Week 9: RNN GRU Seq2seq
1. RNN and GRU.
1-1. RNN : Recurrent Neural Network
1-2. GRU : Gated Recurrent Units
1-3. Padding
2. Seq2seq model with attention.
2-1. Encoder
2-2. Decoder with Attention
2-3. Training
2-4. Evaluation
reference
https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html \
https://towardsdatascience.com/sentiment-analysis-using-lstm-step-by-step-50d074f09948
1. Implement RNN and GRU
0) Import required packages
import torch
import torch.nn as nn
import torch.nn.functional as F1) Recurrent Neural Network (RNN)
Before we start, let's implement the simple RNN.
import torch.nn as nn
import torch.nn.functional as F
class RNNCell(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNNCell, self).__init__()
self.hidden_size = hidden_size
self.W = nn.Linear(hidden_size, hidden_size, bias=True)
self.U = nn.Linear(input_size, hidden_size, bias=True)
self.V = nn.Linear(hidden_size, output_size, bias=True)
self.softmax = nn.LogSoftmax(dim=1)
def forward(self, x, h):
h = F.tanh(self.W(h)+self.U(x))
o = self.V(h)
y = self.softmax(o)
return y, h## Single RNN Cell
# input_size = 3
# hidden_size = 4
x = torch.randn(1, 3) # batch_size, feature
h0 = torch.randn(1, 4) # batch_size, hidden_size
rnn1 = RNNCell(input_size = 3, hidden_size = 4, output_size = 4)
output, hidden = rnn1(x, h0)
print('output shape:', output.shape)
print('output:', output.data)
print('hidden shape:', hidden.shape)
print('hidden:', hidden.data)output shape: torch.Size([1, 4])
output: tensor([[-1.9828, -1.4582, -1.2161, -1.0987]])
hidden shape: torch.Size([1, 4])
hidden: tensor([[ 0.9662, 0.2798, 0.8023, -0.5260]])
/usr/local/lib/python3.9/dist-packages/torch/nn/functional.py:1956: UserWarning: nn.functional.tanh is deprecated. Use torch.tanh instead.
warnings.warn("nn.functional.tanh is deprecated. Use torch.tanh instead.")In the following sections, we will learn basic usage of pytorch RNN layer with some example codes and practices.
PARAMETERS
- input_size: the number of expected features in the input x
- hidden_size: the number of features in the hidden state h
- num_layers: number of recurrent layers
- bidirectional: if
True, becomes bidirectional RNN - batch_first: if
True, the input and output tensors are provided as(batch, seq, feature)
INPUT
- input: data of shape (batch_size, seq_len, input_size) if batch_first =
True - h0: tensor containing the initial hidden state of shape (num_layers * num_directions, batch_size, hidden_size)
OUTPUT
- output: tensor containing the output features from the last layer of the RNN, for each t provided as
(batch, seq, hidden_size)if batch_first =True - hidden: tensor containing the hidden state for the last time step, provided as
(batch, num_layers * num_directionals, hidden_size)if batch_first =True
Detailed explanation and default values are available on the official site
(https://pytorch.org/docs/stable/generated/torch.nn.RNN.html)
## RNN with 2 layers
# input_size = 3
# seq_len = 5
# hidden_size = 4
x = torch.randn(1, 5, 3) # batch_size, seq_len, feature
h0 = torch.randn(2, 1, 4) # num_layers * num_directions, batch_size, hidden_size
rnn2 = nn.RNN(input_size=3, hidden_size=4, num_layers=2, bidirectional = False, batch_first=True)
output, hidden = rnn2(x, h0)
print('RNN with 2 layers')
print('output shape:', output.shape)
print('output:', output.data)
print('hidden shape:', hidden.shape)
print('hidden:', hidden.data)RNN with 2 layers
output shape: torch.Size([1, 5, 4])
output: tensor([[[-0.7107, -0.0901, 0.5273, -0.4067],
[-0.5118, 0.2032, 0.7410, 0.5709],
[-0.8290, 0.0247, 0.6061, -0.1309],
[-0.4607, -0.0221, 0.8550, 0.6106],
[-0.6431, 0.0912, 0.7876, 0.4497]]])
hidden shape: torch.Size([2, 1, 4])
hidden: tensor([[[ 0.7471, -0.1726, 0.0188, -0.0329]],
[[-0.6431, 0.0912, 0.7876, 0.4497]]])## Bidirectional RNN
# input_size = 3
# seq_len = 5
# hidden_size = 4
x = torch.randn(1, 5, 3) # batch_size, seq_len, feature
h0 = torch.randn(2, 1, 4) # num_layers * num_directions, batch_size, hidden_size
rnn3 = nn.RNN(input_size=3, hidden_size=4, num_layers=1, bidirectional=True, batch_first=True)
output, hidden = rnn3(x, h0)
print('Bidirectional RNN')
print('output shape:', output.shape)
print('output:', output.data)
print('hidden shape:', hidden.shape)
print('hidden:', hidden.data)Bidirectional RNN
output shape: torch.Size([1, 5, 8])
output: tensor([[[-0.6564, 0.1975, 0.2298, 0.2368, 0.2386, -0.6305, -0.2053,
-0.0575],
[ 0.5862, 0.5628, -0.3403, -0.1892, 0.4985, -0.0467, -0.4683,
-0.4001],
[-0.3996, 0.5191, 0.1306, 0.6954, 0.3795, -0.4883, 0.1459,
0.1618],
[-0.3455, -0.2594, -0.7693, -0.3756, 0.0190, 0.1552, 0.3396,
-0.4070],
[ 0.7284, 0.7610, 0.4701, 0.2799, -0.5761, 0.6649, 0.3688,
-0.7002]]])
hidden shape: torch.Size([2, 1, 4])
hidden: tensor([[[ 0.7284, 0.7610, 0.4701, 0.2799]],
[[ 0.2386, -0.6305, -0.2053, -0.0575]]])###########################################################################################################
###TO-DO: Make your own RNN!
