神经网络的训练就是梯度反向传播的过程,也是面试的时候手撕的重要考点之一!
下面我搭建了两层神经网络,使用sigmoid激活函数,具体的公式推导就忽略了,但是要注意的是,记住公式是最为关键的。
import numpy as np
np.random.seed(17)
def sigmoid(x):
return 1 / (1 + np.exp(-x))
def sigmoid_grad(x):
return (1.0 - sigmoid(x)) * sigmoid(x)
def softmax(x):
if x.ndim == 2:
x = x.T
x = x - np.max(x, axis=0)
y = np.exp(x) / np.sum(np.exp(x), axis=0)
return y.T
x = x - np.max(x) # 溢出对策
return np.exp(x) / np.sum(np.exp(x))
# 搭建两层全连接神经网络,使用sigmoid激活函数,完成10分类任务
class TwoLayerNet:
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
# 初始化权重
self.params = {}
self.params['W1'] = weight_init_std * np.random.randn(input_size, hidden_size)
self.params['b1'] = np.zeros(hidden_size)
self.params['W2'] = weight_init_std * np.random.randn(hidden_size, output_size)
self.params['b2'] = np.zeros(output_size)
def predict(self, x):
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
y = softmax(a2)
return y
# x:输入数据, t:监督数据
def loss(self, x, t):
y = self.predict(x)
LOSS = (1/y.shape[0])*np.sum(-t * np.log(y) - (1-t) * np.log(1-y))
return LOSS
def accuracy(self, x, t):
y = self.predict(x)
y = np.argmax(y, axis=1)
t = np.argmax(t, axis=1)
accuracy = np.sum(y == t) / float(x.shape[0])
return accuracy
# x:输入数据, t:监督数据
def gradient(self, x, t):
W1, W2 = self.params['W1'], self.params['W2']
b1, b2 = self.params['b1'], self.params['b2']
grads = {}
batch_num = x.shape[0]
# forward
a1 = np.dot(x, W1) + b1
z1 = sigmoid(a1)
a2 = np.dot(z1, W2) + b2
y = softmax(a2)
# backward
dy = (y - t) / batch_num # 输出层反向误差
grads['W2'] = np.dot(z1.T, dy)
grads['b2'] = np.sum(dy, axis=0)
da1 = np.dot(dy, W2.T) # 隐层反向传播误差
dz1 = sigmoid_grad(a1) * da1
grads['W1'] = np.dot(x.T, dz1)
grads['b1'] = np.sum(dz1, axis=0)
return grads
# 数据集:随机的初始的数据集,输入为10分类,数据集大小是3万
x_train = np.random.randn(30000, 784)
t_train = np.random.randint(0, 2, size=(x_train.shape[0], 10))
train_size = x_train.shape[0]
batch_size = 512
# 任务1:梯度检查
# 梯度检查
net = TwoLayerNet(input_size=784, hidden_size=80, output_size=10)
grad = net.gradient(x_train, t_train)
print("-------梯度检查---------")
print(grad["W1"].shape)
print(grad["b1"].shape)
print(grad["W2"].shape)
print(grad["b2"].shape)
# 任务2:模型训练
if __name__ == "__main__":
net = TwoLayerNet(input_size=784, hidden_size=80, output_size=10)
batch_size = 512
learning_rate = 0.01
iters = 1000 # 适当设定循环的次数
loss_history = []
iter_per_epoch = max(train_size / batch_size, 1)
for i in range(iters):
batch_mask = np.random.choice(train_size, batch_size)
x_batch = x_train[batch_mask]
t_batch = t_train[batch_mask]
grad = net.gradient(x_batch, t_batch)
# 更新参数
for key in ('W1', 'b1', 'W2', 'b2'):
net.params[key] -= learning_rate * grad[key]
loss = net.loss(x_batch, t_batch)
if (i+1) % 100 == 0:
loss_history.append(round(loss, 4))
print("iterrs:%d, loss:%.4f" % (i+1, loss))
print(loss_history)
参考文献:
斋藤康毅:深度学习入门_ 基于Python的理论与实现