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FashionMNIST/fashion-mnist_train.csv

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README.md

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+# Classification-of-Image-Data-with-MLP-and-CNN
+
+This project implements both a multilayer perceptron and a convolutional neural network in Python; 
+the perceptron comprises an input layer, one or more hidden layers, and an output layer, 
+while the convolutional network features two convolutional layers, a fully connected hidden layer
+and a fully connected output layer.
+
+
+# Authors
+
+- Batuhan Berk Başoğlu, 260768350 - batuhan-basoglu
+- Jared Tritt, 260763506 - Jaredtritt

multilayer-perceptron.py

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+import numpy as np
+from torchvision import datasets
+
+class MLP:
+    def __init__(self, input_size, hidden_size1, hidden_size2, output_size, weight_scale):
+        self.W1 = np.random.randn(input_size, hidden_size1) * weight_scale
+        self.b1 = np.zeros((1, hidden_size1))
+        self.W2 = np.random.randn(hidden_size1, hidden_size2) * weight_scale
+        self.b2 = np.zeros((1, hidden_size2))
+        self.W3 = np.random.randn(hidden_size2, output_size) * weight_scale
+        self.b3 = np.zeros((1, output_size))
+
+    def forward(self, x):
+        self.x = x       
+        self.z1 = x @ self.W1 + self.b1
+        self.a1 = self.relu(self.z1)   
+        self.z2 = self.a1 @ self.W2 + self.b2  
+        self.a2 = self.relu(self.z2)          
+        self.z3 = self.a2 @ self.W3 + self.b3  
+        self.a3 = self.softmax(self.z3)    
+        return self.a3
+
+    def backward(self, y, lr):
+        m = y.shape[0]
+        y_one_hot = self.one_hot_encode(y, self.W3.shape[1])
+
+        dz3 = self.a3 - y_one_hot      
+        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)
+        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)
+        dw1 = (self.x.T @ dz1) / m                 
+        db1 = np.sum(dz1, axis=0, keepdims=True) / m
+
+        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):
+        return np.maximum(0, x)
+
+    @staticmethod
+    def relu_deriv(x):
+        return (x > 0).astype(float)
+
+    @staticmethod
+    def softmax(x):
+        e_x = np.exp(x - np.max(x, axis=1, keepdims=True))
+        return e_x / np.sum(e_x, axis=1, keepdims=True)
+
+    @staticmethod
+    def one_hot_encode(y, num_classes):
+        return np.eye(num_classes)[y]
+
+    @staticmethod
+    def cross_entropy_loss(y, y_hat):
+        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 train_model(self, x_train, y_train, x_val, y_val, lr, epochs, batch_size):
+        for epoch in range(1, epochs + 1):
+            perm = np.random.permutation(x_train.shape[0])
+            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]
+                y_batch = y_train_shuffled[start:end]
+
+                self.forward(x_batch)
+                self.backward(y_batch, lr)
+
+                epoch_loss += self.cross_entropy_loss(y_batch, self.a3)
+
+            epoch_loss /= num_batches
+
+            val_pred = self.predict(x_val)
+            val_acc  = np.mean(val_pred == y_val)
+
+            print(f"Epoch {epoch:02d} | Training Loss: {epoch_loss:.4f} | Value Accuracy: {val_acc:.4f}")
+
+        return val_acc
+
+    def predict(self, x):
+        probs = self.forward(x)
+        return np.argmax(probs, axis=1)
+
+
+train_set = datasets.FashionMNIST(root='.', train=True, download=True)
+test_set  = datasets.FashionMNIST(root='.', train=False, download=True)
+
+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 = MLP(
+    input_size  = 28 * 28,
+    hidden_size1= 128,
+    hidden_size2= 64,
+    output_size = 10,
+    weight_scale= 1e-2
+)
+
+mlp.train_model(
+    x_train  = x_train,
+    y_train  = y_train,
+    x_val    = x_test,
+    y_val    = y_test,
+    lr       = 1e-2,
+    epochs   = 10,
+    batch_size=128
+)
+
+test_pred = mlp.predict(x_test)
+test_acc  = np.mean(test_pred == y_test)
+print(f"\nFinal test accuracy: {test_acc:.4f}")