###Here, you can make your own RNN changing the dimensions, number of layers, bidirectional etc.
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, bidirectional):
super().__init__()
self.input_size, self.hidden_size, self.output_size = input_size, hidden_size, output_size
self.num_layers = num_layers
self.num_directions = 2 if bidirectional else 1
self.rnn = nn.RNN(input_size=self.input_size, hidden_size = self.hidden_size, num_layers=self.num_layers, bidirectional=bidirectional, batch_first=True)
self.output_fc = nn.Linear(self.hidden_size * self.num_directions, output_size)
def forward(self, x):
self.batch_size = x.size(0)
h0 = self.init_hidden()
output, hidden = self.rnn(x, h0)
output = self.output_fc(output[:, -1, :])
return output
def init_hidden(self):
return torch.zeros(self.num_layers * self.num_directions, self.batch_size, self.hidden_size)## Run your own RNN!
input_size = 3 ; hidden_size = 4; output_size = 2
num_layers = 1
bidirectional = True
x = torch.randn(1, 4, input_size) # batch_size, seq_len, input_size
rnn = RNN(input_size, hidden_size, output_size, num_layers, bidirectional)
output = rnn(x)
print('output shape:', output.shape)
print('output: ', output.data)output shape: torch.Size([1, 2])
output: tensor([[0.2376, 0.0386]])2) Gated Recurrent Unit (GRU)
In PyTorch, GRU layer is given with the pytorch torch.nn.GRU package. In this section, we will learn basic usage of pytorch GRU layer with some example codes and practices.
PARAMETERS
- input_size: the number of expected features in the input x
- hidden_size: the number of features in the hidden state h
- num_layers: number of recurrent layers
- bidirectional: if
True, becomes bidirectional RNN - batch_first: if
True, then the input and output tensors are provided as(batch, seq, feature)
INPUT
- input: data of shape (batch_size, seq_len, input_size) if batch_first =
True - h0: tensor containing the initial hidden state of shape (num_layers * num_directions, batch_size, hidden_size)
OUTPUT
- output: tensor containing the output features from the last layer of the RNN, for each t provided as
(batch, seq, hidden_size)if batch_first =True - hidden: tensor containing the hidden state for the last time step, provided as
(batch_size, num_layers * num_directionals, hidden_size)if batch_first =True
Detailed explanation and default values are available on the official site
(https://pytorch.org/docs/stable/generated/torch.nn.GRU.html)
# GRU with 1 layer
# input_size = 3
# seq_len = 5
# hidden_size = 4
x = torch.randn(1, 5, 3) # batch_size, seq_len, feature
h0 = torch.randn(1, 1, 4) # num_layers * num_directions, batch_size, hidden_size
gru1 = nn.GRU(input_size = 3, hidden_size = 4, num_layers = 1, bidirectional = False, batch_first = True)
output, hidden = gru1(x, h0)
print('GRU with 1 layer')
print('output shape:', output.shape)
print('output:', output.data)
print('hidden shape:', hidden.shape)
print('hidden:', hidden.data)GRU with 1 layer
output shape: torch.Size([1, 5, 4])
output: tensor([[[ 0.0767, -0.8984, -0.2289, -0.1309],
[ 0.3779, -0.6262, -0.1120, -0.3198],
[-0.2339, -0.6223, 0.0238, 0.1569],
[-0.2661, -0.5978, -0.0531, 0.4618],
[-0.5216, -0.6345, -0.0758, 0.6766]]])
hidden shape: torch.Size([1, 1, 4])
hidden: tensor([[[-0.5216, -0.6345, -0.0758, 0.6766]]])# GRU with 2 layer
# input_size = 3
# seq_len = 5
# hidden_size = 4
x = torch.randn(1, 5, 3) # batch_size, seq_len, feature
h0 = torch.randn(2, 1, 4) # num_layers * num_directions, batch_size, hidden_size
gru2 = nn.GRU(input_size=3, hidden_size=4, num_layers=2, bidirectional = False, batch_first=True)
output, hidden = gru2(x, h0)
print('GRU with 2 layers')
print('output shape:', output.shape)
print('output:', output.data)
print('hidden shape:', hidden.shape)
print('hidden:', hidden.data)
print()GRU with 2 layers
output shape: torch.Size([1, 5, 4])
