1
我對PyTorch非常陌生，對於神經網絡來說相當新穎。
我試圖建立一個可以猜測性別名稱的神經網絡，並且我基於判斷國籍的PyTorch RNN教程。
我得到的代碼運行沒有錯誤，但損失幾乎沒有變化，使我認爲權重沒有更新...
這是我的輸入/輸出/目標張量設置的問題？或者我的訓練功能可能有問題？我很失落，任何幫助將不勝感激：cold_sweat：
這裏是我的代碼：RNN參數不更新？
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import unicodedata
import string
import torch
import torchvision
import torch.nn as nn
import torch.optim as optim
import random
from torch.autograd import Variable
"""------GLOBAL VARIABLES------"""
all_letters = string.ascii_letters + " .,;'"
num_letters = len(all_letters)
all_names = {}
genders = ["Female", "Male"]
"""-------DATA EXTRACTION------"""
def findFiles(path):
return glob.glob(path)
def unicodeToAscii(s):
return ''.join(
c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn'
and c in all_letters
)
# Read a file and split into lines
def readLines(filename):
lines = open(filename, encoding='utf-8').read().strip().split('\n')
return [unicodeToAscii(line) for line in lines]
for file in findFiles("/home/andrew/PyCharm/PycharmProjects/CantStop/data/names/*.txt"):
gender = file.split("/")[-1].split(".")[0]
names = readLines(file)
all_names[gender] = names
"""-----DATA INTERPRETATION-----"""
def nameToTensor(name):
tensor = torch.zeros(len(name), 1, num_letters)
for index, letter in enumerate(name):
tensor[index][0][all_letters.find(letter)] = 1
return tensor
def outputToGender(output):
gender, gender_index = output.data.topk(1)
if gender_index[0][0] == 0:
return "Female"
return "Male"
"""------NETWORK SETUP------"""
class Net(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(Net, self).__init__()
self.hidden_size = hidden_size
#Layer 1
self.Lin1 = nn.Linear(input_size+hidden_size, int((input_size+hidden_size)/2))
self.ReLu1 = nn.ReLU()
self.Batch1 = nn.BatchNorm1d(int((input_size+hidden_size)/2))
#Layer 2
self.Lin2 = nn.Linear(int((input_size+hidden_size)/2), output_size)
self.ReLu2 = nn.ReLU()
self.Batch2 = nn.BatchNorm1d(output_size)
self.softMax = nn.LogSoftmax()
#Hidden layer
self.HidLin = nn.Linear(input_size+hidden_size, hidden_size)
self.HidReLu = nn.ReLU()
self.HidBatch = nn.BatchNorm1d(hidden_size)
def forward(self, input, hidden):
comb = torch.cat((input, hidden), 1)
hidden = self.HidBatch(self.HidReLu(self.HidLin(comb)))
output1 = self.Batch1(self.ReLu1(self.Lin1(comb)))
output2 = self.softMax(self.Batch2(self.ReLu2(self.Lin2(output1))))
return output2, hidden
def initHidden(self):
return Variable(torch.zeros(1, self.hidden_size))
NN = Net(num_letters, 128, 2)
"""------TRAINING------"""
def getRandomTrainingEx():
gender = genders[random.randint(0, 1)]
name = all_names[gender][random.randint(0, len(all_names[gender])-1)]
gender_tensor = Variable(torch.LongTensor([genders.index(gender)]))
name_tensor = Variable(nameToTensor(name))
return gender_tensor, name_tensor, gender
def train(input, target):
hidden = NN.initHidden()
loss_func = nn.NLLLoss()
alpha = 0.01
NN.zero_grad()
for i in range(input.size()[0]):
output, hidden = NN(input[i], hidden)
loss = loss_func(output, target)
loss.backward()
for w in NN.parameters():
w.data.add_(-alpha, w.grad.data)
return output, loss
for i in range(5000):
gender_tensor, name_tensor, gender = getRandomTrainingEx()
output, loss = train(name_tensor, gender_tensor)
if i%500 == 0:
print("Guess: %s, Correct: %s, Loss: %s" % (outputToGender(output), gender, loss.data[0]))
而這裏的輸出：
Guess: Male, Correct: Male, Loss: 0.6931471824645996
Guess: Male, Correct: Female, Loss: 0.7400936484336853
Guess: Male, Correct: Male, Loss: 0.6755779385566711
Guess: Female, Correct: Female, Loss: 0.6648257374763489
Guess: Male, Correct: Male, Loss: 0.6765623688697815
Guess: Female, Correct: Male, Loss: 0.7330614924430847
Guess: Female, Correct: Female, Loss: 0.6565149426460266
Guess: Male, Correct: Female, Loss: 0.6946508884429932
Guess: Female, Correct: Female, Loss: 0.6621525287628174
Guess: Male, Correct: Male, Loss: 0.6662092804908752
Process finished with exit code 0
你在訓練時會得到什麼損失和準確性？ – DarkCygnus
另外，您好，歡迎來到Stack Overflow，請花點時間瀏覽[歡迎導覽]（https://stackoverflow.com/tour），在此處瞭解您的方式（並獲取您的第一張徽章），閱讀如何創建[Minimal，Complete和Verifiable示例]（https://stackoverflow.com/help/mcve）並檢查[如何提出好問題]（https://stackoverflow.com/help/how-to - 問），所以你增加了獲得反饋和有用答案的機會。 – DarkCygnus