我想基于Skorch的Dataset类创建自己的数据集类，因为我想区分分类列和连续列。这些分类列将通过模型中的嵌入层传递。结果很奇怪，因为它显示NAN 像这样：
epoch train_loss valid_loss dur
------- ------------ ------------ ------
1 nan nan 0.2187
2 nan nan 0.1719
3 nan nan 0.1719
4 nan nan 0.1562
5 nan nan 0.1406
您能帮我解决它吗？我正在使用此kaggle中的数据： Here
from skorch import NeuralNetRegressor
from skorch.dataset import Dataset
import torch
import torch.nn as nn
import torch.nn.functional as F
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder
class TabularDataset(Dataset):
def __init__(self, data, cat_cols=None, output_col=None):
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[self.cat_cols].astype(np.int64).values
else:
self.cat_X = np.zeros((self.n, 1))
def __len__(self):
# Denotes the total number of sampoes
return self.n
def __getitem__(self, idx):
# generates one sample of data
return [self.cont_X[idx], self.cat_X[idx]], self.y[idx]
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, X):
cont_data = X[0]
cat_data = X[1]
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
# Read data
data = pd.read_csv("data/train.csv", usecols=["SalePrice", "MSSubClass", "MSZoning", "LotFrontage", "LotArea",
"Street", "YearBuilt", "LotShape", "1stFlrSF", "2ndFlrSF"]).dropna()
categorical_features = ["MSSubClass", "MSZoning", "Street", "LotShape", "YearBuilt"]
output_feature = "SalePrice"
# Label Encode Categorial Features
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])
# feed Forward NN
cat_dims = [int(data[col].nunique()) for col in categorical_features]
emb_dims = [(x, min(50, (x + 1) // 2)) for x in cat_dims]
net = FeedForwardNN(emb_dims, no_of_cont=4, lin_layer_sizes=[50, 100],
output_size=1, emb_dropout=0.04,
lin_layer_dropouts=[0.001, 0.01])
# Fit
ds = TabularDataset(data=data, cat_cols=categorical_features,
output_col=output_feature)
X = data.drop(['SalePrice'], axis=1)
y = data['SalePrice'].values.reshape(-1, 1)
net = NeuralNetRegressor(
net,
max_epochs=5,
lr=0.1,
dataset=ds
)
net.fit(X, y)
答案 0 :(得分：0)
问题不在于skorch，而是您的数据。您必须调整您的输入，在这种情况下，尤其是目标，以避免巨大的损失和爆炸的梯度。首先，我建议使用sklearn.preprocessing.StandardScaler：
X请注意，当您拥有提供实际数据的数据集时，不需要y和net.fit，只需将其传递给net = NeuralNetRegressor(
net,
max_epochs=5,
lr=0.00001,
)
net.fit(ds, y=None)
：
Task 1
get the data from the System X
the Java DSL split
post the data to the System Y
post the reply data to the X
the Java DSL aggregate
Task 2
get the data from the System X
the Java DSL split
post the data to the System Y
post the reply data to the X
the Java DSL aggregate
...