CNN卷积神经网络手写数字识别 ，python代码

好的，下面是一个简单的手写数字识别的卷积神经网络的 Python 代码：

1. 首先导入需要的库：

import numpy as np
import tensorflow as tf
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, Dropout

2. 加载 MNIST 数据集：

(x_train, y_train), (x_test, y_test) = mnist.load_data()

3. 对数据进行预处理：

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

4. 将标签进行 one-hot 编码：

y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

5. 构建卷积神经网络模型：

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(Conv2D(64, kernel_size=(3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))

6. 编译模型：

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

7. 训练模型：

model.fit(x_train, y_train, batch_size=128, epochs=10, validation_data=(x_test, y_test))

8. 在测试集上评估模型：

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

这段代码实现了一个简单的手写数字识别的卷积神经网络模型，可以在 MNIST 数据集上得到约 99% 的准确率。