Question

我想使用个人数据库在PyTorch上训练一个简单的神经网络。此数据库从Excel文件导入并存储在df。

中

其中一列名为"Target"，它是网络的目标变量。如何使用此数据框作为PyTorch神经网络的输入？

我试过这个，但它不起作用：

target = pd.DataFrame(data = df['Target'])
train = data_utils.TensorDataset(df, target)
train_loader = data_utils.DataLoader(train, batch_size = 10, shuffle = True)

Answer 1

您可以使用以下函数将任何数据框或熊猫系列转换为pytorch张量

import pandas as pd
import torch

# determine the supported device
def get_device():
    if torch.cuda.is_available():
        device = torch.device('cuda:0')
    else:
        device = torch.device('cpu') # don't have GPU 
    return device

# convert a df to tensor to be used in pytorch
def df_to_tensor(df):
    device = get_device()
    return torch.from_numpy(df.values).float().to(device)

df_tensor = df_to_tensor(df)
series_tensor = df_to_tensor(series)

Answer 2

也许试试看它是否能解决你的问题（基于你的示例代码）？

train_target = torch.tensor(train['Target'].values.astype(np.float32))
train = torch.tensor(train.drop('Target', axis = 1).values.astype(np.float32)) 
train_tensor = data_utils.TensorDataset(train, train_target) 
train_loader = data_utils.DataLoader(dataset = train_tensor, batch_size = batch_size, shuffle = True)

Answer 3

您可以将 df.values 属性（一个 numpy 数组）直接传递给 Dataset 构造函数：

import torch.utils.data as data_utils

# Creating np arrays
target = df['Target'].values
features = df.drop('Target', axis=1).values

# Passing to DataLoader
train = data_utils.TensorDataset(features, target)
train_loader = data_utils.DataLoader(train, batch_size=10, shuffle=True)

^{注意：您的特征 (df) 还包含目标变量 (df['Target'])，即您的网络正在“作弊”，因为它可以看到输入中的目标.您需要从功能集中删除此列。}

Answer 4

只需转换pandas dataframe -> numpy array -> pytorch tensor。下面是一个示例：

import pandas as pd
import numpy as np
import torch

df = pd.read_csv('train.csv')
target = pd.DataFrame(df['target'])
del df['target']
train = data_utils.TensorDataset(torch.Tensor(np.array(df)), torch.Tensor(np.array(target)))
train_loader = data_utils.DataLoader(train, batch_size = 10, shuffle = True)

希望这将帮助您使用pytorch（与pytorch的最新版本兼容）创建自己的数据集。

Answer 5

#This works for me

target = torch.tensor(df['Targets'].values)
features = torch.tensor(df.drop('Targets', axis = 1).values)

train = data_utils.TensorDataset(features, target)
train_loader = data_utils.DataLoader(train, batch_size=10, shuffle=True)

将Pandas数据帧转换为PyTorch张量？

5 个答案: