最近，我用手套来初始化Embedding的权重，发现from_pretrained和weight.data.copy_的区别是：from_pretrained = {weight.data.copy_（）; embedding.weight.requires_grad = False}
我想知道我说的话是否正确？
并且我的手套准确度比没有手套的准确度低10％，但是当我计算未被手套覆盖的单词时，我发现只有10％的单词不在手套中。
import numpy as np
import pandas as pd
import unicodedata, re, string
import nltk
import torch
import torch.nn as nn
from torch.utils.data import TensorDataset, DataLoader
import seaborn as sns
sns.set(color_codes=True)
df_train = pd.read_csv(r'E:\Mice\train.tsv', delimiter='\t') # r代表反转义
df_test = pd.read_csv(r'E:\Mice\test.tsv', delimiter='\t')
def remove_non_ascii(words):
"""Remove non-ASCII characters from list of tokenized words"""
new_words = []
for word in words:
new_word = unicodedata.normalize('NFKD', word).encode('ascii', 'ignore').decode('utf-8', 'ignore')
new_words.append(new_word)
return new_words
def to_lowercase(words):
"""Convert all characters to lowercase from list of tokenized words"""
new_words = []
for word in words:
new_word = word.lower()
new_words.append(new_word)
return new_words
def remove_punctuation(words):
"""Remove punctuation from list of tokenized words"""
new_words = []
for word in words:
new_word = re.sub(r'[^\w\s]', '', word)
if new_word != '':
new_words.append(new_word)
return new_words
def remove_numbers(words):
"""Remove all interger occurrences in list of tokenized words with textual representation"""
new_words = []
for word in words:
new_word = re.sub(r"\d+", "", word)
if new_word != '':
new_words.append(new_word)
return new_words
def normalize(words):
words = remove_non_ascii(words)
words = to_lowercase(words)
words = remove_punctuation(words)
words = remove_numbers(words)
# words = remove_stopwords(words)
return words
df_train['Words'] = df_train['Phrase'].apply(nltk.word_tokenize)
df_train['Words'] = df_train['Words'].apply(normalize)
word_set = set()
for l in df_train['Words']:
for e in l:
word_set.add(e)
# 从这里开始构建词向量，简单的索引。
word_to_int = {word: ii for ii, word in enumerate(word_set, 1)}
df_train['Tokens'] = df_train['Words'].apply(lambda l: [word_to_int[word] for word in l])
max_len = df_train['Tokens'].str.len().max()
all_tokens = np.array([t for t in df_train['Tokens']])
encoded_labels = np.array([l for l in df_train['Sentiment']])
features = np.zeros((len(all_tokens), max_len), dtype=int)
# for each phrase, add zeros at the end
for i, row in enumerate(all_tokens):
features[i, :len(row)] = row
# 到这里词向量构建结束
# 下面是另一种, 用glove预训练。
embeddings_index = {}
f = open('E:/Mice/glove.6B.100d.txt', encoding='utf-8')
for line in f:
values = line.split()
word = values[0]
coefs = np.asarray(values[1:], dtype='float32')
embeddings_index[word] = coefs
f.close()
# 同样属于glove, 构建词矩阵。
num = 0 # 计算下有多少数没有算进去。
tem_word = 0
embedding_matrix = np.zeros((len(word_to_int) + 1, 100)) # 因为预训练的glove就是100
for word, i in word_to_int.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
else:
num = num + 1
if num == 1:
temp_word = i
# 要放在weight 中的。
print(embedding_matrix[tem_word])
print("未统计的数字：", num)
print("glove length", len(embeddings_index))
print("word length", len(word_to_int))
# split_frac = 0.8
# split_idx = int(len(features) * 0.8)
# train_x, remaining_x = features[:split_idx], features[split_idx:]
# train_y, remaining_y = encoded_labels[:split_idx], encoded_labels[split_idx:]
# test_idx = int(len(remaining_x) * 0.5)
# val_x, test_x = remaining_x[:test_idx], remaining_x[test_idx:]
# val_y, test_y = remaining_y[:test_idx], remaining_y[test_idx:]
split_frac = 0.8
split_idx = int(len(features) * 0.8)
train_x, remaining_x = features[:split_idx], features[split_idx:]
train_y, remaining_y = encoded_labels[:split_idx], encoded_labels[split_idx:]
test_idx = int(len(remaining_x) * 0.5)
val_x, test_x = remaining_x[:test_idx], remaining_x[test_idx:]
val_y, test_y = remaining_y[:test_idx], remaining_y[test_idx:]
train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y))
valid_data = TensorDataset(torch.from_numpy(val_x), torch.from_numpy(val_y))
test_data = TensorDataset(torch.from_numpy(test_x), torch.from_numpy(test_y))
batch_size = 54
# make sure the SHUFFLE your training data
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
valid_loader = DataLoader(valid_data, shuffle=True, batch_size=batch_size)
test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size)
train_on_gpu = torch.cuda.is_available()
if train_on_gpu:
print('Training on GPU.')
else:
print('No GPU available, training on CPU.')
class SentimentRNN(nn.Module):
"""
The RNN model that will be used to perform Sentiment analysis.
"""
def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers, drop_prob=0.5):
"""
Initialize the model by setting up the layers.
