From 434fd3b0d560932b09d989a2f40950346691ca2e Mon Sep 17 00:00:00 2001
From: "MSI\\alber" <alberto.cavallo@synesthesia.it>
Date: Wed, 8 Nov 2023 11:55:27 +0100
Subject: [PATCH] Final commit

---
 knn.py           |  85 +++++++++--------
 mlp.py           | 243 +++++++++++++++++++++--------------------------
 read_cifar.py    |  28 ++++--
 results/knn.png  | Bin 0 -> 35264 bytes
 results/loss.png | Bin 0 -> 18495 bytes
 results/mlp.png  | Bin 0 -> 28948 bytes
 6 files changed, 172 insertions(+), 184 deletions(-)
 create mode 100644 results/knn.png
 create mode 100644 results/loss.png
 create mode 100644 results/mlp.png

diff --git a/knn.py b/knn.py
index 370e49b..8f7755b 100644
--- a/knn.py
+++ b/knn.py
@@ -1,67 +1,76 @@
 from read_cifar import *
 from collections import Counter
+import matplotlib.pyplot as plt
 
-def compute_distance(m1, m2):
-    if m1.shape != m2.shape:
-        raise ValueError("Dimensions must be identical")
-    #distance = np.linalg.norm(m1 - m2)
-    x = (m1 - m2) ** 2
-    y = np.sum(x)
-    dist = np.sqrt(y)
-
-    return dist
+# Compute the euclidean distance matrix where the rows are the training data and the columns the testing data
+# In the dists[i][j] there is the euclidean distance between the i-data_train image and the j-data_test image
 def distance_matrix(data_train, data_test):
-    dists = []
 
-    for test in data_test:
-        dist = []
-        for train in data_train:
-            dist.append(compute_distance(test, train))
-        dists.append(dist)
+    train_squared = np.sum(data_train ** 2, axis=1, keepdims=True)
+    test_squared = np.sum(data_test ** 2, axis=1, keepdims=True)
+    dot_product = np.dot(data_train, data_test.T)
+    dists = np.sqrt(train_squared - 2 * dot_product + test_squared.T)
+    #print(dists.shape)
 
     return dists
 
 def knn_predict(dists, labels_train, k):
-    predictions=[]
+
+    # we look for the k-images at the minimum distance for each data_test image
+    # and we assign the class with the highest frequency among the k
+    # (I personally prefer having the testing data on the rows)
+    dists=dists.T
+    predictions = []
+
     for distances in dists:
         min_indexes = np.argpartition(distances, k)[:k]
         possible_pred = labels_train[min_indexes]
         counted = Counter(possible_pred)
         pred = counted.most_common(1)[0][0]
         predictions.append(pred)
+
     return predictions
 
-def evaluate_knn(predictions, labels_test):
-    sum=0
-    for i in range(len(predictions)):
-        if predictions[i] == labels_test[i]:
-            sum+=1
+def evaluate_knn(dists, labels_train, labels_test, k):
 
-    return sum / len(predictions)
+    # We apply the knn algorithm and then we compare the predictionswith the labels
+    predictions = knn_predict(dists, labels_train, k)
 
-'''def evaluate_knn(data_train , labels_train,data_test ,labels_test, k):
-        
-    return'''
+    return np.mean(predictions == labels_test)
 
 def main():
-    folder_path = 'data/cifar-10-batches-py'
-    data, labels = read_cifar(folder_path)
 
-    print((data.shape))
-    print((labels.shape))
+    print('#START#')
 
-    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
-
-    print("Training set shape:", data_train.shape, labels_train.shape)
-    print("Testing set shape:", data_test.shape, labels_test.shape)
-
-    dists=distance_matrix(data_train, data_test)
+    # Set hyperparameters
+    num_k = 20
 
-    prediction=knn_predict(dists, labels_train, 4)
+    # Load CIFAR dataset and split the training data and the labels for the two phases(train and test)
+    folder_path = 'data/cifar-10-batches-py'
+    data, labels = read_cifar(folder_path)
 
-    accuracy = evaluate_knn(prediction, labels_test)
+    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, 0.9)
 
