用numpy实现BP神经网络

在本篇博文中，我们只使用numpy来搭建一个简单的包含输入层、隐藏层、输出层的神经网络，我们选择sigmoid作为激活函数，选择均方差损失函数，最后使用mnist数据集进行了训练和测试。

1、公式推导

均方差损失函数：

[loss = J(W,b,x,y)=frac{1}{2}||a^L-y||^2 ]

前向传播过程：

[ z^l = W^la^{l-1}+b^l \ a^l=sigma(z^l) ]

反向传播过程：

[ frac{partial J}{partial W^l}=delta^l(a^{l-1})^T \ frac{partial J}{partial b^l}=delta^l \ delta^l=[(W^{l+1})^Tdelta^{l+1}]odotsigma'(z^l)\ delta^L=(a^L-y)odotsigma'(z^L) ]

2、一些工具函数

包括sigmoid激活函数及其一阶导数，和将标签进行one-hot编码的函数，如下所示：

# 标签one-hot处理
def onehot(targets, num):
    result = np.zeros((num, 10))
    for i in range(num):
        result[i][targets[i]] = 1
    return result

# sigmoid
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# sigmoid的一阶导数
def Dsigmoid(x):
    return sigmoid(x)*(1-sigmoid(x))

3、神经网络具体实现

代码中的self.d2和d1代表公式中的(delta)，其他变量均直接按照公式命名，代码如下：

class NN(object):
    def __init__(self, l0, l1, l2):
        self.lr = 0.1                                        # 学习率
        self.W1 = np.random.randn(l0, l1) * 0.01             # 初始化
        self.b1 = np.random.randn(l1) * 0.01
        self.W2 = np.random.randn(l1, l2) * 0.01
        self.b2 = np.random.randn(l2) * 0.01

    # 前向传播
    def forward(self, X, y):
        self.X = X                                           # m x 784
        self.z1 = np.dot(X, self.W1) + self.b1               # m x 500, 500是中间层层数
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2         # m x 10
        self.a2 = sigmoid(self.z2)
        loss = np.sum((self.a2 - y) * (self.a2 - y)) / 2     # 均方差
        self.d2 = (self.a2 - y) * Dsigmoid(self.z2)          # m x 10 , 用于误差反向传播
        return loss, self.a2

    # 反向传播
    def backward(self):
        dW2 = np.dot(self.a1.T, self.d2) / 3                  # 500 x 10, batchsize=3
        db2 = np.sum(self.d2, axis=0) / 3                     # 10
        d1 = np.dot(self.d2, self.W2.T) * Dsigmoid(self.z1)   # m x 500, 用于误差反向传播
        dW1 = np.dot(self.X.T, d1) / 3                        # 784x 500
        db1 = np.sum(d1, axis=0) / 3                          # 500

        self.W2 -= self.lr * dW2
        self.b2 -= self.lr * db2
        self.W1 -= self.lr * dW1
        self.b1 -= self.lr * db1

4、训练和测试

我们直接使用了torchvision集成的mnist数据集，在训练后将权重参数保存到文件中，测试时再从文件中读取权重参数，最后我们测试的准确率达到了96.48%。

def train():
    nn = NN(784, 500, 10)

    for epoch in range(10):
        for i in range(0, 60000, 3):
            X = train_data.data[i:i + 3]
            y = train_data.targets[i:i + 3]
            loss, _ = nn.forward(X, y)
            print("Epoch:", epoch, "-", i, ":", "{:.3f}".format(loss))
            nn.backward()
        np.savez("data.npz", w1=nn.W1, b1=nn.b1, w2=nn.W2, b2=nn.b2)

def test():
    r = np.load("data.npz")
    nn = NN(784, 500, 10)
    nn.W1 = r["w1"]
    nn.b1 = r["b1"]
    nn.W2 = r["w2"]
    nn.b2 = r["b2"]
    _, result = nn.forward(test_data.data, test_data.targets2)
    result = np.argmax(result, axis=1)
    precison = np.sum(result==test_data.targets) / 10000
    print("Precison:", precison)

# Mnist手写数字集
train_data = torchvision.datasets.MNIST(root='./mnist/', train=True, download=False)
test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
train_data.data = train_data.data.numpy()         # [60000,28,28]
train_data.targets = train_data.targets.numpy()   # [60000]
test_data.data = test_data.data.numpy()           # [10000,28,28]
test_data.targets = test_data.targets.numpy()     # [10000]

# 输入向量处理
train_data.data = train_data.data.reshape(60000, 28 * 28) / 255.  # (60000, 784)
test_data.data = test_data.data.reshape(10000, 28 * 28) / 255.

