使用paddle写一段数据格式为二维矩阵的14类目的分类网络

好的，以下是一个简单的使用 PaddlePaddle 实现数据格式为二维矩阵的14类目的分类网络的示例代码：

import paddle
import paddle.nn.functional as F

# 定义网络结构
class Net(paddle.nn.Layer):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = paddle.nn.Linear(28*28, 512)
        self.fc2 = paddle.nn.Linear(512, 256)
        self.fc3 = paddle.nn.Linear(256, 14)

    def forward(self, x):
        x = paddle.flatten(x, start_axis=1, stop_axis=-1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义训练和测试函数
def train(model, train_loader, optimizer):
    model.train()
    for batch_id, data in enumerate(train_loader()):
        x_data = data[0]
        y_data = paddle.to_tensor(data[1])
        y_data = paddle.nn.functional.one_hot(y_data, num_classes=14)
        logits = model(x_data)
        loss = paddle.nn.functional.binary_cross_entropy_with_logits(logits, y_data)
        avg_loss = paddle.mean(loss)
        if batch_id % 100 == 0:
            print("epoch: {}, batch_id: {}, loss is: {}".format(epoch, batch_id, avg_loss.numpy()))
        avg_loss.backward()
        optimizer.step()
        optimizer.clear_grad()

def test(model, test_loader):
    model.eval()
    correct = 0
    total = 0
    for data in test_loader():
        x_data = data[0]
        y_data = paddle.to_tensor(data[1])
        y_data = paddle.nn.functional.one_hot(y_data, num_classes=14)
        logits = model(x_data)
        pred = F.sigmoid(logits)
        pred = paddle.argmax(pred, axis=1)
        total += y_data.shape[0]
        correct += (pred == paddle.argmax(y_data, axis=1)).numpy().sum()
    acc = correct / total
    print("test accuracy: {}".format(acc))

# 加载数据
train_dataset = paddle.vision.datasets.MNIST(mode='train')
test_dataset = paddle.vision.datasets.MNIST(mode='test')

train_loader = paddle.io.DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = paddle.io.DataLoader(test_dataset, batch_size=32, shuffle=False)

# 创建模型和优化器
model = Net()
optimizer = paddle.optimizer.Adam(parameters=model.parameters())

# 训练和测试模型
EPOCH_NUM = 5
for epoch in range(EPOCH_NUM):
    train(model, train_loader, optimizer)
    test(model, test_loader)

这里使用了 MNIST 数据集作为示例数据集，模型结构为三层全连接层（FC），分别为 512、256 和 14 个神经元。在训练和测试过程中，使用了二元交叉熵损失（binary_cross_entropy_with_logits）和准确率作为评估指标。