pycharm深度学习预测波士顿房价代码

以下是一个使用PyCharm编写的深度学习预测波士顿房价的代码，其中使用了神经网络模型和相关的科学计算库、数据交换格式包和绘图工具包：

# 导入相关库
import numpy as np
import json
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

# 定义神经网络类
class Network(object):
    def __init__(self, sizes):
        self.num_layers = len(sizes)
        self.sizes = sizes
        self.biases = [np.zeros((y, 1)) for y in sizes[1:]]
        self.weights = [np.random.randn(y, x) for x, y in zip(sizes[:-1], sizes[1:])]

    def feedforward(self, a):
        for b, w in zip(self.biases, self.weights):
            a = sigmoid(np.dot(w, a) + b)
        return a

    def SGD(self, training_data, epochs, mini_batch_size, eta, test_data=None):
        if test_data: n_test = len(test_data)
        n = len(training_data)
        for j in range(epochs):
            np.random.shuffle(training_data)
            mini_batches = [training_data[k:k+mini_batch_size] for k in range(0, n, mini_batch_size)]
            for mini_batch in mini_batches:
                self.update_mini_batch(mini_batch, eta)
            if test_data:
                print("Epoch {0}: {1} / {2}".format(j, self.evaluate(test_data), n_test))
            else:
                print("Epoch {0} complete".format(j))

    def update_mini_batch(self, mini_batch, eta):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        for x, y in mini_batch:
            delta_nabla_b, delta_nabla_w = self.backprop(x, y)
            nabla_b = [nb+dnb for nb, dnb in zip(nabla_b, delta_nabla_b)]
            nabla_w = [nw+dnw for nw, dnw in zip(nabla_w, delta_nabla_w)]
        self.weights = [w-(eta/len(mini_batch))*nw for w, nw in zip(self.weights, nabla_w)]
        self.biases = [b-(eta/len(mini_batch))*nb for b, nb in zip(self.biases, nabla_b)]

    def backprop(self, x, y):
        nabla_b = [np.zeros(b.shape) for b in self.biases]
        nabla_w = [np.zeros(w.shape) for w in self.weights]
        activation = x
        activations = [x]
        zs = []
        for b, w in zip(self.biases, self.weights):
            z = np.dot(w, activation)+b
            zs.append(z)
            activation = sigmoid(z)
            activations.append(activation)
        delta = self.cost_derivative(activations[-1], y) * sigmoid_prime(zs[-1])
        nabla_b[-1] = delta
        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
        for l in range(2, self.num_layers):
            z = zs[-l]
            sp = sigmoid_prime(z)
            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
            nabla_b[-l] = delta
            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
        return (nabla_b, nabla_w)

    def evaluate(self, test_data):
        test_results = [(np.argmax(self.feedforward(x)), y) for (x, y) in test_data]
        return sum(int(x == y) for (x, y) in test_results)

    def cost_derivative(self, output_activations, y):
        return (output_activations-y)

# 定义sigmoid函数和其导数
def sigmoid(z):
    return 1.0/(1.0+np.exp(-z))

def sigmoid_prime(z):
    return sigmoid(z)*(1-sigmoid(z))

# 加载数据集
with open('housing.data', 'r') as f:
    data = f.readlines()
    data = [list(map(float, x.strip().split())) for x in data]
    data = [(np.array(x[:-1]).reshape(13, 1), x[-1]) for x in data]

# 划分训练集和测试集
training_data, test_data = data[:-10], data[-10:]

# 初始化神经网络
net = Network([13, 5, 1])

# 训练神经网络
net.SGD(training_data, 1000, 10, 0.5, test_data=test_data)

# 绘制预测结果
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = [x[0][0] for x in test_data]
y = [x[0][1] for x in test_data]
z = [x[1] for x in test_data]
ax.scatter(x, y, z, c='r', marker='o')
x = np.arange(0, 1, 0.1)
y = np.arange(0, 1, 0.1)
x, y = np.meshgrid(x, y)
z = np.array([net.feedforward(np.array([xi, yi]).reshape(2, 1))[0][0] for xi, yi in zip(x, y)])
ax.plot_surface(x, y, z)
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')
plt.show()