liuroy / classfication_demo

用logistic回归，SVM，神经网络实现分类算法

classfication_demo

用logistic回归，SVM，神经网络实现分类算法

Logistic分类

采用随机梯度下降方法实现。

def train(self, num_iteration=150):
    """随机梯度上升算法

    Args:
        data (numpy.ndarray): 训练数据集
        labels (numpy.ndarray): 训练标签
        num_iteration (int): 迭代次数
    """
    for j in xrange(num_iteration):
        data_index = range(self.data_num)
        for i in xrange(self.data_num):
            # 学习速率
            alpha = 0.01
            rand_index = int(random.uniform(0, len(data_index)))
            error = self.label[rand_index] - sigmoid(sum(self.data[rand_index] * self.weights + self.b))
            self.weights += alpha * error * self.data[rand_index]
            self.b += alpha * error
            del(data_index[rand_index])

效果图：

神经网络

实现一个只有两层的神经网络

BGD实现

批量梯度下降实现代码：

def batch_gradient_descent(self, num_passes=20000):
    """批量梯度下降训练模型"""
    for i in xrange(0, num_passes):
        # Forward propagation
        z1 = self.data.dot(self.W1) + self.b1
        a1 = np.tanh(z1)
        z2 = a1.dot(self.W2) + self.b2
        exp_scores = np.exp(z2)
        probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)

        # Backpropagation
        delta3 = probs

        delta3[range(self.num_examples), self.label] -= 1
        dW2 = (a1.T).dot(delta3)
        db2 = np.sum(delta3, axis=0, keepdims=True)
        delta2 = delta3.dot(self.W2.T) * (1 - np.power(a1, 2))
        dW1 = np.dot(self.data.T, delta2)
        db1 = np.sum(delta2, axis=0)

        # Add regularization terms (b1 and b2 don't have regularization terms)
        dW2 += self.reg_lambda * self.W2
        dW1 += self.reg_lambda * self.W1

        # Gradient descent parameter update
        self.W1 += -self.epsilon * dW1
        self.b1 += -self.epsilon * db1
        self.W2 += -self.epsilon * dW2
        self.b2 += -self.epsilon * db2

隐藏层只有三个神经元时的效果图：

SGD实现

随机梯度下降实现：

def stochastic_gradient_descent(self, num_passes=1000):
    """随机梯度下降训练模型"""
    for i in xrange(0, num_passes):
        data_index = range(self.num_examples)

        for j in xrange(self.num_examples):
            rand_index = int(np.random.uniform(0, len(data_index)))
            x = np.mat(self.data[rand_index])
            y = self.label[rand_index]

            # Forward propagation
            z1 = x.dot(self.W1) + self.b1
            a1 = np.tanh(z1)
            z2 = a1.dot(self.W2) + self.b2
            exp_scores = np.exp(z2)
            probs = exp_scores / np.sum(exp_scores, axis=1, keepdims=True)

            # Backpropagation
            delta3 = probs
            if y:
                delta3[0, 0] -= 1
            else:
                delta3[0, 1] -= 1
            dW2 = (a1.T).dot(delta3)
            db2 = np.sum(delta3, axis=0, keepdims=True)
            va = delta3.dot(self.W2.T)
            vb = 1 - np.power(a1, 2)
            delta2 = np.mat(np.array(va) * np.array(vb))
            dW1 = x.T.dot(delta2)
            db1 = np.sum(delta2, axis=0)

            # Add regularization terms (b1 and b2 don't have regularization terms)
            dW2 += self.reg_lambda * self.W2
            dW1 += self.reg_lambda * self.W1

            # Gradient descent parameter update
            self.W1 += -self.epsilon * dW1
            self.b1 += -self.epsilon * db1
            self.W2 += -self.epsilon * dW2
            self.b2 += -self.epsilon * db2

            del(data_index[rand_index])

隐藏层只有三个神经元时的效果图：

SVM

暂时还没写完

classfication_demo's Issues

这个在分类数据库方面设计上 ，有一定的积极作用吗？

能不能提供一下过文本训练可以让计算机写出特定风格的文章的那个项目的github地址

在common.py里有个报错

Z = predict_func(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)

ValueError: cannot reshape array of size 1 into shape (372,499)