在Spyder中运行Pytorch神经网络时出现属性错误

from torch.utils.data import Dataset, DataLoader class TabularDataset(Dataset): def __init__(self, data, cat_cols=None, output_col=None): """ Characterizes a Dataset for PyTorch Parameters ---------- data: pandas data frame The data frame object for the input data. It must contain all the continuous, categorical and the output columns to be used. cat_cols: List of strings The names of the categorical columns in the data. These columns will be passed through the embedding layers in the model. These columns must be label encoded beforehand. output_col: string The name of the output variable column in the data provided. """ self.n = data.shape[0] if output_col: self.y = data[output_col].astype(np.float32).values.reshape(-1, 1) else: self.y = np.zeros((self.n, 1)) self.cat_cols = cat_cols if cat_cols else [] self.cont_cols = [col for col in data.columns if col not in self.cat_cols + [output_col]] if self.cont_cols: self.cont_X = data[self.cont_cols].astype(np.float32).values else: self.cont_X = np.zeros((self.n, 1)) if self.cat_cols: self.cat_X = data[cat_cols].astype(np.int64).values else: self.cat_X = np.zeros((self.n, 1)) def __len__(self): """ Denotes the total number of samples. """ return self.n def __getitem__(self, idx): """ Generates one sample of data. """ return [self.y[idx], self.cont_X[idx], self.cat_X[idx]] import torch import torch.nn as nn import torch.nn.functional as F class FeedForwardNN(nn.Module): def __init__(self, emb_dims, no_of_cont, lin_layer_sizes, output_size, emb_dropout, lin_layer_dropouts): """ Parameters ---------- emb_dims: List of two element tuples This list will contain a two element tuple for each categorical feature. The first element of a tuple will denote the number of unique values of the categorical feature. The second element will denote the embedding dimension to be used for that feature. no_of_cont: Integer The number of continuous features in the data. lin_layer_sizes: List of integers. The size of each linear layer. The length will be equal to the total number of linear layers in the network. output_size: Integer The size of the final output. emb_dropout: Float The dropout to be used after the embedding layers. lin_layer_dropouts: List of floats The dropouts to be used after each linear layer. """ super().__init__() # Embedding layers self.emb_layers = nn.ModuleList([nn.Embedding(x, y) for x, y in emb_dims]) no_of_embs = sum([y for x, y in emb_dims]) self.no_of_embs = no_of_embs self.no_of_cont = no_of_cont # Linear Layers first_lin_layer = nn.Linear(self.no_of_embs + self.no_of_cont, lin_layer_sizes[0]) self.lin_layers = nn.ModuleList([first_lin_layer] + [nn.Linear(lin_layer_sizes[i], lin_layer_sizes[i + 1]) for i in range(len(lin_layer_sizes) - 1)]) for lin_layer in self.lin_layers: nn.init.kaiming_normal_(lin_layer.weight.data) # Output Layer self.output_layer = nn.Linear(lin_layer_sizes[-1], output_size) nn.init.kaiming_normal_(self.output_layer.weight.data) # Batch Norm Layers self.first_bn_layer = nn.BatchNorm1d(self.no_of_cont) self.bn_layers = nn.ModuleList([nn.BatchNorm1d(size) for size in lin_layer_sizes]) # Dropout Layers self.emb_dropout_layer = nn.Dropout(emb_dropout) self.droput_layers = nn.ModuleList([nn.Dropout(size) for size in lin_layer_dropouts]) def forward(self, cont_data, cat_data): if self.no_of_embs != 0: x = [emb_layer(cat_data[:, i]) for i,emb_layer in enumerate(self.emb_layers)] x = torch.cat(x, 1) x = self.emb_dropout_layer(x) if self.no_of_cont != 0: normalized_cont_data = self.first_bn_layer(cont_data) if self.no_of_embs != 0: x = torch.cat([x, normalized_cont_data], 1) else: x = normalized_cont_data for lin_layer, dropout_layer, bn_layer in\ zip(self.lin_layers, self.droput_layers, self.bn_layers): x = F.relu(lin_layer(x)) x = bn_layer(x) x = dropout_layer(x) x = self.output_layer(x) return x categorical_features = ["cat1", "cat2", "cat3"] output_feature = ["output"] data = data[output_feature + categorical_features + ["cont1", "cont2"]].copy().dropna() from sklearn.preprocessing import LabelEncoder label_encoders = {} for cat_col in categorical_features: label_encoders[cat_col] = LabelEncoder() data[cat_col] = label_encoders[cat_col].fit_transform(data[cat_col]) dataset = TabularDataset(data=data, cat_cols=categorical_features,output_col=output_feature) batchsize = 256 dataloader = DataLoader(dataset, batchsize, shuffle=True, num_workers=1) cat_dims = [int(data[col].nunique()) for col in categorical_features] emb_dims = [(x, min(50, (x + 1) // 2)) for x in cat_dims] device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = FeedForwardNN(emb_dims, no_of_cont=2, lin_layer_sizes=[50, 100], output_size=1, emb_dropout=0.04, lin_layer_dropouts=[0.001,0.01]).to(device) import tqdm no_of_epochs = 5 criterion = nn.MSELoss() optimizer = torch.optim.Adam(model.parameters(), lr=0.1) for epoch in tqdm.tqdm(range(no_of_epochs)): for y, cont_x, cat_x in dataloader: cat_x = cat_x.to(device) cont_x = cont_x.to(device) y = y.to(device) # Forward Pass preds = model(cont_x, cat_x) loss = criterion(preds, y) # Backward Pass and Optimization optimizer.zero_grad() loss.backward() optimizer.step()

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1楼 · 发布于 2024-09-26 22:55:04

您可以尝试使用控制台名称空间而不是空名称空间来运行代码（尝试保留TabularDataset定义）。为此，您需要选中首选项对话框中的选项Run in Console's namespace instead of an empty one：菜单Tools > Preferences（或🔧 按钮显示对话框）和Run > General settings > Run in Console's namespace instead of an empty one

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