np.argmax(y_pre, axis=1是做什么
时间: 2024-04-29 19:20:05 浏览: 10
这是在一个预测结果的概率分布中,返回最大概率对应的类别标签。其中,y_pre是一个形状为(batch_size, num_classes)的矩阵,表示每个样本属于每个类别的概率分布。axis=1表示在每一行中找到最大值的索引,也就是最可能的类别标签。函数返回一个形状为(batch_size,)的一维数组,其中每个元素都是对应样本的最可能的类别标签。
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label_pre = np.argmax(y_pre, axis=1)
这段代码的作用是将神经网络的输出 y_pre 转换为对应的类别标签。其中,np.argmax() 函数的作用是返回数组中最大值的索引,axis=1 表示在每行中查找最大值的索引。由于 y_pre 是一个二维数组,每行表示一个样本在各个类别上的置信度,因此对 y_pre 沿着行的方向进行 np.argmax() 操作即可得到每个样本的预测标签。最终得到的 label_pre 是一个一维数组,包含了所有样本的预测标签。
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.