PyTorch搭建循环神经网络（RNN）进行文本分类、预测及损失分析（对不同国家的语言单词和姓氏进行分类，附源码和数据集）

需要源码和数据集请点赞关注收藏后评论区留言~~~

下面我们将使用循环神经网络训练来自18种起源于不同语言的数千种姓氏，并根据拼写方式预测名称的来源。

一、数据准备和预处理

总共有18个txt文件，并且对它们进行预处理，输出如下

部分预处理代码如下

from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
 
def findFiles(path): return glob.glob(path)
 
print(findFiles('data/names/*.txt'))
 
import unicodedata
import string
 
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
 
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
        and c in all_letters
    )
 
 
for filename in findFiles('data/names/*.txt'):
    category = os.path.splitext(os.path.basename(filename))[0]
    all_categories.append(category)
    lines = readLines(filename)
    category_lines[category] = lines
 
n_categories = len(all_categories)

 二、将名字转换为张量

现在已经整理好了所有数据集种的名字，这里需要将它们转换为张量以使用它们，为了表示单个字母，这里使用独热编码的方法

三、构建神经网络

在PyTorch种构建循环神经网络涉及在多个时间步长上克隆多个RNN层 的参数，RNN层保留了Hidden State和梯度，这些状态完全由PyTorch的计算图来自动完成维护，这意味我们只需要关心前馈网络而不需要关注反向传播

 四、训练RNN网络

训练该网络所需要做的是向他输入大量的数据，令其进行预测，然后告诉它是否有错误

每个训练的循环包含下面七个步骤

1：创建输入和目标Tensor

2：创建归零的初始Hidden State

3：输入一个字母

4：传递Hidden State给下一个字母输入

5：比较最终输出和目标

6：反向传播

7：返回输出和损失

平均损失如下

 

 五、绘制损失变化图像

绘制网络的历史损失变化，以显示网络学习情况 

可见随着训练次数的增加损失逐渐 梯度下降

 六、预测结果

为了了解网络在不同类别上的表现如何，这里将创建一个混淆矩阵，为每种实际语言指示网络猜测那种语言，结果如下图，可以从主轴上挑出一些亮点，以显示它猜错了哪些语言

可见中文/朝鲜语 西班牙语/意大利语会有混淆，网络预测希腊语名字十分准确，但是英语名字预测的很糟糕

 七、预测用户输入

大家可以输入任何希望预测的名字到模型中，网络会给出几个名字最有可能的语言类型

 八、代码

需要全部源码请点赞关注收藏后评论区留言~~~

from __future__ import unicode_literals, print_function, division
 
 
from io import open
import glob
import os
 
def findFiles(path): return glob.glob(path)
 
print(findFiles('data/names/*.txt'))
 
import unicodedata
import string
 
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
 
# Turn a Unicode string to plain ASCII, thanks to https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
    return ''.join(
        c for c in unicodedata.normalize('NFD', s)
        if unicodedata.category(c) != 'Mn'
        and c in all_letters
    )
 
print(unicodeToAscii('Ślusàrski'))
 
# Build the category_lines dictionary, a list of names per language
category_lines = {}
all_categories = []
 
# 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 filename in findFiles('data/names/*.txt'):
    category = os.path.splitext(os.path.basename(filename))[0]
    all_categories.append(category)
    lines = readLines(filename)
    category_lines[category] = lines
 
n_categories = len(all_categories)
 
 
 
# 
# 
 
# In[33]:
 
 
#print(category_lines['Italian'][:5])
 
 
# Turning Names into Tensors
 
# 
 
# In[34]:
 
 
import torch
 
# Find letter index from all_letters, e.g. "a" = 0
def letterToIndex(letter):
    return all_letters.find(letter)
 
# Just for demonstration, turn a letter into a <1 x n_letters> Tensor
def letterToTensor(letter):
    tensor = torch.zeros(1, n_letters)
    tensor[0][letterToIndex(letter)] = 1
    return tensor
 
# Turn a line into a <line_length x 1 x n_letters>,
# or an array of one-hot letter vectors
def lineToTensor(line):
    tensor = torch.zeros(len(line), 1, n_letters)
    for li, letter in enumerate(line):
        tensor[li][0][letterToIndex(letter)] = 1
    return tensor
 
print(letterToTensor('J'))
 
print(lineToTensor('Jones').size())
 
