Keras:分类报告精度与多类的model.predict精度不同

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

# 生成示例数据
X = np.random.rand(1000, 10)
y = np.random.randint(0, 3, size=(1000,))

# 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, random_state=42)

# 构建模型
model = Sequential()
model.add(Dense(64, input_dim=10, activation='relu'))
model.add(Dense(3, activation='softmax'))

# 编译模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 训练模型
model.fit(X_train, y_train, epochs=10, batch_size=32)

# 预测
y_pred = np.argmax(model.predict(X_test), axis=1)

# 生成分类报告
print(classification_report(y_test, y_pred))