-    print(accuracy)
+    # Computation of the distance matrix once
+    dists = distance_matrix(data_train, data_test)
+
+    # Test the knn algorithm at the variation of k
+    accuracies=[]
+    for k in range(num_k):
+        accuracy = evaluate_knn(dists, labels_train, labels_test, k+1)
+        print('For k = ' + str(k) +' accuracy : '+ str(round(accuracy, 4)))
+        accuracies.append(accuracy)
+
+    # Plot the accuracy for each k
+    plt.figure(figsize=(10, 6))
+    x = range(1, num_k + 1)
+    plt.plot(x, accuracies)
+    plt.xlabel('K')
+    plt.ylabel('Accuracy')
+    plt.title('Accuracy evolution')
+    plt.grid()
+    plt.savefig('results/knn.png')
+    plt.show()
 
 if __name__ == "__main__":
     main()
diff --git a/mlp.py b/mlp.py
index 1505d08..c2f9b0e 100644
--- a/mlp.py
+++ b/mlp.py
@@ -1,172 +1,141 @@
 import numpy as np
 import matplotlib.pyplot as plt
-import pylab as pl
-
-def sigmoid(x):
- return 1/(1 + np.exp(-x))
-
-def learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate):
-
-    # 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))
+from read_cifar import *
+
+# Function to code a vector to one-hot encoding
+def one_hot(y):
+    one_hot_matrix = np.zeros((y.shape[0], max(y)+1))
+    for i in range(y.shape[0]):
+        one_hot_matrix[i, y[i]] = 1
+    return one_hot_matrix
+
+# Sigmoid activation function
+def sigmoid(z):
+    return 1 / (1 + np.exp(-z))
+
+# Function to perform one gradient descent step with Binary cross-entropy loss
+def learn_once_cross_entropy(w1, b1, w2, b2, data, labels, learning_rate):
+    m = data.shape[0]  # Batch size
+
+    a0 = data
+    z1 = np.matmul(a0, w1) + b1
+    a1 = sigmoid(z1)
+    z2 = np.matmul(a1, w2) + b2
+    a2 = sigmoid(z2)
+
+    #Compute the Binary Cross-Entropy loss
+    loss = -np.sum(labels * np.log(a2) + (1 - labels) * np.log(1 - a2)) / m
+
+    # To update the weights and biases, we need to calculate the gradients of the loss with respect to each parameter
+    d_a2 = (a2 - labels) / m
+    d_z2 = d_a2
+    d_w2 = np.matmul(a1.T, d_z2)
+    d_b2 = np.sum(d_z2, axis=0, keepdims=True)
+
+    d_a1 = np.matmul(d_z2, w2.T)
+    d_z1 = d_a1 * a1 * (1 - a1)
+    d_w1 = np.matmul(a0.T, d_z1)
+    d_b1 = np.sum(d_z1, axis=0, keepdims=True)
+
+    # Update weights and biases, these serve to reduce the loss during training. As is demonstrated in the plot 'loss.png'
+    w1 -= learning_rate * d_w1
+    b1 -= learning_rate * d_b1
+    w2 -= learning_rate * d_w2
+    b2 -= learning_rate * d_b2
 
     return w1, b1, w2, b2, loss
 
-
-def one_hot(vet):
-    encoded = np.zeros((len(vet), max(vet) + 1), dtype=int)
-    encoded[np.arange(len(vet)), vet]=1
-    return encoded
-
-
-def learn_once_cross_entropy(w1, b1, w2, b2, data, labels_train, learning_rate):
-    # Forward Pass
-    Z1 = np.dot(data, w1) + b1
-    A1 = sigmoid(Z1)
-    Z2 = np.dot(A1, w2) + b2
-    A2 = sigmoid(Z2)
-
-    # Calculate loss (Binary Cross Entropy)
-    m = labels_train.shape[0]
-    epsilon = 1e-15  # small constant to avoid log(0)
-    loss = (-1.0 / m) * np.sum(labels_train * np.log(A2 + epsilon) + (1 - labels_train) * np.log(1 - A2 + epsilon))
-
-    # Backward Pass
-    dZ2 = A2 - labels_train
-    dW2 = (1 / data.shape[0]) * np.dot(A1.T, dZ2)
-    db2 = (1 / data.shape[0]) * np.sum(dZ2, axis=0)
-    dZ1 = np.dot(dZ2, w2.T) * A1 * (1 - A1)
-    dW1 = (1 / data.shape[0]) * np.dot(data.T, dZ1)
-    db1 = (1 / data.shape[0]) * np.sum(dZ1, axis=0)
-
-    # Update weights and biases
-    w1 -= learning_rate * dW1
-    b1 -= learning_rate * db1
-    w2 -= learning_rate * dW2
-    b2 -= learning_rate * db2
-
-    return w1, b1, w2, b2, loss
-
-
-def accuracy(Y, Y_pred):
-    m = Y.shape[0]
-    correct_predictions = np.sum(Y == Y_pred)
-    return correct_predictions / m
-
-
+# Function to train an MLP for a specified number of epochs
 def train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epochs):
-    train_accuracies = []
 
