y_test_predict = mlp.predict_classes(X_test_normal) accuracy_test = accuracy_score(y_test,y_test_predict) print(accuracy_test)
时间: 2023-09-10 18:15:51 浏览: 44
同样,`predict_classes` 方法在 Keras 2.4.0 或更高版本已经被弃用,建议使用 `predict` 方法和 `argmax` 函数来获取预测结果的类别。修改后的代码示例如下:
```
# 生成模型在测试集上的预测概率分布
probabilities = mlp.predict(X_test_normal)
# 获取预测结果的类别
y_test_predict = np.argmax(probabilities, axis=-1)
# 计算模型在测试集上的准确率
accuracy_test = accuracy_score(y_test, y_test_predict)
print(accuracy_test)
```
这样就可以避免上面提到的 `AttributeError: 'Sequential' object has no attribute 'predict_classes'` 错误,并且计算出测试集上的准确率。
相关问题
y_train_predict = mlp.predict(X_train) a = np.ones(275) b = a / 2 c = np.insert(y_train_predict, 0, b, axis=1) y_train_predict = np.argmax(c, axis=1) y_train_predict = y_train_predict.reshape(275, 1) from sklearn.metrics import accuracy_score accuracy_train = accuracy_score(y_train, y_train_predict) print(accuracy_train)
The code snippet you provided calculates the training accuracy of a multi-layer perceptron (MLP) model. Here's the modified code with the accuracy calculation and printing:
```python
y_train_predict = mlp.predict(X_train)
a = np.ones(275)
b = a / 2
c = np.insert(y_train_predict, 0, b, axis=1)
y_train_predict = np.argmax(c, axis=1)
y_train_predict = y_train_predict.reshape(275, 1)
from sklearn.metrics import accuracy_score
accuracy_train = accuracy_score(y_train, y_train_predict)
print(accuracy_train)
```
This code computes the predictions `y_train_predict` of the MLP model on the training data `X_train`. It then creates an array `b` filled with ones and divides it by 2 to get an array of half ones. This array is inserted as the first column of `y_train_predict` using `np.insert()`. The `argmax()` function is then applied to `y_train_predict` to get the predicted class labels. Finally, the accuracy between the true training labels `y_train` and the predicted labels `y_train_predict` is calculated using `accuracy_score()` from the `sklearn.metrics` module, and printed out.
多层感知机(MLP)模型没有提供 accuracy_score 函数怎么办
如果多层感知机(MLP)模型没有提供 accuracy_score 函数,你可以使用其他方法来计算模型的准确率。以下是一种可能的方法:
1. 首先,使用 MLP 模型对测试数据进行预测,得到预测结果。
2. 将预测结果与真实标签进行比较,计算预测准确的样本数量。
3. 将预测准确的样本数量除以总样本数量,得到准确率。
下面是一个示例代码片段,展示如何使用 sklearn 库中的 accuracy_score 函数计算 MLP 模型的准确率:
```python
from sklearn.metrics import accuracy_score
# 假设你已经训练好了 MLP 模型,并有测试数据 X_test 和对应的真实标签 y_true
y_pred = model.predict(X_test) # 使用 MLP 模型进行预测
accuracy = accuracy_score(y_true, y_pred) # 计算准确率
print("准确率:", accuracy)
```
请注意,上述代码中的 `model` 是你训练好的 MLP 模型,`X_test` 是测试数据,`y_true` 是对应的真实标签。你需要根据实际情况进行相应的调整。
如果你使用的是其他库或框架来构建 MLP 模型,可以查看该库或框架的文档,了解如何计算模型的准确率。