卷积神经网络实现手写数字识别python代码实现

很高兴能为您解答问题！以下是手写数字识别的Python代码实现：

# 导入必要的包
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

# 加载MNIST数据集
(train_X, train_y), (test_X, test_y) = mnist.load_data()

# 对数据进行预处理
train_X = train_X.reshape((train_X.shape[0], 28, 28, 1))
test_X = test_X.reshape((test_X.shape[0], 28, 28, 1))
train_X, test_X = train_X / 255.0, test_X / 255.0
train_y = to_categorical(train_y)
test_y = to_categorical(test_y)

# 定义模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D((2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dense(10, activation='softmax'))

# 编译模型并训练
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(train_X, train_y, epochs=5, batch_size=64, verbose=1, validation_data=(test_X, test_y))

# 评估模型
model.evaluate(test_X, test_y, verbose=2)

这段代码实现了用卷积神经网络识别手写数字。 如果您有更多的问题，欢迎随时向我提问！