133 lines
4.1 KiB
Python
133 lines
4.1 KiB
Python
import numpy as np
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from torchvision import datasets
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class MLP:
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def __init__(self, input_size, hidden_size1, hidden_size2, output_size, weight_scale):
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self.W1 = np.random.randn(input_size, hidden_size1) * weight_scale
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self.b1 = np.zeros((1, hidden_size1))
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self.W2 = np.random.randn(hidden_size1, hidden_size2) * weight_scale
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self.b2 = np.zeros((1, hidden_size2))
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self.W3 = np.random.randn(hidden_size2, output_size) * weight_scale
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self.b3 = np.zeros((1, output_size))
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def forward(self, x):
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self.x = x
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self.z1 = x @ self.W1 + self.b1
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self.a1 = self.relu(self.z1)
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self.z2 = self.a1 @ self.W2 + self.b2
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self.a2 = self.relu(self.z2)
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self.z3 = self.a2 @ self.W3 + self.b3
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self.a3 = self.softmax(self.z3)
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return self.a3
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def backward(self, y, lr):
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m = y.shape[0]
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y_one_hot = self.one_hot_encode(y, self.W3.shape[1])
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dz3 = self.a3 - y_one_hot
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dw3 = (self.a2.T @ dz3) / m
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db3 = np.sum(dz3, axis=0, keepdims=True) / m
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dz2 = (dz3 @ self.W3.T) * self.relu_deriv(self.z2)
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dw2 = (self.a1.T @ dz2) / m
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db2 = np.sum(dz2, axis=0, keepdims=True) / m
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dz1 = (dz2 @ self.W2.T) * self.relu_deriv(self.z1)
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dw1 = (self.x.T @ dz1) / m
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db1 = np.sum(dz1, axis=0, keepdims=True) / m
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self.W3 -= lr * dw3
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self.b3 -= lr * db3
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self.W2 -= lr * dw2
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self.b2 -= lr * db2
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self.W1 -= lr * dw1
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self.b1 -= lr * db1
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@staticmethod
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def relu(x):
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return np.maximum(0, x)
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@staticmethod
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def relu_deriv(x):
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return (x > 0).astype(float)
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@staticmethod
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def softmax(x):
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e_x = np.exp(x - np.max(x, axis=1, keepdims=True))
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return e_x / np.sum(e_x, axis=1, keepdims=True)
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@staticmethod
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def one_hot_encode(y, num_classes):
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return np.eye(num_classes)[y]
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@staticmethod
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def cross_entropy_loss(y, y_hat):
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m = y.shape[0]
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eps = 1e-12
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y_hat_clipped = np.clip(y_hat, eps, 1. - eps)
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log_probs = -np.log(y_hat_clipped[np.arange(m), y])
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return np.mean(log_probs)
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def train_model(self, x_train, y_train, x_val, y_val, lr, epochs, batch_size):
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for epoch in range(1, epochs + 1):
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perm = np.random.permutation(x_train.shape[0])
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x_train_shuffled, y_train_shuffled = x_train[perm], y_train[perm]
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epoch_loss = 0.0
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num_batches = int(np.ceil(x_train.shape[0] / batch_size))
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for i in range(num_batches):
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start = i * batch_size
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end = start + batch_size
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x_batch = x_train_shuffled[start:end]
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y_batch = y_train_shuffled[start:end]
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self.forward(x_batch)
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self.backward(y_batch, lr)
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epoch_loss += self.cross_entropy_loss(y_batch, self.a3)
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epoch_loss /= num_batches
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val_pred = self.predict(x_val)
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val_acc = np.mean(val_pred == y_val)
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print(f"Epoch {epoch:02d} | Training Loss: {epoch_loss:.4f} | Value Accuracy: {val_acc:.4f}")
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return val_acc
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def predict(self, x):
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probs = self.forward(x)
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return np.argmax(probs, axis=1)
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train_set = datasets.FashionMNIST(root='.', train=True, download=True)
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test_set = datasets.FashionMNIST(root='.', train=False, download=True)
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x_train = train_set.data.numpy().reshape(-1, 28 * 28).astype(np.float32) / 255.0
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y_train = train_set.targets.numpy()
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x_test = test_set.data.numpy().reshape(-1, 28 * 28).astype(np.float32) / 255.0
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y_test = test_set.targets.numpy()
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mlp = MLP(
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input_size = 28 * 28,
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hidden_size1= 128,
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hidden_size2= 64,
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output_size = 10,
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weight_scale= 1e-2
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)
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mlp.train_model(
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x_train = x_train,
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y_train = y_train,
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x_val = x_test,
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y_val = y_test,
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lr = 1e-2,
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epochs = 10,
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batch_size=128
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)
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test_pred = mlp.predict(x_test)
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test_acc = np.mean(test_pred == y_test)
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print(f"\nFinal test accuracy: {test_acc:.4f}")
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