Python 3.x 如何获得逻辑回归的正确答案?
我在二进制分类问题上没有得到期望的结果 问题在于使用二元分类将乳腺癌标记为: -良性,或 -恶性的 它没有给出期望的输出 首先,有一个函数加载数据集,该数据集返回形状的测试和训练数据:Python 3.x 如何获得逻辑回归的正确答案?,python-3.x,machine-learning,neural-network,logistic-regression,Python 3.x,Machine Learning,Neural Network,Logistic Regression,我在二进制分类问题上没有得到期望的结果 问题在于使用二元分类将乳腺癌标记为: -良性,或 -恶性的 它没有给出期望的输出 首先,有一个函数加载数据集,该数据集返回形状的测试和训练数据: x_train is of shape: (30, 381), y_train is of shape: (1, 381), x_test is of shape: (30, 188), y_test is of shape: (1, 188). 然后是一个逻辑回归分类器类,用于预测输出 from skle
x_train is of shape: (30, 381),
y_train is of shape: (1, 381),
x_test is of shape: (30, 188),
y_test is of shape: (1, 188).
然后是一个逻辑回归分类器类,用于预测输出
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import numpy as np
def load_dataset():
cancer_data = load_breast_cancer()
x_train, x_test, y_train, y_test = train_test_split(cancer_data.data, cancer_data.target, test_size=0.33)
x_train = x_train.T
x_test = x_test.T
y_train = y_train.reshape(1, (len(y_train)))
y_test = y_test.reshape(1, (len(y_test)))
m = x_train.shape[1]
return x_train, x_test, y_train, y_test, m
class Neural_Network():
def __init__(self):
np.random.seed(1)
self.weights = np.random.rand(30, 1) * 0.01
self.bias = np.zeros(shape=(1, 1))
def sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def train(self, x_train, y_train, iterations, m, learning_rate=0.5):
for i in range(iterations):
z = np.dot(self.weights.T, x_train) + self.bias
a = self.sigmoid(z)
cost = (-1 / m) * np.sum(y_train * np.log(a) + (1 - y_train) * np.log(1 - a))
if (i % 500 == 0):
print("Cost after iteration %i: %f" % (i, cost))
dw = (1 / m) * np.dot(x_train, (a - y_train).T)
db = (1 / m) * np.sum(a - y_train)
self.weights = self.weights - learning_rate * dw
self.bias = self.bias - learning_rate * db
def predict(self, inputs):
m = inputs.shape[1]
y_predicted = np.zeros((1, m))
z = np.dot(self.weights.T, inputs) + self.bias
a = self.sigmoid(z)
for i in range(a.shape[1]):
y_predicted[0, i] = 1 if a[0, i] > 0.5 else 0
return y_predicted
if __name__ == "__main__":
'''
step-1 : Loading data set
x_train is of shape: (30, 381)
y_train is of shape: (1, 381)
x_test is of shape: (30, 188)
y_test is of shape: (1, 188)
'''
x_train, x_test, y_train, y_test, m = load_dataset()
neuralNet = Neural_Network()
'''
step-2 : Train the network
'''
neuralNet.train(x_train, y_train,10000,m)
y_predicted = neuralNet.predict(x_test)
print("Accuracy on test data: ")
print(accuracy_score(y_test, y_predicted)*100)
给出此输出的程序:
C:\Python36\python.exe C:/Users/LENOVO/PycharmProjects/MarkDmo001/Numpy.py
Cost after iteration 0: 5.263853
C:/Users/LENOVO/PycharmProjects/MarkDmo001/logisticReg.py:25: RuntimeWarning: overflow encountered in exp
return 1 / (1 + np.exp(-x))
C:/Users/LENOVO/PycharmProjects/MarkDmo001/logisticReg.py:33: RuntimeWarning: divide by zero encountered in log
cost = (-1 / m) * np.sum(y_train * np.log(a) + (1 - y_train) * np.log(1 - a))
C:/Users/LENOVO/PycharmProjects/MarkDmo001/logisticReg.py:33: RuntimeWarning: invalid value encountered in multiply
cost = (-1 / m) * np.sum(y_train * np.log(a) + (1 - y_train) * np.log(1 - a))
Cost after iteration 500: nan
Cost after iteration 1000: nan
Cost after iteration 1500: nan
Cost after iteration 2000: nan
Cost after iteration 2500: nan
Cost after iteration 3000: nan
Cost after iteration 3500: nan
Cost after iteration 4000: nan
Cost after iteration 4500: nan
Cost after iteration 5000: nan
Cost after iteration 5500: nan
Cost after iteration 6000: nan
Cost after iteration 6500: nan
Cost after iteration 7000: nan
Cost after iteration 7500: nan
Cost after iteration 8000: nan
Cost after iteration 8500: nan
Cost after iteration 9000: nan
Cost after iteration 9500: nan
Accuracy:
0.0
问题是梯度爆炸。您需要将输入标准化为
[0,1]
如果查看训练数据中的功能3和功能23,您将看到大于3000的值。在这些值与初始权重相乘后,它们仍然位于范围[0,30]
内。因此,在第一次迭代中,z
向量只包含值高达50左右的正数。因此,a
向量(乙状结肠的输出)如下所示:
[0.9994797 0.99853904 0.99358676 0.99999973 0.98392862 0.99983016 0.99818802 ...]
所以在第一步中,你的模型总是以很高的置信度预测1。但这并不总是正确的,而且模型输出导致大梯度的概率很高,当您查看dw
的最高值时可以看到。就我而言
388dw[3]
was 571dw[23]
[0,55]
中。因此,您可以清楚地看到这些特性中的大量输入是如何导致梯度爆炸的。因为梯度下降现在会朝相反的方向迈出一大步,所以下一步的权重不是[0,0.01]
,而是[-285,0.002]
,这只会让事情变得更糟。在下一次迭代中,z
包含的值约为-100万,这会导致sigmoid函数溢出
解决方案
[0,1]
[-0.01,0.01]
中使用权重,以便它们大致相互抵消。否则,您在z
中的值仍然与您拥有的特征数成线性比例.T
s是因为sklearn希望训练输入具有形状(num_样本,num_特征)
,而你的x_训练和x_测试具有形状(num_特征,num_样本)
,我按照你说的做了,但我没有得到输出,请告诉我如何应用解决方案2,我确实尝试了不同的权重,但仍然得到了相同的输出。我复制了上面的代码,只是用我答案中的代码块替换了第60到71行。然后,我得到了0.06的损失和97.8%的准确率。顺便说一下,为了评估准确性,您需要调用print(准确性评分(y\u测试[0],y\u预测[0])*100)
,因为y\u测试
和y\u预测
是二维数组。
x_train, x_test, y_train, y_test, m = load_dataset()
scaler = MinMaxScaler()
x_train_normalized = scaler.fit_transform(x_train.T).T
neuralNet = Neural_Network()
'''
step-2 : Train the network
'''
neuralNet.train(x_train_normalized, y_train,10000,m)
# Use the same transformation on the test inputs as on the training inputs
x_test_normalized = scaler.transform(x_test.T).T
y_predicted = neuralNet.predict(x_test_normalized)