output: tensor([[[ 0.9352, -0.7128, -0.6386, 0.2451],
[ 0.7871, -0.2688, -0.5574, 0.1923],
[ 0.6583, -0.2632, -0.4675, 0.1633],
[ 0.5941, -0.3615, -0.2825, 0.1540],
[ 0.5390, -0.4207, -0.2395, 0.1434]]])
hidden shape: torch.Size([2, 1, 4])
hidden: tensor([[[ 0.1818, -0.2955, 0.3163, 0.3594]],
[[ 0.5390, -0.4207, -0.2395, 0.1434]]])# bidirectional GRU
# input_size = 3
# seq_len = 5
# hidden_size = 4
x = torch.randn(1, 5, 3) # batch_size, seq_len, feature
h0 = torch.randn(2, 1, 4) # num_layers * num_directions, batch_size, hidden_size
gru3 = nn.GRU(input_size=3, hidden_size=4, num_layers=1, bidirectional=True, batch_first=True)
output, hidden = gru3(x, h0)
print('Bidirectional GRU')
print('output shape:', output.shape)
print('output:', output.data)
print('hidden shape:', hidden.shape)
print('hidden:', hidden.data)Bidirectional GRU
output shape: torch.Size([1, 5, 8])
output: tensor([[[-0.2406, 0.5601, -0.0119, 0.4332, 0.4604, -0.4934, 0.3282,
-0.3649],
[-0.2339, 0.5399, -0.0937, -0.0575, 0.6697, 0.0072, 0.3696,
-0.3461],
[-0.0217, 0.2809, 0.0145, 0.0782, 0.7410, 0.4955, 0.1322,
-0.5562],
[ 0.0898, 0.2370, -0.1471, 0.3458, 0.5239, -0.0251, 0.0267,
-0.8583],
[ 0.4711, -0.0497, 0.1791, 0.3069, 0.3998, -0.0756, -0.2502,
-1.0351]]])
hidden shape: torch.Size([2, 1, 4])
hidden: tensor([[[ 0.4711, -0.0497, 0.1791, 0.3069]],
[[ 0.4604, -0.4934, 0.3282, -0.3649]]])################################################################
### TODO: Make your own GRU!
### Here, you can make your own GRU changing the dimensions, number of layers, bidirectional etc.
class GRU(nn.Module):
def __init__(self, input_size, hidden_size, output_size, num_layers, bidirectional):
super().__init__()
self.input_size, self.hidden_size, self.output_size = input_size, hidden_size, output_size
self.num_layers = num_layers
self.num_directions = 2 if bidirectional else 1
self.gru = nn.GRU(input_size=self.input_size, hidden_size = self.hidden_size, num_layers=self.num_layers, bidirectional=bidirectional, batch_first=True)
self.output_fc = nn.Linear(self.hidden_size * self.num_directions, output_size)
def forward(self, x):
self.batch_size = x.size(0)
h0 = self.init_hidden()
output, hidden = self.gru(x, h0)
output = self.output_fc(output[:, -1, :])
return output
def init_hidden(self):
return torch.zeros(self.num_layers * self.num_directions, self.batch_size, self.hidden_size)## Run your own GRU!
input_size = 3 ; hidden_size = 4; output_size = 2
num_layers = 1
bidirectional = True
x = torch.randn(1, 4, input_size) # batch_size, seq_len, input_size
gru = GRU(input_size, hidden_size, output_size, num_layers, bidirectional)
output = gru(x)
print('output shape:', output.shape)
print('output: ', output.data)output shape: torch.Size([1, 2])
output: tensor([[0.2999, 0.2196]])3) Padding
We can make better use of the GPU by training on batches of many sequences at once, but doing so brings up the question of how to deal with sequences of variable lengths. The simple solution is to "pad" the shorter sentences with some padding symbol (in this case 0).
from string import punctuation
from collections import Counter
import numpy as np
import torch
import torch.nn as nn
from torch.utils.data import DataLoader, TensorDataset
import random!wget https://www.dropbox.com/s/oyaa5mbla6ubsnq/reviews.txt
!wget https://www.dropbox.com/s/zlg9mdukkwo241j/labels.txt--2023-03-23 05:05:38-- https://www.dropbox.com/s/oyaa5mbla6ubsnq/reviews.txt
Resolving www.dropbox.com (www.dropbox.com)... 162.125.81.18, 2620:100:6016:18::a27d:112
Connecting to www.dropbox.com (www.dropbox.com)|162.125.81.18|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: /s/raw/oyaa5mbla6ubsnq/reviews.txt [following]
--2023-03-23 05:05:38-- https://www.dropbox.com/s/raw/oyaa5mbla6ubsnq/reviews.txt
Reusing existing connection to www.dropbox.com:443.
