如何将模型输入到KNN分类算法中?

发布于 2025-02-02 14:19:11 字数 608 浏览 3 评论 0 原文

我想使用KNN进行图像泥浆化。我使用制作模型。我有20张狗类别中的10张图像,而猫类类别有10张图像。我很难将模型进入KNN算法,我的编码存在问题。这是我的代码:

knn_model=KNeighborsClassifier(n_neighbors=3) #define K=3
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=0)
predict_knn=knn_model.predict(X_test)
print(predict_knn)

有一个错误:找到的输入变量,示例数量不一致:[60,20]

我需要您的意见如何修复此代码。谢谢。

I want to make image clasification using KNN. i use https://pythonprogramming.net/loading-custom-data-deep-learning-python-tensorflow-keras/ to make a model. i have 20 image which 10 image in dog category and 10 image in cat category. I'm having trouble entering the model into the KNN algorithm,there is a problem in my coding. this is my code:

knn_model=KNeighborsClassifier(n_neighbors=3) #define K=3
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=0)
predict_knn=knn_model.predict(X_test)
print(predict_knn)

there is an error : found input variables with inconsistent numbers of samples: [60, 20]

I need your opinion how to fix this code. thank you.

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我还不会笑 2025-02-09 14:19:11

问题可能是由于 x y 不一致的样本尺寸

1。 len(y)== 20
# Works
import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.arange(20*32*32*3).reshape((20, 32, 32, 3)), list(range(20))
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=0)
2。 len(y)== 60
# Does not work
X, y = np.arange(20*32*32*3).reshape((20, 32, 32, 3)), list(range(60))
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=0)

第二个脚本会产生以下错误。

The problem could be due to the inconsistent sample size of X and y.

1. len(y) == 20
# Works
import numpy as np
from sklearn.model_selection import train_test_split

X, y = np.arange(20*32*32*3).reshape((20, 32, 32, 3)), list(range(20))
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=0)
2. len(y) == 60
# Does not work
X, y = np.arange(20*32*32*3).reshape((20, 32, 32, 3)), list(range(60))
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=0)

The second script produces the below error.

enter image description here

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