基于paddlepaddle框架采用神经网络BP分类CIFAR10数据集，输出目标函数损失+训练数据平均准确率+测试数据平均准确+测试数据各类别的分类准确率 Python详细代码

好的，以下是基于PaddlePaddle框架采用神经网络BP分类CIFAR10数据集的Python代码：

import paddle
import paddle.fluid as fluid
import numpy as np

# 定义数据集
def load_data():
    trainset, testset = paddle.dataset.cifar10.load()
    train_reader = paddle.batch(
        paddle.reader.shuffle(
            trainset, buf_size=50000),
        batch_size=128)
    test_reader = paddle.batch(
        paddle.reader.shuffle(
            testset, buf_size=10000),
        batch_size=128)
    return train_reader, test_reader

# 定义神经网络
def convolutional_neural_network(image, type_size):
    # 第一个卷积-池化层
    conv_pool_1 = fluid.layers.conv2d(
        input=image,
        num_filters=32,
        filter_size=5,
        act="relu")
    conv_pool_1 = fluid.layers.pool2d(
        input=conv_pool_1,
        pool_size=2,
        pool_type='max',
        pool_stride=2)

    # 第二个卷积-池化层
    conv_pool_2 = fluid.layers.conv2d(
        input=conv_pool_1,
        num_filters=64,
        filter_size=5,
        act="relu")
    conv_pool_2 = fluid.layers.pool2d(
        input=conv_pool_2,
        pool_size=2,
        pool_type='max',
        pool_stride=2)

    # 第三个卷积-池化层
    conv_pool_3 = fluid.layers.conv2d(
        input=conv_pool_2,
        num_filters=128,
        filter_size=5,
        act="relu")
    conv_pool_3 = fluid.layers.pool2d(
        input=conv_pool_3,
        pool_size=2,
        pool_type='max',
        pool_stride=2)

    # 全连接层
    fc_1 = fluid.layers.fc(input=conv_pool_3, size=512, act='relu')
    fc_2 = fluid.layers.fc(input=fc_1, size=type_size, act='softmax')
    return fc_2

# 训练函数
def train_program():
    # 定义输入数据和标签
    image = fluid.layers.data(name='image', shape=[3, 32, 32], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    # 获取分类器
    type_size = 10
    predict = convolutional_neural_network(image, type_size)

    # 定义损失函数
    cost = fluid.layers.cross_entropy(input=predict, label=label)
    avg_cost = fluid.layers.mean(cost)

    # 定义准确率函数
    accuracy = fluid.layers.accuracy(input=predict, label=label)

    # 定义优化方法
    optimizer = fluid.optimizer.AdamOptimizer(learning_rate=0.001)
    optimizer.minimize(avg_cost)

    # 定义数据读取器
    train_reader, _ = load_data()

    # 定义Executor
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    # 开始训练
    epochs = 10
    for epoch in range(epochs):
        for batch_id, data in enumerate(train_reader()):
            train_cost, train_acc = exe.run(
                program=fluid.default_main_program(),
                feed={'image': np.array([x[0] for x in data]).astype('float32'),
                      'label': np.array([x[1] for x in data]).astype('int64')},
                fetch_list=[avg_cost, accuracy])
            if batch_id % 100 == 0:
                print('Epoch: {}, Batch: {}, Cost: {}, Accuracy: {}'.format(
                    epoch, batch_id, train_cost[0], train_acc[0]))

# 测试函数
def test_program():
    # 定义输入数据和标签
    image = fluid.layers.data(name='image', shape=[3, 32, 32], dtype='float32')
    label = fluid.layers.data(name='label', shape=[1], dtype='int64')

    # 获取分类器
    type_size = 10
    predict = convolutional_neural_network(image, type_size)

    # 定义准确率函数
    accuracy = fluid.layers.accuracy(input=predict, label=label)

    # 定义数据读取器
    _, test_reader = load_data()

    # 定义Executor
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())

    # 开始测试
    test_accs = []
    test_costs = []
    test_class_accs = np.zeros(10)
    for batch_id, data in enumerate(test_reader()):
        test_cost, test_acc, test_predict = exe.run(
            program=fluid.default_main_program(),
            feed={'image': np.array([x[0] for x in data]).astype('float32'),
                  'label': np.array([x[1] for x in data]).astype('int64')},
            fetch_list=[avg_cost, accuracy, predict])

        test_accs.append(test_acc[0])
        test_costs.append(test_cost[0])

        # 统计每个类别的分类准确率
        for i in range(len(test_predict)):
            class_idx = data[i][1]
            class_accs = (np.argmax(test_predict[i]) == class_idx)
            test_class_accs[class_idx] += class_accs

    # 输出结果
    print('Test Cost: {}, Test Accuracy: {}, Test Class Accuracy: {}'.format(
        np.mean(test_costs), np.mean(test_accs), test_class_accs / 1000))

# 开始训练和测试
train_program()
test_program()

这个代码定义了一个三层卷积神经网络，使用Adam优化器进行训练，使用准确率和损失函数进行评估，最终输出训练数据的平均准确率、测试数据的平均准确率和测试数据各类别的分类准确率。