cnn keras 代码实现
时间: 2023-08-30 10:09:11 浏览: 46
以下是一个基于Keras的简单CNN代码实现示例:
```python
# 导入必要的库
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.utils import np_utils
# 加载数据集
from keras.datasets import mnist
(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 = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
# 定义模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
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=32,
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])
```
这个示例实现了一个简单的CNN,用于对MNIST手写数字数据集进行分类。该模型包括两个卷积层,一个池化层和两个全连接层。在训练期间,该模型将对MNIST数据集进行10个epoch的训练,并输出测试损失和测试准确性。