--- 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