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paddle_temp

作者:互联网

import paddle
from paddle.io import Dataset

BATCH_SIZE = 64
BATCH_NUM = 20

IMAGE_SIZE = [17]
CLASS_NUM = 5

import numpy as np 

class MyDataset(Dataset):
    """
    步骤一:继承paddle.io.Dataset类
    """
    def __init__(self, num_samples):
        """
        步骤二:实现构造函数,定义数据集大小
        """
        super(MyDataset, self).__init__()
        self.num_samples = num_samples
        self.data = np.random.randn(5,17)


    def __getitem__(self, index):
        """
        步骤三:实现__getitem__方法,定义指定index时如何获取数据,并返回单条数据(训练数据,对应的标签)
        """
        # data = paddle.uniform(IMAGE_SIZE, dtype='float32')
        # print(type(data))
        data = self.data[index].reshape(17)
        # data = np.dtype(np.float32)
        data = data.astype(np.float32)
        # label = paddle.randint(0, CLASS_NUM-1, dtype='int64')
        label = np.array(np.random.randint(4)).reshape(1)
        print(label)
        return data, label

    def __len__(self):
        """
        步骤四:实现__len__方法,返回数据集总数目
        """
        return self.num_samples

# 测试定义的数据集
custom_dataset = MyDataset(5)

print('=============custom dataset=============')
for data, label in custom_dataset:
    print(data.shape, label.shape)
    print(data, label)
    break

mnist = paddle.nn.Sequential(
    paddle.nn.Flatten(),
    paddle.nn.Linear(17, 512),
    paddle.nn.ReLU(),
    paddle.nn.Dropout(0.2),
    paddle.nn.Linear(512, 5)
)
# 预计模型结构生成模型对象,便于进行后续的配置、训练和验证
model = paddle.Model(mnist)

# 模型训练相关配置,准备损失计算方法,优化器和精度计算方法
model.prepare(paddle.optimizer.Adam(parameters=model.parameters()),
                paddle.nn.CrossEntropyLoss(),
                paddle.metric.Accuracy())

# 开始模型训练
model.fit(custom_dataset,
            epochs=5,
            batch_size=64,
            verbose=1)

标签:__,nn,temp,self,paddle,label,data
来源: https://www.cnblogs.com/hiccuplh/p/16217501.html