 
 
# This RNN module (mostly copied from `the PyTorch for Torch users
# tutorial <https://pytorch.org/tutorials/beginner/former_torchies/
# nn_tutorial.html#example-2-recurrent-net>`__)
# is just 2 linear layers which operate on an input and hidden state, with
# a LogSoftmax layer after the output.
# 
# .. figure:: https://i.imgur.com/Z2xbySO.png
#    :alt:
# 
# 
# 
# 
 
# In[35]:
 
 
i
        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)
 
    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden
 
    def initHidden(self):
        return torch.zeros(1, self.hidden_size)
 
n_hidden = 128
rnn = RNN(n_letters, n_hidden, n_categories)
 
 
# To run a step of this network we need to pass an input (in our case, the
# Tensor for the current letter) and a previous hidden state (which we
# initialize as zeros at first). We'll get back the output (probability of
# each language) and a next hidden state (which we keep for the next
# step).
# 
# 
# 
# In[36]:
inp
# For the sake of efficiency we don't want to be creating a new Tensor for
# every step, so we will use ``lineToTensor`` instead of
# ``letterToTensor`` and use slices. This could be further optimized by
# pre-computing batches of Tensors.
# 
# 
# 
 
# In[37]:
 
 
input = lineToTensor('Albert')
hidden = torch.zeros(1, n_hidden)
 
output, next_hidden = rnn(input[0], hidden)
print(output)
 
 
# As you can see the output is a ``<1 x n_categories>`` Tensor, where
# every item is the likelihood of that category (higher is more likely).
# 
# 
# 
 
# Training
# ========
# Preparing for Training
# ----------------------
# 
# Before going into training we should make a few helper functions. The
# first is to interpret the output of the network, which we know to be a
# likelihood of each category. We can use ``Tensor.topk`` to get the index
# of the greatest value:
# 
# 
# 
 
# In[38]:
 
 
def categoryFromOutput(output):
    top_n, top_i = output.topk(1)
    category_i = top_i[0].item()
    return all_categories[category_i], category_i
 
#print(categoryFromOutput(output))
 
 
# We will also want a quick way to get a training example (a name and its
# language):
# 
# 
# 
 
# In[39]:
 
 
import random
 
def randomChoice(l):
    return l[random.randint(0, len(l) - 1)]
 
def randomTrainingExample():
    category = randomChoice(all_categories)
    line = randomChoice(category_lines[category])
    category_tensor = torch.tensor([all_categories.index(category)], dtype=torch.long)
    line_tensor = lineToTensor(line)
    return category, line, category_tensor, line_tensor
 
for i in range(10):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    print('category =', category, '/ line =', line)
 
 
# Training the Network
# --------------------
# 
# Now all it takes to train this network is show it a bunch of examples,
# have it make guesses, and tell it if it's wrong.
# 
# For the loss function ``nn.NLLLoss`` is appropriate, since the last
# layer of the RNN is ``nn.LogSoftmax``.
# 
# 
# 
# In[40]:
criterion = nn.NLLLoss()
# 
#    -  Keep hidden state for next letter
# 
# -  Compare final output to target
# -  Back-propagate
# -  Return the output and loss
# 
# 
# 
# In[41]:
learning_rate = 0.005 # If you set this too high, it might explode. If too low, it might not learn
def train(category_tensor, line_tensor):
    hidden = rnn.initHidden()
    rnn.zero_grad()
    for i in range(line_tensor.size()[0]):
        output, hidden = rnn(line_tensor[i], hidden)
    loss = criterion(output, category_tensor)
    loss.backward()
    # Add parameters' gradients to their values, multiplied by learning rate
    for p in rnn.parameters():
        p.data.add_(p.grad.data, alpha=-learning_rate)
 
    return output, loss.item()
 