HTTP request sent, awaiting response... 302 Found
Location: https://uc26ba05440661fbcddd22546bd0.dl.dropboxusercontent.com/cd/0/inline/B4xOcbZhQKHPx649_PZycRs3pZ_Gca6y3kR04ZOptJWlnzN2eXXYpGiviGqijskCwAR8hgzHaNck6uBnkEJeOsmX9djk9GK5GAheGerB4WcdapSOFkDBL9yek-Z90ZITPkJB564iGB3llwEihTIPG8z67eRxookpSrqyCprS8gZOHg/file# [following]
--2023-03-23 05:05:38-- https://uc26ba05440661fbcddd22546bd0.dl.dropboxusercontent.com/cd/0/inline/B4xOcbZhQKHPx649_PZycRs3pZ_Gca6y3kR04ZOptJWlnzN2eXXYpGiviGqijskCwAR8hgzHaNck6uBnkEJeOsmX9djk9GK5GAheGerB4WcdapSOFkDBL9yek-Z90ZITPkJB564iGB3llwEihTIPG8z67eRxookpSrqyCprS8gZOHg/file
Resolving uc26ba05440661fbcddd22546bd0.dl.dropboxusercontent.com (uc26ba05440661fbcddd22546bd0.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f
Connecting to uc26ba05440661fbcddd22546bd0.dl.dropboxusercontent.com (uc26ba05440661fbcddd22546bd0.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 114035 (111K) [text/plain]
Saving to: ‘reviews.txt’
reviews.txt 100%[===================>] 111.36K --.-KB/s in 0.004s
2023-03-23 05:05:39 (30.6 MB/s) - ‘reviews.txt’ saved [114035/114035]
--2023-03-23 05:05:39-- https://www.dropbox.com/s/zlg9mdukkwo241j/labels.txt
Resolving www.dropbox.com (www.dropbox.com)... 162.125.64.18, 2620:100:6016:18::a27d:112
Connecting to www.dropbox.com (www.dropbox.com)|162.125.64.18|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: /s/raw/zlg9mdukkwo241j/labels.txt [following]
--2023-03-23 05:05:40-- https://www.dropbox.com/s/raw/zlg9mdukkwo241j/labels.txt
Reusing existing connection to www.dropbox.com:443.
HTTP request sent, awaiting response... 302 Found
Location: https://ucb97082b012e4172848bba79ed6.dl.dropboxusercontent.com/cd/0/inline/B4w6Xkcr4XTNQAqu1PVJ3gTMBIOL1MOUSVuDWv9s9Zyj1KnO49BSMCDZjVvS-KqVpoyVYPDWiBxXIIWmWoy9BTOLkyPBYtSmTf0PLwoAjcRq0Gysx8wmzkNOI4H5MgSCh0TRcsGTqXjR4sLR_7sYd8T_6jukIJCBP8vaATGZrPfWpg/file# [following]
--2023-03-23 05:05:41-- https://ucb97082b012e4172848bba79ed6.dl.dropboxusercontent.com/cd/0/inline/B4w6Xkcr4XTNQAqu1PVJ3gTMBIOL1MOUSVuDWv9s9Zyj1KnO49BSMCDZjVvS-KqVpoyVYPDWiBxXIIWmWoy9BTOLkyPBYtSmTf0PLwoAjcRq0Gysx8wmzkNOI4H5MgSCh0TRcsGTqXjR4sLR_7sYd8T_6jukIJCBP8vaATGZrPfWpg/file
Resolving ucb97082b012e4172848bba79ed6.dl.dropboxusercontent.com (ucb97082b012e4172848bba79ed6.dl.dropboxusercontent.com)... 162.125.64.15, 2620:100:6016:15::a27d:10f
Connecting to ucb97082b012e4172848bba79ed6.dl.dropboxusercontent.com (ucb97082b012e4172848bba79ed6.dl.dropboxusercontent.com)|162.125.64.15|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 4499 (4.4K) [text/plain]
Saving to: ‘labels.txt’
labels.txt 100%[===================>] 4.39K --.-KB/s in 0s
2023-03-23 05:05:42 (542 MB/s) - ‘labels.txt’ saved [4499/4499]with open ('reviews.txt', 'r') as f:
reviews = f.read()
with open('labels.txt', 'r') as f:
labels = f.read()
print(reviews[:50])
print()
print(labels[:26])The characters are unlikeable and the script is aw
negative
negative
negative0) Preprocessing
# lower case
reviews = reviews.lower()
# remove punctuation
all_text = ''.join([c for c in reviews if c not in punctuation])
# create list of reviews
reviews_split = all_text.split('\n')
labels_split = labels.split('\n')
print ('Number of reviews :', len(reviews_split))
## tokenize - create vocab to int mapping dictionary
all_text2 = ' '.join(reviews_split)
# create a list of words
words = all_text2.split()
# Count all the words using Counter Method
count_words = Counter(words)
total_words = len(words)
sorted_words = count_words.most_common(total_words)
print("count_words")
print(count_words)
print()
