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---
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 |