手写数字识别python卷积神经网络

手写数字识别通常使用Python中的卷积神经网络（Convolutional Neural Networks, CNN），因为CNN特别适合处理图像数据。在Keras或TensorFlow库中，你可以按照以下步骤构建一个简单的CNN模型来进行MNIST数据集的手写数字识别：

1. 导入必要的库：

import tensorflow as tf
from tensorflow.keras import layers

2. 加载并预处理MNIST数据集：

(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()
x_train = x_train.reshape(-1, 28, 28, 1).astype('float32') / 255
x_test = x_test.reshape(-1, 28, 28, 1).astype('float32') / 255
y_train = tf.keras.utils.to_categorical(y_train, 10)
y_test = tf.keras.utils.to_categorical(y_test, 10)

3. 构建CNN模型：

model = tf.keras.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.5),
    layers.Dense(10, activation='softmax')
])

4. 编译并训练模型：

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, validation_data=(x_test, y_test))

5. 测试模型性能：

test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Test accuracy: {test_acc}")