# make vocab2int
#There is a small trick here, in this mapping index will start from 0
#i.e. mapping of ‘the’ will be 0. But later on we are going to do padding for shorter reviews and conventional choice for padding is 0. So we need to start this indexing from 1
vocab_to_int = {w:i+1 for i, (w,c) in enumerate(sorted_words)}
print("vocab_to_int")
print(vocab_to_int)Number of reviews : 500
count_words
Counter({'the': 996, 'a': 637, 'this': 547, 'and': 502, 'it': 491, 'i': 460, 'is': 446, 'of': 429, 'movie': 411, 'to': 379, 'was': 239, 'in': 236, 'that': 180, 'film': 166, 'you': 161, 'but': 155, 'one': 144, 'for': 140, 'not': 139, 'with': 136, 'br': 135, 'its': 127, 'on': 121, 'bad': 117, 'good': 112, 'as': 109, 'have': 109, 'be': 97, 'are': 92, 'great': 90, 'so': 85, 'if': 85, 'at': 84, 'like': 84, 'my': 83, 'all': 81, 'very': 80, 'see': 79, 'just': 79, 'an': 79, 'time': 76, 'from': 73, 'acting': 68, 'by': 65, 'ever': 65, 'seen': 65, 'movies': 63, 'dont': 63, 'watch': 61, 'what': 60, 'story': 60, 'me': 59, 'has': 58, 'really': 57, 'plot': 56, 'no': 56, 'some': 55, 'worst': 54, 'about': 54, 'out': 53, 'there': 52, 'only': 51, 'even': 51, 'will': 51, 'they': 49, 'too': 48, 'more': 46, 'when': 44, 'love': 44, 'much': 43, 'or': 43, 'can': 42, 'who': 42, 'he': 42, 'made': 40, 'his': 39, 'best': 38, 'had': 38, 'would': 38, 'were': 38, 'think': 37, 'up': 37, 'your': 36, 'how': 35, 'do': 35, 'saw': 33, 'people': 33, 'make': 33, 'go': 33, 'funny': 33, 'waste': 31, 'first': 31, 'could': 30, 'ive': 30, 'well': 30, 'watching': 29, 'because': 29, 'than': 29, 'better': 29, 'terrible': 28, 'did': 28, 'script': 27, 'any': 27, 'didnt': 27, 'way': 27, 'cast': 27, 'cant': 27, 'tv': 27, 'again': 26, 'into': 26, 'been': 25, 'never': 25, 'most': 25, 'get': 24, 'real': 24, 'comedy': 24, 'little': 24, 'characters': 23, 'actors': 23, 'special': 23, 'im': 23, 'why': 23, 'fun': 22, 'still': 22, 'effects': 21, 'should': 21, 'years': 21, 'after': 21, 'minutes': 21, 'boring': 21, 'films': 21, 'say': 20, 'thing': 20, 'end': 20, 'scenes': 20, 'their': 20, 'her': 20, 'money': 19, 'those': 19, 'know': 19, 'other': 19, 'got': 19, 'then': 19, 'rent': 19, 'over': 19, 'does': 19, 'nothing': 19, 'here': 18, '10': 18, 'life': 18, 'she': 18, 'wonderful': 18, 'performance': 18, 'being': 17, 'video': 17, 'want': 17, 'look': 17, 'which': 17, 'many': 17, 'also': 17, 'makes': 17, 'lot': 17, 'long': 17, 'poor': 16, 'worth': 16, 'whole': 16, 'classic': 16, 'old': 16, 'another': 16, 'interesting': 16, 'such': 16, 'character': 16, 'work': 16, 'scene': 16, 'ending': 15, 'true': 15, 'absolutely': 15, 'them': 15, 'horrible': 15, 'may': 15, 'especially': 15, 'we': 15, 'looks': 15, 'give': 15, 'humor': 15, 'awful': 14, 'while': 14, 'every': 14, 'least': 14, 'without': 14, 'liked': 14, 'believe': 14, 'pretty': 14, 'now': 13, 'actor': 13, 'horror': 13, 'sorry': 13, '1': 13, 'watched': 13, 'big': 13, 'wasnt': 13, 'avoid': 13, 'hard': 13, 'once': 13, 'times': 13, 'everyone': 13, 'music': 13, 'enjoyed': 13, 'excellent': 13, 'definitely': 12, 'far': 12, 'few': 12, 'same': 12, 'totally': 12, 'recommend': 12, 'written': 12, 'before': 12, 'doesnt': 12, 'must': 12, 'dialogue': 12, 'original': 12, 'put': 12, 'these': 12, 'director': 12, 'favorite': 12, 'am': 11, 'fan': 11, 'two': 11, 'stupid': 11, 'sequel': 11, 'seeing': 11, 'predictable': 11, 'enjoy': 11, 'last': 11, 'maybe': 11, 'action': 11, 'where': 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vocab_to_int