"""
super(SentimentRNN, self).__init__()
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
# embedding and LSTM layers
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.embedding.weight.requires_grad = False
self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers,
dropout=drop_prob, batch_first=True)
# dropout layer
self.dropout = nn.Dropout(0.3)
# linear
self.fc = nn.Linear(hidden_dim, output_size)
def forward(self, x, hidden):
"""
Perform a forward pass of our model on some input and hidden state.
"""
batch_size = x.size(0) # input.shape = (54, 48)
# embeddings and lstm_out
x = x.long() # 走的坑, 这里新增加了 .long()
embeds = self.embedding(x) # input 应该是： LongTensor of arbitrary shape containing the indices to extract
# embeds.shape = (54, 48, 400)
lstm_out, hidden = self.lstm(embeds, hidden) # lstm_out.shape = (54, 48, 256)
# transform lstm output to input size of linear layers
lstm_out = lstm_out.transpose(0, 1) # 48, 54, 256
lstm_out = lstm_out[-1] # lstm_out.shape = 54, 256
out = self.dropout(lstm_out)
out = self.fc(out)
return out, hidden
def init_hidden(self, batch_size):
""" Initializes hidden state """
# Create two new tensors with sizes n_layers x batch_size x hidden_dim,
# initialized to zero, for hidden state and cell state of LSTM
weight = next(self.parameters()).data # 这里的shape是[16210, 400] 也是embedding的两个参数
if train_on_gpu:
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_().cuda())
# new 和 view、reshape的区别于何处， zero_ 还是初始化成了 0
else:
hidden = (weight.new(self.n_layers, batch_size, self.hidden_dim).zero_(),
weight.new(self.n_layers, batch_size, self.hidden_dim).zero_())
return hidden
# Instantiate the model w/ hyperparams
# vocab_size = len(word_to_int) + 1 # +1 for the 0 padding
vocab_size = len(word_set) + 1
output_size = 5
embedding_dim = 100
hidden_dim = 256
n_layers = 2
net = SentimentRNN(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)
net.embedding.weight.data.copy_(torch.from_numpy(embedding_matrix))
lr = 0.003
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=lr)
# training params
epochs = 3 # 3-4 is approx where I noticed the validation loss stop decreasing
counter = 0
print_every = 100
clip = 5 # gradient clipping
# move model to GPU, if available
if train_on_gpu:
net.cuda()
net.train()
# train for some number of epochs
for e in range(epochs):
# initialize hidden state
h = net.init_hidden(batch_size)
# batch loop
for inputs, labels in train_loader:
# inputs.shape = (54, 48) # batch_size 就是54 . 48是 token的长度 # labels.shpe = 54, 每一项都是分类.
counter += 1
if train_on_gpu:
inputs, labels = inputs.cuda(), labels.cuda()
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
h = tuple([each.data for each in h])
# zero accumulated gradients
net.zero_grad()
# get the output from the model
output, h = net(inputs, h) # output.shape = [54, 5]
# calculate the loss and perform backprop
loss = criterion(output.squeeze(), labels.long()) # 走的坑, 这里新增加了 .squeeze() ; .long()
loss.backward()
# `clip_grad_norm` helps prevent the exploding gradient problem in RNNs / LSTMs.
nn.utils.clip_grad_norm_(net.parameters(), clip)
optimizer.step()
# loss stats
if counter % print_every == 0:
# Get validation loss
val_h = net.init_hidden(batch_size)
val_losses = []
net.eval()
for inputs, labels in valid_loader:
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
val_h = tuple([each.data for each in val_h])
if train_on_gpu:
inputs, labels = inputs.cuda(), labels.cuda()
output, val_h = net(inputs, val_h)
val_loss = criterion(output.squeeze(), labels.long())
val_losses.append(val_loss.item())
net.train()
print("Epoch: {}/{}...".format(e + 1, epochs),
"Step: {}...".format(counter),
"Loss: {:.6f}...".format(loss.item()),
"Val Loss: {:.6f}".format(np.mean(val_losses)))
test_losses = [] # track loss
num_correct = 0
# init hidden state
h = net.init_hidden(batch_size)
net.eval()
# iterate over test data
for inputs, labels in test_loader:
# Creating new variables for the hidden state, otherwise
# we'd backprop through the entire training history
h = tuple([each.data for each in h])
if train_on_gpu:
inputs, labels = inputs.cuda(), labels.cuda()
# get predicted outputs
output, h = net(inputs, h)
# calculate loss
test_loss = criterion(output.squeeze(), labels.long())
test_losses.append(test_loss.item())
# convert output probabilities to predicted class
_, pred = torch.max(output, 1)
# compare predictions to true label
correct_tensor = pred.eq(labels.view_as(pred))
correct = np.squeeze(correct_tensor.numpy()) if not train_on_gpu else np.squeeze(correct_tensor.cpu().numpy())
num_correct += np.sum(correct)
# -- stats! -- ##
# avg test loss
print("Test loss: {:.3f}".format(np.mean(test_losses)))
# accuracy over all test data
test_acc = num_correct / len(test_loader.dataset)
print("Test accuracy: {:.3f}".format(test_acc))
print(net.embedding.weight[tem_word])