self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost

这些是神经网络的一些属性，例如学习率 `self.learning_rate`，迭代次数 `self.num_iterations`，输入数据 `self.x` 和输出数据 `self.y`，权重 `self.w` 和偏置 `self.b`，成本函数值 `self.costs` 和一个布尔变量 `self.print_cost`，用于控制是否打印成本函数值。这些属性将在神经网络的训练过程中使用。

相关问题

import pandas as pd import warnings import sklearn.datasets import sklearn.linear_model import matplotlib import matplotlib.font_manager as fm import matplotlib.pyplot as plt import numpy as np import seaborn as sns data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx') print(data.info()) fig = plt.figure(figsize=(10, 8)) sns.heatmap(data.corr(), cmap="YlGnBu", annot=True) plt.title('相关性分析热力图') plt.rcParams['axes.unicode_minus'] = False plt.rcParams['font.sans-serif'] = 'SimHei' plt.show() y = data['y'] x = data.drop(['y'], axis=1) print('************************输出新的特征集数据***************************') print(x.head()) from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42) def relu(x): output=np.maximum(0, x) return output def relu_back_propagation(derror_wrt_output,x): derror_wrt_dinputs = np.array(derror_wrt_output, copy=True) derror_wrt_dinputs[x <= 0] = 0 return derror_wrt_dinputs def activated(activation_choose,x): if activation_choose == 'relu': return relu(x) def activated_back_propagation(activation_choose, derror_wrt_output, output): if activation_choose == 'relu': return relu_back_propagation(derror_wrt_output, output) class NeuralNetwork: def __init__(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output

这段代码存在两个问题：

1. 在import语句中没有使用逗号分隔符，应该在每个import语句之间添加逗号分隔符。
2. 在函数set_xy()的定义中，函数体没有缩进，应该在函数体内部添加缩进。

以下是修改后的代码：

import pandas as pd, warnings
import sklearn.datasets, sklearn.linear_model
import matplotlib, matplotlib.font_manager as fm
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

data = pd.read_excel(r'C:\Users\Lenovo\Desktop\data.xlsx')
print(data.info())

fig = plt.figure(figsize=(10, 8))
sns.heatmap(data.corr(), cmap="YlGnBu", annot=True)
plt.title('相关性分析热力图')
plt.rcParams['axes.unicode_minus'] = False
plt.rcParams['font.sans-serif'] = 'SimHei'
plt.show()

y = data['y']
x = data.drop(['y'], axis=1)

print('************************输出新的特征集数据***************************')
print(x.head())

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)

def relu(x):
    output=np.maximum(0, x)
    return output

def relu_back_propagation(derror_wrt_output,x):
    derror_wrt_dinputs = np.array(derror_wrt_output, copy=True)
    derror_wrt_dinputs[x <= 0] = 0
    return derror_wrt_dinputs

def activated(activation_choose,x):
    if activation_choose == 'relu':
        return relu(x)

def activated_back_propagation(activation_choose, derror_wrt_output, output):
    if activation_choose == 'relu':
        return relu_back_propagation(derror_wrt_output, output)

class NeuralNetwork:
    def __init__(self, layers_strcuture, print_cost = False):
        self.layers_strcuture = layers_strcuture
        self.layers_num = len(layers_strcuture)
        self.param_layers_num = self.layers_num - 1
        self.learning_rate = 0.0618
        self.num_iterations = 2000
        self.x = None
        self.y = None
        self.w = dict()
        self.b = dict()
        self.costs = []
        self.print_cost = print_cost
        self.init_w_and_b()
    
    def set_learning_rate(self,learning_rate):
        self.learning_rate=learning_rate 
    
    def set_num_iterations(self, num_iterations):
        self.num_iterations = num_iterations 
    
    def set_xy(self, input, expected_output):
        self.x = input
        self.y = expected_output

class NeuralNetwork: def __init__(self, layers_strcuture, print_cost = False): self.layers_strcuture = layers_strcuture self.layers_num = len(layers_strcuture) self.param_layers_num = self.layers_num - 1 self.learning_rate = 0.0618 self.num_iterations = 2000 self.x = None self.y = None self.w = dict() self.b = dict() self.costs = [] self.print_cost = print_cost self.init_w_and_b() def set_learning_rate(self,learning_rate): self.learning_rate=learning_rate def set_num_iterations(self, num_iterations): self.num_iterations = num_iterations def set_xy(self, input, expected_output): self.x = input self.y = expected_output

这段代码定义了一个名为NeuralNetwork的类，包含了类的构造函数__init__()和一些其他的方法。该类的构造函数__init__()接受一个参数layers_structure，表示神经网络的结构，即每一层的神经元数量。该类还包含了一些其他的属性和方法，包括：

- layers_num: 表示神经网络的层数
- param_layers_num: 表示神经网络的参数层数，即除去输入层和输出层的层数
- learning_rate: 表示神经网络的学习率
- num_iterations: 表示神经网络的迭代次数
- x: 表示输入数据
- y: 表示期望输出数据
- w: 表示神经网络的权重参数
- b: 表示神经网络的偏置参数
- costs: 表示每次迭代的损失值
- print_cost: 表示是否打印每次迭代的损失值

该类还包含了一些其他方法，包括set_learning_rate()、set_num_iterations()和set_xy()等，用于设置神经网络的学习率、迭代次数和输入数据等。这些方法可以在实例化该类后进行调用。