 
# Now we just have to run that with a bunch of examples. Since the
# ``train`` function returns both the output and loss we can print its
# guesses and also keep track of loss for plotting. Since there are 1000s
# of examples we print only every ``print_every`` examples, and take an
# average of the loss.
# 
# 
# 
 
    m = math.floor(s / 60)
    s -= m * 60
    return '%dm %ds' % (m, s)
 
start = time.time()
 
for iter in range(1, n_iters + 1):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output, loss = train(category_tensor, line_tensor)
    current_loss += loss
 
    # Print iter number, loss, name and guess
    if iter % print_every == 0:
        guess, guess_i = categoryFromOutput(output)
        correct = '✓' if guess == category else '✗ (%s)' % category
        print('%d %d%% (%s) %.4f %s / %s %s' % (iter, iter / n_iters * 100, timeSince(start), loss, line, guess, correct))
 
    # Add current loss avg to list of losses
    if iter % plot_every == 0:
        all_losses.append(current_loss / plot_every)
        current_loss = 0
 
 
# Plotting the Results
# --------------------
# 
# Plotting the historical loss from ``all_losses`` shows the network
# learning:
# 
# 
# 
 
# In[22]:
 
 
plt.figure()
plt.plot(all_losses)
 
 
# Evaluating the Results
# ======================
# 
# To see how well the network performs on different categories, we will
# create a confusion matrix, indicating for every actual language (rows)
# which language the network guesses (columns). To calculate the confusion
# matrix a bunch of samples are run through the network with
# ``evaluate()``, which is the same as ``train()`` minus the backprop.
# 
# 
# 
 
# In[46]:
 
 
# Keep track of correct guesses in a confusion matrix
confusion = torch.zeros(n_categories, n_categories)
n_confusion = 10000
 
# Just return an output given a line
def evaluate(line_tensor):
    hidde
# Go through a bunch of examples and record which are correctly guessed
for i in range(n_confusion):
    category, line, category_tensor, line_tensor = randomTrainingExample()
    output = evaluate(line_tensor)
    guess, guess_i = categoryFromOutput(output)
    category_i = all_categories.index(category)
    confusion[category_i][guess_i] += 1
 
# Normalize by dividing every row by its sum
for i in range(n_categories):
    confusion[i] = confusion[i] / confusion[i].sum()
 
# Set up plot
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(confusion.numpy())
fig.colorbar(cax)
 
# Set up axes
ax.set_xticklabels([''] + all_categories, rotation=90)
ax.set_yticklabels([''] + all_categories)
 
# Force label at every tick
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
 
 
# You can pick out bright spots off the main axis that show which
# languages it guesses incorrectly, e.g. Chinese for Korean, and Spanish
# for Italian. It seems to do very well with Greek, and very poorly with
# English (perhaps because of overlap with other languages).
# 
# 
# 
 
# Running on User Input
# ---------------------
# 
# 
# 
 
# In[47]:
 
 
def predict(input_line, n_predictions=3):
    print('\n> %s' % input_line)
    with t evaluate(lineToTensor(input_line))
 = []
 
        for i in range(n_predictions):
            value = topv[0][i].item()
            category_index = topi[0][i].item()
            print('(%.2f) %s' % (value, all_categories[category_index]))
            predictions.append([value, all_categories[category_index]])
 
predict('Dovesky')
predict('Jackson')
predict('Satoshi')
 
 
# The final versions of the scripts `in the Practical PyTorch
# repo <https://github.com/spro/practical-pytorch/tree/master/char-rnn-classification>`__
# split the above code into a few files:
# 
# -  ``data.py`` (loads files)
# -  ``model.py`` (defines the RNN)
# -  ``train.py`` (runs training)
# -  ``predict.py`` (runs ``predict()`` with command line arguments)
# -  ``server.py`` (serve prediction as a JSON API with bottle.py)
# 
# Run ``train.py`` to train and save the network.
# 
# Run ``predict.py`` with a name to view predictions:
# 
# ::
# 
#     $ python predict.py Hazaki
#     (-0.42) Japanese
#     (-1.39) Polish
irst name -> gender
#    -  Character name -> writer
#    -  Page title -> blog or subreddit
# 
# -  Get better results with a bigger and/or better shaped network
# 
#    -  Add more linear layers
#    -  Try the ``nn.LSTM`` and ``nn.GRU`` layers
#    -  Combine multiple of these RNNs as a higher level network
# 
# 
# 

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