Part 3 : ann

knn.py

@@ -41,6 +41,7 @@ def test_knn() :
     X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
     
     for k in k_indices :
+        print(k)
         accuracy = evaluate_knn(X_train, y_train, X_test, y_test, k)
         accuracies.append(accuracy)
     
@@ -50,9 +51,9 @@ def test_knn() :
     plt.plot(k_indices, accuracies)
     plt.title("Accuracy as function of k")
     plt.legend()
-    plt.show()
     plt.savefig('/Users/milancart/Documents/GitHub/image-classification/result/knn.png')
-
+    plt.show()
+    plt.close()
 
 if __name__ == "__main__":
     

mlp.py

+import numpy as np
+import read_cifar
+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(data) #number of training examples
+    # Forward pass
+    a0 = data # the data are the input of the first layer
+    z1 = np.dot(a0, w1) + b1  # input of the hidden layer
+    a1 = sigmoid(z1)  # output of the hidden layer (sigmoid activation function)
+    z2 = np.dot(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
+    
+    # Backpropagation
+    delta_a2 = 2 / N_out * (a2 - targets)
+    delta_z2 = delta_a2 * (a2 * (1 - a2))  # 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) 
+    delta_b2 = np.sum(delta_z2, axis = 0, keepdims = True) 
+
+    delta_a1 = np.dot(delta_z2, w2.T)
+    delta_z1 = delta_a1 * (a1 * (1 - a1))
+    delta_w1 = np.dot(a0.T, delta_z1) 
+    delta_b1 = np.sum(delta_z1, 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) 
+
+  
+    a0 = data 
+    z1 = np.matmul(a0, w1) + b1  
+    a1 = sigmoid(z1)  
+    z2 = np.matmul(a1, w2) + b2  
+    a2 = softmax_stable(z2) 
+    predictions = a2  
+
+    y_true_one_hot = one_hot(labels_train)
+    loss = cross_entropy_loss(predictions, y_true_one_hot)
+
+   
+    delta_z2 = (a2 - y_true_one_hot) 
+    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
+
+    
+   
+    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):
+    
+    a0 = data 
+    z1 = np.matmul(a0, w1) + b1  
+    a1 = sigmoid(z1)  
+    z2 = np.matmul(a1, w2) + b2 
+    a2 = softmax_stable(z2)
+    predictions = a2
+    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
+
+    #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.savefig('/Users/milancart/Documents/GitHub/image-classification/results/mlp.png')
+    plt.show()
+    plt.close()
+
+
+
+if __name__ == '__main__':
+    data, labels = read_cifar.read_cifar('/Users/milancart/Documents/GitHub/image-classification/Data/cifar-10-batches-py')
+    X_train, X_test, y_train, y_test = read_cifar.split_dataset(data, labels, 0.9)
+    d_in, d_h, d_out = 3072, 64, 10
+    learning_rate = 0.1
+    num_epoch = 100
+
+    plot_graph(X_train, y_train, X_test ,y_test , d_h, learning_rate, num_epoch)
+    
+  
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