python写卷积神经网络

要使用Python编写卷积神经网络，你可以使用深度学习库例如TensorFlow或PyTorch。下面是一个用TensorFlow编写卷积神经网络的示例代码：

import tensorflow as tf

# 定义卷积神经网络模型
def conv_net(x):
    # 输入层
    input_layer = tf.reshape(x, [-1, 28, 28, 1])  # 将输入的图像数据reshape为合适的形状

    # 卷积层
    conv1 = tf.layers.conv2d(inputs=input_layer, filters=32, kernel_size=[5, 5], activation=tf.nn.relu)
    # 池化层
    pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)

    # 卷积层
    conv2 = tf.layers.conv2d(inputs=pool1, filters=64, kernel_size=[5, 5], activation=tf.nn.relu)
    # 池化层
    pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)

    # 全连接层
    flatten = tf.layers.flatten(pool2)  # 将池化层的输出展平为一维向量
    dense = tf.layers.dense(inputs=flatten, units=1024, activation=tf.nn.relu)

    # 输出层
    logits = tf.layers.dense(inputs=dense, units=10)

    return logits


# 使用卷积神经网络进行手写数字识别
def handwritten_digit_recognition():
    # 加载手写数字数据集
    (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

    # 数据预处理
    x_train = x_train / 255.0
    x_test = x_test / 255.0

    # 定义占位符
    x = tf.placeholder(tf.float32, [None, 28, 28])
    y = tf.placeholder(tf.int32, [None])

    # 构建模型
    logits = conv_net(x)

    # 定义损失函数
    loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=y))

    # 优化器
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(loss_op)

    # 准确率评估
    correct_pred = tf.equal(tf.argmax(logits, 1), tf.cast(y, tf.int64))
    accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

    # 创建会话并训练模型
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())

        # 训练参数设置
        num_epochs = 10  # 迭代次数
        batch_size = 128  # 每批次样本数量

        for epoch in range(num_epochs):
            num_batches = x_train.shape // batch_size
            for batch in range(num_batches):
                batch_x = x_train