CNN神经网络图像分类 代码

以下是使用Keras构建CNN神经网络进行图像分类的示例代码：

# 导入必要的库
import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import to_categorical

# 加载数据集
from keras.datasets import cifar10
(x_train, y_train), (x_test, y_test) = cifar10.load_data()

# 数据预处理
x_train = x_train.astype('float32') / 255
x_test = x_test.astype('float32') / 255
y_train = to_categorical(y_train, 10)
y_test = to_categorical(y_test, 10)

# 构建模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

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

# 训练模型
model.fit(x_train, y_train,
          batch_size=128,
          epochs=10,
          verbose=1,
          validation_data=(x_test, y_test))

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

该代码使用了Keras库，首先加载了CIFAR-10数据集，然后对数据进行预处理，接着构建了一个包含两个卷积层和两个全连接层的CNN模型，并使用Adam优化器进行训练。最后，评估了模型在测试集上的性能。