{'the': 1, 'a': 2, 'this': 3, 'and': 4, 'it': 5, 'i': 6, 'is': 7, 'of': 8, 'movie': 9, 'to': 10, 'was': 11, 'in': 12, 'that': 13, 'film': 14, 'you': 15, 'but': 16, 'one': 17, 'for': 18, 'not': 19, 'with': 20, 'br': 21, 'its': 22, 'on': 23, 'bad': 24, 'good': 25, 'as': 26, 'have': 27, 'be': 28, 'are': 29, 'great': 30, 'so': 31, 'if': 32, 'at': 33, 'like': 34, 'my': 35, 'all': 36, 'very': 37, 'see': 38, 'just': 39, 'an': 40, 'time': 41, 'from': 42, 'acting': 43, 'by': 44, 'ever': 45, 'seen': 46, 'movies': 47, 'dont': 48, 'watch': 49, 'what': 50, 'story': 51, 'me': 52, 'has': 53, 'really': 54, 'plot': 55, 'no': 56, 'some': 57, 'worst': 58, 'about': 59, 'out': 60, 'there': 61, 'only': 62, 'even': 63, 'will': 64, 'they': 65, 'too': 66, 'more': 67, 'when': 68, 'love': 69, 'much': 70, 'or': 71, 'can': 72, 'who': 73, 'he': 74, 'made': 75, 'his': 76, 'best': 77, 'had': 78, 'would': 79, 'were': 80, 'think': 81, 'up': 82, 'your': 83, 'how': 84, 'do': 85, 'saw': 86, 'people': 87, 'make': 88, 'go': 89, 'funny': 90, 'waste': 91, 'first': 92, 'could': 93, 'ive': 94, 'well': 95, 'watching': 96, 'because': 97, 'than': 98, 'better': 99, 'terrible': 100, 'did': 101, 'script': 102, 'any': 103, 'didnt': 104, 'way': 105, 'cast': 106, 'cant': 107, 'tv': 108, 'again': 109, 'into': 110, 'been': 111, 'never': 112, 'most': 113, 'get': 114, 'real': 115, 'comedy': 116, 'little': 117, 'characters': 118, 'actors': 119, 'special': 120, 'im': 121, 'why': 122, 'fun': 123, 'still': 124, 'effects': 125, 'should': 126, 'years': 127, 'after': 128, 'minutes': 129, 'boring': 130, 'films': 131, 'say': 132, 'thing': 133, 'end': 134, 'scenes': 135, 'their': 136, 'her': 137, 'money': 138, 'those': 139, 'know': 140, 'other': 141, 'got': 142, 'then': 143, 'rent': 144, 'over': 145, 'does': 146, 'nothing': 147, 'here': 148, '10': 149, 'life': 150, 'she': 151, 'wonderful': 152, 'performance': 153, 'being': 154, 'video': 155, 'want': 156, 'look': 157, 'which': 158, 'many': 159, 'also': 160, 'makes': 161, 'lot': 162, 'long': 163, 'poor': 164, 'worth': 165, 'whole': 166, 'classic': 167, 'old': 168, 'another': 169, 'interesting': 170, 'such': 171, 'character': 172, 'work': 173, 'scene': 174, 'ending': 175, 'true': 176, 'absolutely': 177, 'them': 178, 'horrible': 179, 'may': 180, 'especially': 181, 'we': 182, 'looks': 183, 'give': 184, 'humor': 185, 'awful': 186, 'while': 187, 'every': 188, 'least': 189, 'without': 190, 'liked': 191, 'believe': 192, 'pretty': 193, 'now': 194, 'actor': 195, 'horror': 196, 'sorry': 197, '1': 198, 'watched': 199, 'big': 200, 'wasnt': 201, 'avoid': 202, 'hard': 203, 'once': 204, 'times': 205, 'everyone': 206, 'music': 207, 'enjoyed': 208, 'excellent': 209, 'definitely': 210, 'far': 211, 'few': 212, 'same': 213, 'totally': 214, 'recommend': 215, 'written': 216, 'before': 217, 'doesnt': 218, 'must': 219, 'dialogue': 220, 'original': 221, 'put': 222, 'these': 223, 'director': 224, 'favorite': 225, 'am': 226, 'fan': 227, 'two': 228, 'stupid': 229, 'sequel': 230, 'seeing': 231, 'predictable': 232, 'enjoy': 233, 'last': 234, 'maybe': 235, 'action': 236, 'where': 237, 'thought': 238, 'sense': 239, 'year': 240, 'instead': 241, 'laugh': 242, 'found': 243, 'off': 244, 'shows': 245, 'going': 246, 'screen': 247, 'performances': 248, 'simply': 249, 'scary': 250, 'actually': 251, 'worse': 252, 'back': 253, 'youll': 254, 'youre': 255, 'low': 256, 'something': 257, 'down': 258, 'wonder': 259, 'through': 260, 'away': 261, 'yourself': 262, 'line': 263, 'talent': 264, 'direction': 265, 'anything': 266, 'loved': 267, 'show': 268, 'heart': 269, 'thats': 270, 'isnt': 271, 'find': 272, 'rating': 273, 'hilarious': 274, 'beautiful': 275, 'part': 276, 'weak': 277, 'piece': 278, 'although': 279, 'anyone': 280, 'gets': 281, 'done': 282, 'quite': 283, 