+    train_accuracies = []
+    losses=[]
     for epoch in range(num_epochs):
-        for i in range(data_train.shape[0]):
-            x = data_train[i:i+1]
-            y = labels_train[i:i+1]
-            w1, b1, w2, b2, loss = learn_once_cross_entropy(w1, b1, w2, b2, x, y, learning_rate)
-
-        # Calculate accuracy for the epoch
-        Z1 = np.dot(data_train, w1) + b1
-        A1 = sigmoid(Z1)
-        Z2 = np.dot(A1, w2) + b2
-        A2 = sigmoid(Z2)
-        train_pred = (A2 > 0.5).astype(int)
-        acc = accuracy(labels_train, train_pred)
-        train_accuracies.append(acc)
-
-    return w1, b1, w2, b2, train_accuracies
+        print('EPOCH ' + str(epoch + 1))
+        labels_coded = one_hot(labels_train)
+        w1, b1, w2, b2, loss = learn_once_cross_entropy(w1, b1, w2, b2, data_train, labels_coded, learning_rate)
 
-def test_mlp(w1, b1, w2, b2, data_test, labels_test):
-    Z1 = np.dot(data_test, w1) + b1
-    A1 = sigmoid(Z1)
-    Z2 = np.dot(A1, w2) + b2
-    A2 = sigmoid(Z2)
-    test_pred = (A2 > 0.5).astype(int)
-    test_acc = accuracy(labels_test, test_pred)
-    return test_acc
+    # Calculate training accuracy for this epoch
+        a0 = data_train
+        z1 = np.matmul(a0, w1) + b1
+        a1 = sigmoid(z1)
+        z2 = np.matmul(a1, w2) + b2
+        a2 = sigmoid(z2)
+        predictions = np.argmax(a2, axis=1)
 
-def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epochs):
-    d_in = data_train.shape[1]
-    w1 = np.random.randn(d_in, d_h)
-    b1 = np.zeros((1, d_h))
-    w2 = np.random.randn(d_h, 1)
-    b2 = np.zeros((1, 1))
+        accuracy = np.mean(predictions == labels_train)
+        train_accuracies.append(accuracy)
 
-    w1, b1, w2, b2, train_accuracies = train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epochs)
-    test_accuracy = test_mlp(w1, b1, w2, b2, data_test, labels_test)
+        print('Loss : '+ str(round(loss, 4)) + '\n')
 
-    return train_accuracies, test_accuracy
+    return w1, b1, w2, b2, train_accuracies, losses
 
+# Function to test the MLP on a test set
+def test_mlp(w1, b1, w2, b2, data_test, labels_test):
 
+    a0 = data_test
+    z1 = np.matmul(a0, w1) + b1
+    a1 = sigmoid(z1)
+    z2 = np.matmul(a1, w2) + b2
+    a2 = sigmoid(z2)
 
+    predictions = np.argmax(a2, axis=1)
+    test_accuracy = np.mean(predictions == labels_test)
 
-def main_MSE():
-    N = 30  # number of input data
-    d_in = 3  # input dimension
-    d_h = 3  # number of neurons in the hidden layer
-    d_out = 2  # output dimension (number of neurons of the output layer)
-    learning_rate = 0.1
+    return test_accuracy
 
+# Function to run the entire MLP training and testing process
+def run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate, num_epochs):
 
-    # Random initialization of the network weights and biaises
-    w1 = 2 * np.random.rand(d_in, d_h) - 1  # first layer weights
-    b1 = np.zeros((1, d_h))  # first layer biaises
-    w2 = 2 * np.random.rand(d_h, d_out) - 1  # second layer weights
-    b2 = np.zeros((1, d_out))  # second layer biaises
+    d_in = data_train.shape[1]
+    num_classes = len(np.unique(labels_train))
 
-    data = np.random.rand(N, d_in)  # create a random data
-    targets = np.random.rand(N, d_out)  # create a random targets
+    # Initialize the first and second layer weights and biases
+    w1 = 2 * np.random.rand(d_in, d_h) - 1
+    b1 = np.zeros((1, d_h))
+    w2 = 2 * np.random.rand(d_h, num_classes) - 1
+    b2 = np.zeros((1, num_classes))
 