# 标签one-hot处理
train_data.targets = onehot(train_data.targets, 60000) # (60000, 10)
test_data.targets2 = onehot(test_data.targets, 10000)  # 用于前向传播

train()
#test()

5、完整代码

import torchvision
import numpy as np

# 标签one-hot处理
def onehot(targets, num):
    result = np.zeros((num, 10))
    for i in range(num):
        result[i][targets[i]] = 1
    return result

# sigmoid
def sigmoid(x):
    return 1 / (1 + np.exp(-x))

# sigmoid的一阶导数
def Dsigmoid(x):
    return sigmoid(x)*(1-sigmoid(x))


class NN(object):
    def __init__(self, l0, l1, l2):
        self.lr = 0.1                                        # 学习率
        self.W1 = np.random.randn(l0, l1) * 0.01             # 初始化
        self.b1 = np.random.randn(l1) * 0.01
        self.W2 = np.random.randn(l1, l2) * 0.01
        self.b2 = np.random.randn(l2) * 0.01

    # 前向传播
    def forward(self, X, y):
        self.X = X                                           # m x 784
        self.z1 = np.dot(X, self.W1) + self.b1               # m x 500, 等于中间层层数
        self.a1 = sigmoid(self.z1)
        self.z2 = np.dot(self.a1, self.W2) + self.b2         # m x 10
        self.a2 = sigmoid(self.z2)
        loss = np.sum((self.a2 - y) * (self.a2 - y)) / 2     # 均方差
        self.d2 = (self.a2 - y) * Dsigmoid(self.z2)          # m x 10 , 用于反向传播
        return loss, self.a2

    # 反向传播
    def backward(self):
        dW2 = np.dot(self.a1.T, self.d2) / 3                  # 500 x 10, batchsize=3
        db2 = np.sum(self.d2, axis=0) / 3                     # 10
        d1 = np.dot(self.d2, self.W2.T) * Dsigmoid(self.z1)   # m x 500, 用于反向传播
        dW1 = np.dot(self.X.T, d1) / 3                        # 784x 500
        db1 = np.sum(d1, axis=0) / 3                          # 500

        self.W2 -= self.lr * dW2
        self.b2 -= self.lr * db2
        self.W1 -= self.lr * dW1
        self.b1 -= self.lr * db1


def train():
    nn = NN(784, 500, 10)

    for epoch in range(10):
        for i in range(0, 60000, 3):
            X = train_data.data[i:i + 3]
            y = train_data.targets[i:i + 3]
            loss, _ = nn.forward(X, y)
            print("Epoch:", epoch, "-", i, ":", "{:.3f}".format(loss))
            nn.backward()
        np.savez("data.npz", w1=nn.W1, b1=nn.b1, w2=nn.W2, b2=nn.b2)

def test():
    r = np.load("data.npz")
    nn = NN(784, 500, 10)
    nn.W1 = r["w1"]
    nn.b1 = r["b1"]
    nn.W2 = r["w2"]
    nn.b2 = r["b2"]
    _, result = nn.forward(test_data.data, test_data.targets2)
    result = np.argmax(result, axis=1)
    precison = np.sum(result==test_data.targets) / 10000
    print("Precison:", precison)

if __name__ == '__main__':

    # Mnist手写数字集
    train_data = torchvision.datasets.MNIST(root='./mnist/', train=True, download=False)
    test_data = torchvision.datasets.MNIST(root='./mnist/', train=False)
    train_data.data = train_data.data.numpy()         # [60000,28,28]
    train_data.targets = train_data.targets.numpy()   # [60000]
    test_data.data = test_data.data.numpy()           # [10000,28,28]
    test_data.targets = test_data.targets.numpy()     # [10000]

    # 输入向量处理
    train_data.data = train_data.data.reshape(60000, 28 * 28) / 255.  # (60000, 784)
    test_data.data = test_data.data.reshape(10000, 28 * 28) / 255.

    # 标签one-hot处理
    train_data.targets = onehot(train_data.targets, 60000) # (60000, 10)
    test_data.targets2 = onehot(test_data.targets, 10000)  # 用于前向传播

    train()
    #test()