问题描述
使用PyTorch，我在下面有一个ANN模型（用于分类任务）：
import torch
import torch.nn as nn
# Setting up artifical neural net model which separates out categorical
# from continuous features,so that embedding Could be applied to
# categorical features
class TabularModel(nn.Module):
# Initialize parameters embeds,emb_drop,bn_cont and layers
def __init__(self,emb_szs,n_cont,out_sz,layers,p=0.5):
super().__init__()
self.embeds = nn.ModuleList([nn.Embedding(ni,nf) for ni,nf in emb_szs])
self.emb_drop = nn.Dropout(p)
self.bn_cont = nn.Batchnorm1d(n_cont)
# Create empty list for each layer in the neural net
layerlist = []
# Number of all embedded columns for categorical features
n_emb = sum((nf for ni,nf in emb_szs))
# Number of inputs for each layer
n_in = n_emb + n_cont
for i in layers:
# Set the linear function for the weights and biases,wX + b
layerlist.append(nn.Linear(n_in,i))
# Using ReLu activation function
layerlist.append(nn.ReLU(inplace=True))
# normalised all the activation function output values
layerlist.append(nn.Batchnorm1d(i))
# Set some of the normalised activation function output values to zero
layerlist.append(nn.Dropout(p))
# Reassign number of inputs for the next layer
n_in = i
# Append last layer
layerlist.append(nn.Linear(layers[-1],out_sz))
# Create sequential layers
self.layers = nn.Sequential(*layerlist)
# Function for Feedforward
def forward(self,x_cat_cont):
x_cat = x_cat_cont[:,0:cat_train.shape[1]].type(torch.int64)
x_cont = x_cat_cont[:,cat_train.shape[1]:].type(torch.float32)
# Create empty list for embedded categorical features
embeddings = []
# Embed categorical features
for i,e in enumerate(self.embeds):
embeddings.append(e(x_cat[:,i]))
# Concatenate embedded categorical features
x = torch.cat(embeddings,1)
# Apply dropout rates to categorical features
x = self.emb_drop(x)
# Batch normalize continuous features
x_cont = self.bn_cont(x_cont)
# Concatenate categorical and continuous features
x = torch.cat([x,x_cont],1)
# Feed categorical and continuous features into neural net layers
x = self.layers(x)
return x
我正在尝试将此模型与skorch的gridsearchcv一起使用，如下所示：
from skorch import NeuralNetBinaryClassifier
# Random seed chosen to ensure results are reproducible by using the same
# initial random weights and biases,and applying dropout rates to the same
# random embedded categorical features and neurons in the hidden layers
torch.manual_seed(0)
net = NeuralNetBinaryClassifier(module=TabularModel,module__emb_szs=emb_szs,module__n_cont=con_train.shape[1],module__out_sz=2,module__layers=[30],module__p=0.0,criterion=nn.CrossEntropyLoss,criterion__weight=cls_wgt,optimizer=torch.optim.Adam,optimizer__lr=0.001,max_epochs=150,device='cuda'
)
from sklearn.model_selection import gridsearchcv
param_grid = {'module__layers': [[30],[50,20]],'module__p': [0.0,0.2,0.4],'max_epochs': [150,175,200,225]
}
models = gridsearchcv(net,param_grid,scoring='roc_auc').fit(cat_con_train.cpu(),y_train.cpu())
models.best_params_
/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit Failed. The score on this train-test partition for these parameters will be set to nan. Details:
ValueError: Expected module output to have shape (n,) or (n,1),got (128,2) instead
FitFailedWarning)
/usr/local/lib/python3.6/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit Failed. The score on this train-test partition for these parameters will be set to nan. Details:
ValueError: Expected module output to have shape (n,2) instead
FitFailedWarning)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-86-c408d65e2435> in <module>()
98
---> 99 models = gridsearchcv(net,y_train.cpu())
100
101 models.best_params_
11 frames
/usr/local/lib/python3.6/dist-packages/skorch/classifier.py in infer(self,x,**fit_params)
303 raise ValueError(
304 "Expected module output to have shape (n,) or "
--> 305 "(n,got {} instead".format(tuple(y_infer.shape)))
306
307 y_infer = y_infer.reshape(-1)
ValueError: Expected module output to have shape (n,2) instead
我不确定出什么问题或如何解决。在这方面的任何帮助将不胜感激。
非常感谢！
解决方法
引用已经概述了解决方案的ptrblck on the pytorch forum：
我猜NeuralNetBinaryClassifier期望输出具有一个logit，因为它用于二进制用例。 如果要对二进制分类使用两个输出单位（这将是具有2个类的多分类），那么我猜必须使用另一个包装器。 我对skorch并不很熟悉，但认为NeuralNetClassifier可能有用。
他的评估是正确的。 skorch的{{1}}期望NeuralNetBinaryClassifier具有一维，因此形状为y或(x,1)，其中(x,)的值为0或1。有效的y为：
y