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
时间: 2024-03-08 15:50:47 浏览: 33
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:
```python
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
```
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