波士顿房价问题 运行出现bug
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#加载飞桨和相关类库 import paddle from paddle.nn import Linear import paddle.nn.functional as F import os import numpy as np import matplotlib.pyplot as plt def load_data(): # 读入训练数据 datafile = "./dataset/housing.data" data = np.fromfile(datafile, sep=' ') #print(data) feature_names = [ 'CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE','DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV' ] feature_num = len(feature_names) data = data.reshape([data.shape[0] // feature_num, feature_num]) print(data.shape[0]) x = data[0] print(x.shape) print(x) # 划分数据集 ratio = 0.8 offset = int(data.shape[0] * ratio) training_data = data[:offset] training_data.shape # 计算train数据集的最大值,最小值,平均值 maximums, minimums, avgs = training_data.max(axis=0), training_data.min(axis=0), \ training_data.sum(axis=0) / training_data.shape[0] #print(maximums, minimums, avgs) # 记录数据的归一化参数,在预测时对数据做归一化 global max_values global min_values global avg_values max_values = maximums min_values = minimums avg_values = avgs # 对数据进行归一化处理 for i in range(feature_num): #print(maximums[i], minimums[i], avgs[i]) data[:, i] = (data[:, i] - minimums[i]) / (maximums[i] - minimums[i]) # 使得每个特征的取值缩放到0~1之间。 training_data = data[:offset] test_data = data[offset:] return training_data, test_data class Regressor(paddle.nn.Layer): # self代表类的实例自身 def __init__(self): # 初始化父类中的一些参数 super(Regressor, self).__init__() # 定义一层全连接层,输入维度是13,输出维度是1 self.fc = paddle.nn.Linear(in_features=13, out_features=1) # 网络的前向计算 def forward(self, inputs): x = self.fc(inputs) return x # 声明定义好的线性回归模型 model = Regressor() # 开启模型训练模式 model.train() # 加载数据 training_data, test_data = load_data() # 定义优化算法,使用随机梯度下降SGD # 学习率设置为0.01 opt = paddle.optimizer.SGD(learning_rate=0.01, parameters=model.parameters()) EPOCH_NUM = 10 # 设置外层循环次数 BATCH_SIZE = 10 # 设置batch大小 # 定义外层循环 for epoch_id in range(EPOCH_NUM): # 在每轮迭代开始之前,将训练数据的顺序随机的打乱 np.random.shuffle(training_data) # 将训练数据进行拆分,每个batch包含10条数据 mini_batches = [training_data[k:k + BATCH_SIZE] for k in range(0, len(training_data), BATCH_SIZE)] # 定义内层循环 for iter_id, mini_batch in enumerate(mini_batches): x = np.array(mini_batch[:, :-1]) # 获得当前批次训练数据 y = np.array(mini_batch[:, -1:]) # 获得当前批次训练标签(真实房价) # 将numpy数据转为飞桨动态图tensor形式 house_features = paddle.to_tensor(x) prices = paddle.to_tensor(y) print(house_features.shape) # 前向计算 predicts = model(house_features) # 计算损失 loss = F.square_error_cost(predicts, label=prices) avg_loss = paddle.mean(loss) if iter_id % 20 == 0: print("epoch: {}, iter: {}, loss is: {}".format(epoch_id, iter_id, avg_loss.numpy())) # 反向传播 avg_loss.backward() # 最小化loss,更新参数 opt.step() # 清除梯度 opt.clear_grad()
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house_features = paddle.to_tensor(x)
应该是97行这句但是一直不知道怎么改
可尝试将以下代码进行修改哈:
```
x = np.array(mini_batch[:, :-1]) # 获得当前批次训练数据
y = np.array(mini_batch[:, -1:]) # 获得当前批次训练标签(真实房价)
```
变更为
```
x = np.array(mini_batch[:, :-1]).astype("float32") # 获得当前批次训练数据
y = np.array(mini_batch[:, -1:]).astype("float32") # 获得当前批次训练标签(真实房价)
```
paddle的网络一般要求输入的浮点数为”float32“格式,输入的类别标签为”int64“格式