'beginning': 284, 'feel': 285, 'dull': 286, 'second': 287, 'top': 288, 'buy': 289, 'always': 290, 'take': 291, 'budget': 292, 'family': 293, 'chance': 294, 'michael': 295, 'serious': 296, 'lame': 297, 'point': 298, 'seem': 299, 'idea': 300, 'production': 301, 'own': 302, 'highly': 303, 'gave': 304, 'bored': 305, 'soundtrack': 306, 'kind': 307, 'start': 308, 'someone': 309, 'between': 310, '2': 311, 'young': 312, 'laughs': 313, 'entertainment': 314, 'girl': 315, 'sort': 316, 'hope': 317, 'perfect': 318, 'gives': 319, 'right': 320, 'disney': 321, 'else': 322, 'mind': 323, 'stuff': 324, 'john': 325, 'remember': 326, 'things': 327, 'rather': 328, 'silly': 329, 'disappointed': 330, 'badly': 331, 'unless': 332, 'looking': 333, 'amazing': 334, 'poorly': 335, 'lots': 336, 'wait': 337, 'incredibly': 338, 'mean': 339, 'series': 340, 'version': 341, 'went': 342, 'moving': 343, 'role': 344, 'sure': 345, 'decent': 346, 'though': 347, 'night': 348, 'probably': 349, '20': 350, 'left': 351, 'save': 352, 'new': 353, 'children': 354, 'picture': 355, 'reason': 356, 'become': 357, '5': 358, 'throughout': 359, 'home': 360, 'ago': 361, 'day': 362, 'attempt': 363, 'him': 364, 'since': 365, 'felt': 366, 'play': 367, 'trying': 368, 'mr': 369, 'cinema': 370, 'book': 371, 'brilliant': 372, 'copy': 373, 'change': 374, 'major': 375, 'dvd': 376, 'opinion': 377, 'lines': 378, 'around': 379, 'crap': 380, 'able': 381, 'perhaps': 382, 'typical': 383, 'annoying': 384, 'romance': 385, 'talented': 386, 'storyline': 387, 'somewhat': 388, 'dog': 389, 'beyond': 390, 'world': 391, 'sad': 392, 'cartoon': 393, 'enough': 394, 'takes': 395, 'kids': 396, 'supposed': 397, 'woman': 398, 'gun': 399, 'documentary': 400, 'plays': 401, 'moments': 402, 'american': 403, 'main': 404, 'both': 405, 'theres': 406, 'redeeming': 407, 'none': 408, 'wasted': 409, 'easily': 410, 'seems': 411, 'disappointment': 412, 'women': 413, 'theater': 414, 'yes': 415, 'everything': 416, 'id': 417, 'need': 418, 'cool': 419, 'laughed': 420, 'question': 421, 'touching': 422, 'superb': 423, 'miss': 424, 'reality': 425, 'hell': 426, 'taste': 427, 'wrong': 428, 'impressive': 429, 'friends': 430, 'comment': 431, 'ok': 432, 'care': 433, 'starts': 434, 'truly': 435, 'imagine': 436, 'thinking': 437, 'acted': 438, 'example': 439, 'expect': 440, 'goes': 441, 'seemed': 442, 'henry': 443, 'sit': 444, 'premise': 445, 'slow': 446, 'short': 447, 'knew': 448, 'possibly': 449, 'stars': 450, 'apart': 451, 'dark': 452, 'certainly': 453, 'place': 454, 'missed': 455, 'act': 456, 'wife': 457, 'fine': 458, 'couldnt': 459, 'run': 460, 'come': 461, 'development': 462, 'child': 463, 'pathetic': 464, 'costs': 465, 'leave': 466, 'fight': 467, 'compared': 468, 'itself': 469, 'fantastic': 470, 'moment': 471, 'joke': 472, 'walked': 473, 'lost': 474, 'might': 475, 'friend': 476, 'feeling': 477, 'hold': 478, 'death': 479, 'quality': 480, 'told': 481, 'understand': 482, 'underrated': 483, 'blow': 484, 'oh': 485, 'particularly': 486, 'value': 487, 'man': 488, 'david': 489, 'three': 490, 'moviebr': 491, 'unwatchable': 492, 'huge': 493, 'summer': 494, 'ridiculous': 495, 'except': 496, 'read': 497, 'try': 498, 'greatest': 499, 'let': 500, 'sometimes': 501, 'cult': 502, 'filmed': 503, 'guy': 504, 'days': 505, 'live': 506, 'finish': 507, 'italian': 508, 'small': 509, 'outstanding': 510, 'wanted': 511, 'along': 512, 'matter': 513, 'please': 514, 'bother': 515, 'shame': 516, 'mess': 517, 'cheesy': 518, 'insomnia': 519, 'laughable': 520, 'potential': 521, 'ages': 522, 'despite': 523, 'tell': 524, 'deserves': 525, 'use': 526, 'renting': 527, 'b': 