bpnn 分类python

BPNN（Back Propagation Neural Network）是一种常见的神经网络模型，用于分类和回归问题。在Python中，可以使用多种库来实现BPNN分类，如TensorFlow、Keras、PyTorch等。以下是一个使用Keras实现BPNN分类的示例代码：

from keras.models import Sequential
from keras.layers import Dense
from keras.utils import np_utils
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

# 加载数据集
iris = load_iris()
X, y = iris.data, iris.target

# 将标签转换为one-hot编码
y = np_utils.to_categorical(y)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# 构建模型
model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=100, batch_size=5)

# 评估模型
score = model.evaluate(X_test, y_test)
print('Test loss:', score[0])
print('Test accuracy:', score[1])