| --- |
| license: mit |
| metrics: |
| - accuracy |
| datasets: |
| - garythung/trashnet |
| --- |
| ## Priyo_Neuralnetwork |
| |
| class Priyo_NeuralNetwork: |
|
|
| def __init__(self, input_size, hidden_size, output_size): |
| # Inisialisasi bobot dan bias secara acak |
| self.weights1 = np.random.randn(input_size, hidden_size) |
| self.bias1 = np.zeros(hidden_size) |
| self.weights2 = np.random.randn(hidden_size, output_size) |
| self.bias2 = np.zeros(output_size) |
| |
| def sigmoid(self, x): |
| clipped_x = np.clip(x, -5, 5) # Clip values between -5 and 5 |
| return 1 / (1 + np.exp(-clipped_x)) |
| |
| def forward(self, X): |
| # Perhitungan forward propagation |
| self.z1 = np.dot(X, self.weights1) + self.bias1 |
| self.a1 = self.sigmoid(self.z1) |
| self.z2 = np.dot(self.a1, self.weights2) + self.bias2 |
| self.a2 = self.sigmoid(self.z2) |
| return self.a2 |
| |
| def backward(self, X, y, learning_rate): |
| m = X.shape[0] |
| dZ2 = self.a2 - y |
| dW2 = 1/m * np.dot(self.a1.T, dZ2) |
| db2 = 1/m * np.sum(dZ2, axis=0) |
| dZ1 = np.dot(dZ2, self.weights2.T) * (1 - self.a1) * self.a1 |
| dW1 = 1/m * np.dot(X.T, dZ1) |
| db1 = 1/m * np.sum(dZ1, axis=0) |
| |
| return dW1, db1, dW2, db2 |
| |
| def train(self, X, y, epochs, learning_rate, beta1=0.9, beta2=0.999, epsilon=1e-8): |
| m = X.shape[0] |
| |
| # Initialize moments for Adam |
| v_dw1, v_db1, v_dw2, v_db2 = np.zeros_like(self.weights1), np.zeros_like(self.bias1), np.zeros_like(self.weights2), np.zeros_like(self.bias2) |
| s_dw1, s_db1, s_dw2, s_db2 = np.zeros_like(self.weights1), np.zeros_like(self.bias1), np.zeros_like(self.weights2), np.zeros_like(self.bias2) |
| t = 0 |
| |
| for epoch in range(epochs): |
| self.forward(X) |
| dW1, db1, dW2, db2 = self.backward(X, y, learning_rate) |
| |
| # Update weights and biases using Adam |
| t += 1 |
| |
| # Update biased first moment estimate |
| v_dw1 = beta1 * v_dw1 + (1 - beta1) * dW1 |
| v_db1 = beta1 * v_db1 + (1 - beta1) * db1 |
| v_dw2 = beta1 * v_dw2 + (1 - beta1) * dW2 |
| v_db2 = beta1 * v_db2 + (1 - beta1) * db2 |
| |
| # Update biased second raw moment estimate |
| s_dw1 = beta2 * s_dw1 + (1 - beta2) * np.square(dW1) |
| s_db1 = beta2 * s_db1 + (1 - beta2) * np.square(db1) |
| s_dw2 = beta2 * s_dw2 + (1 - beta2) * np.square(dW2) |
| s_db2 = beta2 * s_db2 + (1 - beta2) * np.square(db2) |
| |
| # Compute bias-corrected first moment estimate |
| v_dw1_corrected = v_dw1 / (1 - beta1**t) |
| v_db1_corrected = v_db1 / (1 - beta1**t) |
| v_dw2_corrected = v_dw2 / (1 - beta1**t) |
| v_db2_corrected = v_db2 / (1 - beta1**t) |
| |
| # Compute bias-corrected second raw moment estimate |
| s_dw1_corrected = s_dw1 / (1 - beta2**t) |
| s_db1_corrected = s_db1 / (1 - beta2**t) |
| s_dw2_corrected = s_dw2 / (1 - beta2**t) |
| s_db2_corrected = s_db2 / (1 - beta2**t) |
| |
| # Update weights and biases |
| self.weights1 -= learning_rate * v_dw1_corrected / (np.sqrt(s_dw1_corrected) + epsilon) |
| self.bias1 -= learning_rate * v_db1_corrected / (np.sqrt(s_db1_corrected) + epsilon) |
| self.weights2 -= learning_rate * v_dw2_corrected / (np.sqrt(s_dw2_corrected) + epsilon) |
| self.bias2 -= learning_rate * v_db2_corrected / (np.sqrt(s_db2_corrected) + epsilon) |
| |
| if (epoch+1) % 100 == 0: |
| print(f'Epoch {epoch+1}/{epochs}, loss: {self.loss(y, self.a2)}') |
| |
| def loss(self, y_true, y_pred): |
| # Fungsi loss (misalnya binary cross-entropy) |
| return -np.mean(y_true * np.log(y_pred) + (1 - y_true) * np.log(1 - y_pred)) |
| |
| def predict(self, X): |
| # Prediksi menggunakan forward propagation |
| y_pred = self.forward(X) |
| # Rounding untuk klasifikasi biner |
| y_pred = np.round(y_pred) |
| return y_pred |
| |
| def accuracy(self, X, y): |
| # Hitung akurasi |
| y_pred = self.predict(X) |
| accuracy = np.mean(y_pred == y) |
| return accuracy |