-    w1, b1, w2, b2, loss = learn_once_mse(w1, b1, w2, b2, data, targets, learning_rate)
+    # Train the MLP on the training data
+    w1, b1, w2, b2, train_accuracies, losses = train_mlp(w1, b1, w2, b2, data_train, labels_train, learning_rate, num_epochs)
 
-    print('Loss (MSE) : ' + str(loss))
+    # Test the MLP on the testing data
+    test_accuracy = test_mlp(w1, b1, w2, b2, data_test, labels_test)
 
+    return train_accuracies, test_accuracy, losses
 
+def main():
 
+    print('#START#')
 
-def main_CrossEntropy():
+    # Set hyperparameters
     split_factor = 0.9
     d_h = 64
     learning_rate = 0.1
     num_epochs = 100
 
-    # Define your data, labels, and parameters here
-
-    # Generate some sample data for demonstration
-    # Replace this with your actual data
-    data_train = np.random.rand(100, 10)
-    labels_train = np.random.randint(2, size=100)
-    data_test = np.random.rand(20, 10)
-    labels_test = np.random.randint(2, size=20)
-
-    # Call run_mlp_training with your data and parameters
-    train_accuracies, test_accuracy = run_mlp_training(data_train, labels_train, data_test, labels_test, d_h,
-                                                       learning_rate, num_epochs)
-
-    # Create a plot of training accuracies across epochs
-    plt.figure(figsize=(10, 6))
-    x=range(1, num_epochs +1)
-    plt.plot(x, train_accuracies)
-    plt.xlabel('Epochs')
-    plt.ylabel('Accuracy')
-    plt.title('Training Accuracy Evolution')
-    pl.grid()
-
-    plt.savefig('mlp.png')
+    # Load CIFAR dataset and split the training data and the labels for the two phases(train and test)
+    folder_path = 'data/cifar-10-batches-py'
+    data, labels = read_cifar(folder_path)
+    data_train, data_test, labels_train, labels_test = split_dataset(data, labels, split_factor)
+
+    # Run MLP training and testing
+    train_accuracies, test_accuracy, losses= run_mlp_training(data_train, labels_train, data_test, labels_test, d_h, learning_rate,
+                                                       num_epochs)
+    # Test accuracy after 'num_epochs' epochs of training
+    print('FINAL ACCURACY : ' + str(round(test_accuracy, 4)) + '\n')
+
+    # Plot the training accuracy for each epoch
+    x = range(1, num_epochs + 1)
+    plt.plot(x, losses)
+    plt.xlabel('Epoch')
+    plt.ylabel('Training Loss')
+    plt.title('Training Loss vs. Epoch')
+    plt.grid()
+    plt.savefig('results/loss.png')
     plt.show()
 
-
 if __name__ == "__main__":
-    main_MSE()
-    main_CrossEntropy()
-
-
-
+    main()
 
 
diff --git a/read_cifar.py b/read_cifar.py
index fb1251f..1e8d727 100644
--- a/read_cifar.py
+++ b/read_cifar.py
@@ -4,26 +4,36 @@ import os
 from sklearn.model_selection import train_test_split
 
 def read_cifar_batch(batch):
-    with open(batch, 'rb') as fo:
-        dict = pickle.load(fo, encoding='bytes')
-        data = dict[b'data']
-        labels = dict[b'labels']
-        print(dict[b'batch_label'])
-    return data, labels
+
+    with open(batch, 'rb') as file:
+
+        dict = pickle.load(file, encoding='bytes')
+        batch_data = dict[b'data']
+        batch_labels = dict[b'labels']
+
+    return batch_data, batch_labels
 
 def read_cifar(path):
+
     batches_list = os.listdir(path)
     data, labels = [], []
+
     for batch in batches_list:
         if(batch == 'batches.meta' or batch == 'readme.html'):
             continue
         data_batch, labels_batch = read_cifar_batch(path + '/' + batch)
         data.append(data_batch)
         labels.append(labels_batch)
-    return np.array(data, dtype=np.float32).reshape((60000, 3072)), np.array(labels, dtype=np.int64).reshape(-1)
 
-def split_dataset(data, labels, split):
-    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=1-split, shuffle=True)
+    data= np.array(data, dtype=np.float32).reshape((60000, 3072))
+    labels=np.array(labels, dtype=np.int64).reshape(-1)
+
+    return data, labels
+
+def split_dataset(data, labels, split_factor):
+
+    data_train, data_test, labels_train, labels_test = train_test_split(data, labels, test_size=1-split_factor, shuffle=True)
+
     return data_train, data_test, labels_train, labels_test
 
 
diff --git a/results/knn.png b/results/knn.png
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-- 
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