z2 = (a2 - y_true_one_hot) # We divide by the sample size to have an average on the error and avoid big gradient jumps
+ delta_w2 = np.dot(a1.T, delta_z2) / N_out
+ delta_b2 = np.sum(delta_z2, axis = 0, keepdims = True) / N_out
+
+ delta_a1 = np.dot(delta_z2, w2.T)
+ delta_z1 = delta_a1 * (a1 * (1 - a1)) / N_out
+ delta_w1 = np.dot(a0.T, delta_z1) / N_out
+ delta_b1 = np.sum(delta_z1, axis = 0, keepdims = True) / N_out
+
+
+ # Update weights and biases
+ w1 -= learning_rate * delta_w1
+ b1 -= learning_rate * delta_b1
+ w2 -= learning_rate * delta_w2
+ b2 -= learning_rate * delta_b2
+
+ return w1, b1, w2, b2, loss
+
+
+def forward(w1, b1, w2, b2, data):
+ # Forward pass
+ a0 = data # the data are the input of the first layer
+ z1 = np.matmul(a0, w1) + b1 # input of the hidden layer
+ a1 = sigmoid(z1) # output of the hidden layer (sigmoid activation function)
+ z2 = np.matmul(a1, w2) + b2 # input of the output layer
+ a2 = softmax_stable(z2) # output of the output layer (sigmoid activation function)
+ predictions = a2 # the predicted values are the outputs of the output layer
+ return(predictions)
+
+def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch):
+ train_accuracies = []
+ for epoch in range(num_epoch):
+ w1, b1, w2, b2, loss = learn_once_cross_entropy(w1, b1, w2, b2, data_train, labels_train, learning_rate)
+
+ # Compute accuracy
+ predictions = forward(w1, b1, w2, b2, data_train)
+ predicted_labels = np.argmax(predictions, axis=1)
+ accuracy = np.mean(predicted_labels == labels_train)
+ train_accuracies.append(accuracy)
+
+ print(f'Epoch {epoch + 1}/{num_epoch}, Loss: {loss:.3f}, Train Accuracy: {accuracy:.5f}')
+
+ return w1, b1, w2, b2, train_accuracies
+
+def test_mlp(w1, b1, w2, b2, data_test, labels_test):
+
+ # Compute accuracy
+ predictions = forward(w1, b1, w2, b2, data_test)
+ predicted_labels = np.argmax(predictions, axis=1)
+ test_accuracy = np.mean(predicted_labels == labels_test)
+ print(f'Test Accuracy: {test_accuracy:.2f}')
+ return test_accuracy
+
+def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epoch):
+
+ d_in = data_train.shape[1]
+ d_out = 10 #we can hard code it here or len(np.unique(label_train))
+
+ #Random initialisation of weights Xavier initialisation
+ w1 = np.random.randn(d_in, d_h) / np.sqrt(d_in)
+ b1 = np.zeros((1, d_h))
+ w2 = np.random.randn(d_h, d_out) / np.sqrt(d_h)
+ b2 = np.zeros((1, d_out))
+
+ # Train MLP
+ w1, b1, w2, b2, train_accuracies = train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch)
+
+ # Test MLP
+ test_accuracy = test_mlp(w1, b1, w2, b2, data_test, labels_test)
+ return train_accuracies, test_accuracy
+
+def plot_graph(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epoch):
+ # Run MLP training
+ train_accuracies, test_accuracy = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epoch)
+
+ # Plot and save the learning accuracy graph
+ plt.figure(figsize=(8, 6))
+ epochs = np.arange(1, num_epoch + 1)
+ plt.plot(epochs, train_accuracies, marker='x', color='b', label='Train Accuracy')
+ plt.xlabel('Epochs')
+ plt.ylabel('Accuracy')
+ plt.title('MLP Train Accuracy')
+ plt.legend()
+ plt.grid(True)
+ plt.savefig(r'C:\Users\danjo\Documents\GitHub\image-classification\results')
+ plt.show()
+ return()
+
+
+if __name__ == "__main__":
+ path = r'data\cifar-10-batches-py\data_batch_1'
+ main_path = r'data\cifar-10-batches-py'
+ data, labels = read_cifar_batch(path)
+ data, labels = read_cifar(main_path)
+ data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
+
+ d_in, d_h, d_out = 3072, 64, 10
+ learning_rate = 0.1
+ num_epoch = 5
+
+
+ #Initialisation
+ w1 = np.random.randn(d_in, d_h) / np.sqrt(d_in)
+ b1 = np.zeros((1, d_h))
+ w2 = np.random.randn(d_h, d_out) / np.sqrt(d_h)
+ b2 = np.zeros((1, d_out))
+
+ #train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epoch)
+
+ test_mlp(w1, b1, w2, b2, data_test[:50], labels_test[:50])
+
+ plot_graph(data_train, labels_train, data_test, labels_test , d_h, learning_rate, num_epoch)
+
+
+
+
+