Sách Deep Learning cơ bản

94 Chương 6. Backpropagation
b_ = np.zeros((layers[i+1], 1))
self.W.append(w_/layers[i])
self.b.append(b_)
# Tóm tắt mô hình neural network
def __repr__(self):
return "Neural network [{}]".format("-".join(str(l) for l in self.layers))
# Train mô hình với dữ liệu
def fit_partial(self, x, y):
A = [x]
# quá trình feedforward
out = A[-1]
for i in range(0, len(self.layers) - 1):
out = sigmoid(np.dot(out, self.W[i]) + (self.b[i].T))
A.append(out)
# quá trình backpropagation
y = y.reshape(-1, 1)
dA = [-(y/A[-1] - (1-y)/(1-A[-1]))]
dW = []
db = []
for i in reversed(range(0, len(self.layers)-1)):
dw_ = np.dot((A[i]).T, dA[-1] * sigmoid_derivative(A[i+1]))
db_ = (np.sum(dA[-1] * sigmoid_derivative(A[i+1]), 0)).reshape(-1,1)
dA_ = np.dot(dA[-1] * sigmoid_derivative(A[i+1]), self.W[i].T)
dW.append(dw_)
db.append(db_)
dA.append(dA_)
# Đảo ngược dW, db
dW = dW[::-1]
db = db[::-1]
# Gradient descent
for i in range(0, len(self.layers)-1):
self.W[i] = self.W[i] - self.alpha * dW[i]
self.b[i] = self.b[i] - self.alpha * db[i]
def fit(self, X, y, epochs=20, verbose=10):
for epoch in range(0, epochs):
self.fit_partial(X, y)
if epoch % verbose == 0:
loss = self.calculate_loss(X, y)
print("Epoch {}, loss {}".format(epoch, loss))