cnn图像分类代码python
时间: 2023-08-06 15:11:50 浏览: 62
下面是一个基于卷积神经网络的图像分类代码的示例,使用Python和Keras库:
```python
from keras.models import Sequential
from keras.layers import Dense, Conv2D, Flatten, MaxPooling2D
from keras.datasets import mnist
from keras.utils import to_categorical
# 加载MNIST数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 数据预处理
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 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, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(10, activation='softmax'))
# 编译模型
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
# 训练模型
model.fit(X_train, y_train, epochs=10, 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])
```
此代码使用了一个简单的卷积神经网络模型,对MNIST数据集进行分类。该模型包括两个卷积层和两个全连接层。在训练过程中,使用了Adam优化器和交叉熵损失函数。最终,该模型在测试集上的准确率约为99%。