关于Pytorch中的张量尺寸和批量大小感到困惑 [英] Confused about tensor dimensions and batch sizes in pytorch
问题描述
因此，我通常对PyTorch和神经网络很陌生，在创建按性别对姓名进行分类的神经网络时遇到了一些问题.
我基于Ry的PyTorch教程，该教程按国籍对姓名进行分类，但是我决定不采用重复使用的方法... 如果这是错误的想法，请在这里停下来！
但是，每当我尝试通过网络运行输入时，都会告诉我:
So I'm very new to PyTorch and Neural Networks in general, and I'm having some problems creating a Neural Network that classifies names by gender.
I based this off of the PyTorch tutorial for RNNs that classify names by nationality, but I decided not to go with a recurrent approach... Stop me right here if this was the wrong idea!
However, whenever I try to run an input through the network it tells me:
RuntimeError: matrices expected, got 3D, 2D tensors at /py/conda-bld/pytorch_1493681908901/work/torch/lib/TH/generic/THTensorMath.c:1232
我知道这与PyTorch总是希望有一个批处理大小之类的东西有关，并且我以这种方式设置了张量，但是到现在为止，您可能可以告诉我我不知道我要做什么.我在说. 这是我的代码:
I know this has something to do with how PyTorch always expects there to be a batch size or something, and I have my tensor set up that way, but you can probably tell by this point that I have no idea what I'm talking about. Here's my code:
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
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
"""------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, output_size):
super(Net, self).__init__()
#Layer 1
self.Lin1 = nn.Linear(input_size, int(input_size/2))
self.ReLu1 = nn.ReLU()
self.Batch1 = nn.BatchNorm1d(int(input_size/2))
#Layer 2
self.Lin2 = nn.Linear(int(input_size/2), output_size)
self.ReLu2 = nn.ReLU()
self.Batch2 = nn.BatchNorm1d(output_size)
self.softMax = nn.LogSoftmax()
def forward(self, input):
output1 = self.Batch1(self.ReLu1(self.Lin1(input)))
output2 = self.softMax(self.Batch2(self.ReLu2(self.Lin2(output1))))
return output2
NN = Net(num_letters, 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):
loss_func = nn.NLLLoss()
optimizer = optim.SGD(NN.parameters(), lr=0.0001, momentum=0.9)
optimizer.zero_grad()
output = NN(input)
loss = loss_func(output, target)
loss.backward()
optimizer.step()
return output, loss
all_losses = []
current_loss = 0
for i in range(100000):
gender_tensor, name_tensor, gender = getRandomTrainingEx()
output, loss = train(name_tensor, gender_tensor)
current_loss += loss
if i%1000 == 0:
print("Guess: %s, Correct: %s, Loss: %s" % (outputToGender(output), gender, loss.data[0]))
if i%100 == 0:
all_losses.append(current_loss/10)
current_loss = 0
# plt.figure()
# plt.plot(all_losses)
# plt.show()
请帮助新手！
推荐答案
- 调试您的错误:
- Debugging your bug out:
Pycharm 是一个有用的python调试器，可让您设置断点并查看张量的尺寸.
为了便于调试，请不要像这样堆叠东西
Pycharm is a helpful python debugger that let you set breakpoint and views dimension of your tensor.
For easier debug, do not stack forward thing up like that
output1 = self.Batch1(self.ReLu1(self.Lin1(input)))
相反，
h1 = self.ReLu1(self.Lin1(input))
h2 = self.Batch1(h1)
对于stacktrace，Pytorch还提供Pythonic错误stacktrack.我相信
For the stacktrace, Pytorch also provide Pythonic error stacktrack. I believe that before
RuntimeError: matrices expected, got 3D, 2D tensors at /py/conda-bld/pytorch_1493681908901/work/torch/lib/TH/generic/THTensorMath.c:1232
有一些python错误stacktrace指向您的代码.如前所述，为便于调试，请不要向前堆叠.
There are some python error stacktrace that point right into your code. For easier debug, as I said, don't stack forward.
您使用Pycharm在崩溃点之前创建.在调试器监视程序中，然后使用Variable(torch.rand(dim1, dim2))测试正向输入，输出尺寸以及尺寸是否不正确.与输入维度进行比较.在调试器监视程序中调用input.size().
You use Pycharm to create break point before crash point. In debugger watcher Then use Variable(torch.rand(dim1, dim2)) to test out forward pass input, output dimension, and if a dimension is incorrect. Comparing with dimension of input. Call input.size() in debugger watcher.
例如，self.ReLu1(self.Lin1(Variable(torch.rand(10, 20)))).size().如果显示读取的文本(错误)，则输入尺寸不正确.否则，它显示输出的大小.
For example, self.ReLu1(self.Lin1(Variable(torch.rand(10, 20)))).size() . If it show read text (error), then the input dimension is incorrect. Else, it show the size of the output.
- 阅读文档
在 Pytorch文档中，指定输入/输出尺寸.它还有一个示例代码片段
In Pytorch Docs, it specify input/output dimension. It also have a example code snip
>>> rnn = nn.RNN(10, 20, 2)
>>> input = Variable(torch.randn(5, 3, 10))
>>> h0 = Variable(torch.randn(2, 3, 20))
>>> output, hn = rnn(input, h0)
您可以使用PyCharm Debugger中的代码片段来浏览您感兴趣的特定层(RNN，Linear，BatchNorm1d)的输入，输出维度.
You may use the code snip in PyCharm Debugger to explore dimension of input, output of specific layer of your interest (RNN, Linear, BatchNorm1d).
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