cnn数字识别python
时间: 2023-12-11 21:33:22 浏览: 26
卷积神经网络(CNN)是一种在计算机视觉任务中表现优异的深度学习模型,可以用于图像分类、目标检测、图像分割等任务。下面是使用Python和Keras库实现手写数字识别的例子:
```python
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.utils import np_utils
# 加载MNIST数据集
(X_train, y_train), (X_test, y_test) = mnist.load_data()
# 将数据集变为4维张量
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1).astype('float32')
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1).astype('float32')
# 数据归一化
X_train /= 255
X_test /= 255
# 将标签转换为one-hot编码
y_train = np_utils.to_categorical(y_train, 10)
y_test = np_utils.to_categorical(y_test, 10)
# 创建模型
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=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])
```
上述代码中,我们使用了Keras库来构建卷积神经网络模型。首先,我们加载了MNIST数据集,并将其变为4维张量。然后,我们对数据进行了归一化和标签的one-hot编码。接着,我们创建了一个包含两个卷积层、一个池化层和两个全连接层的卷积神经网络模型。最后,我们编译模型并训练它,然后评估模型的性能。