528, 'several': 529, 'took': 530, 'stinks': 531, 'five': 532, 'star': 533, 'mystery': 534, 'head': 535, 'jokes': 536, 'leaves': 537, 'rest': 538, 'cannot': 539, 'call': 540, 'masterpiece': 541, 'stick': 542, 'until': 543, 'basically': 544, 'thriller': 545, 'pointless': 546, 'crappy': 547, 'different': 548, 'late': 549, 'fast': 550, 'glad': 551, 'fell': 552, 'asleep': 553, 'emotion': 554, 'case': 555, 'directing': 556, 'bottom': 557, 'release': 558, 'earth': 559, 'doing': 560, 'job': 561, 'stop': 562, 'usual': 563, 'humour': 564, 'anybody': 565, 'included': 566, 'often': 567, 'surprised': 568, 'opportunity': 569, 'store': 570, 'course': 571, 'tries': 572, 'together': 573, 'himself': 574, 'sick': 575, 'class': 576, 'student': 577, 'turkey': 578, '7': 579, 'comes': 580, 'fact': 581, 'unfortunately': 582, 'bit': 583, 'cute': 584, 'yeah': 585, 'result': 586, 'given': 587, 'cheap': 588, 'havent': 589, 'pure': 590, 'trash': 591, 'our': 592, 'interest': 593, 'besides': 594, 'getting': 595, '3': 596, 'plan': 597, 'making': 598, 'entertaining': 599, 'god': 600, 'writing': 601, 'dollar': 602, 'genius': 603, 'full': 604, 'creepy': 605, 'set': 606, 'released': 607, 'human': 608, 'peter': 609, 'view': 610, 'eyes': 611, 'intended': 612, 'feature': 613, 'simple': 614, 'hardly': 615, 'stayed': 616, 'mans': 617, 'message': 618, 'botched': 619, 'editing': 620, 'guess': 621, 'screenplay': 622, 'williams': 623, 'stage': 624, 'high': 625, 'unfunny': 626, 'played': 627, 'add': 628, 'television': 629, 'us': 630, 'features': 631, 'annoyed': 632, 'lead': 633, 'scifi': 634, '4': 635, 'provide': 636, '25': 637, 'used': 638, 'values': 639, 'works': 640, 'fu': 641, 'impressed': 642, 'wont': 643, 'gene': 644, 'soon': 645, 'caught': 646, 'cried': 647, 'sex': 648, 'subject': 649, 'myself': 650, 'aint': 651, 'nearly': 652, 'cry': 653, 'aside': 654, 'japanese': 655, 'words': 656, 'sucks': 657, 'shot': 658, 'dr': 659, 'charm': 660, 'hours': 661, 'animal': 662, 'art': 663, 'effect': 664, 'contrived': 665, 'ron': 666, 'van': 667, 'episode': 668, 'review': 669, 'four': 670, 'involved': 671, 'hour': 672, 'suspense': 673, '15': 674, 'remarkable': 675, 'free': 676, 'julia': 677, 'smile': 678, 'face': 679, 'cash': 680, 'camera': 681, 'later': 682, 'dancing': 683, 'type': 684, '70s': 685, '210': 686, 'almost': 687, 'problems': 688, 'possible': 689, 'sounded': 690, 'russian': 691, 'intelligent': 692, 'looked': 693, 'century': 694, 'festival': 695, 'extremely': 696, 'beautifully': 697, 'attention': 698, 'likes': 699, 'rental': 700, 'cable': 701, 'supporting': 702, 'shelf': 703, 'killing': 704, 'paid': 705, 'wouldnt': 706, 'joe': 707, 'miserably': 708, 'animation': 709, 'itbr': 710, '110': 711, 'finally': 712, 'excuse': 713, 'martin': 714, 'begin': 715, 'fall': 716, 'angel': 717, 'town': 718, 'hollywood': 719, 'doubt': 720, 'garbage': 721, '410': 722, 'slasher': 723, 'cold': 724, 'cinematography': 725, 'king': 726, 'portrays': 727, 'person': 728, 'reviews': 729, 'dialog': 730, 'turned': 731, 'boy': 732, 'thrown': 733, 'failed': 734, 'camp': 735, 'impossible': 736, 'alien': 737, 'regret': 738, 'positive': 739, 'bunch': 740, 'plots': 741, 'surprise': 742, 'spending': 743, 'side': 744, 'air': 745, 'ruined': 746, 'tells': 747, 'english': 748, 'difficult': 749, 'couple': 750, 'puts': 751, 'yet': 752, 'justice': 753, 'youve': 754, 'admit': 755, 'help': 756, 'comments': 757, 'spent': 758, 'waiting': 759, 'collection': 760, 'alot': 761, 'nine': 762, 'crazy': 763, 'appealing': 764, 'form': 765, 'force': 766, 'comic': 767, 'tale': 768, 'okay': 769, 'wish': 770, 'unpleasant': 771, 'amateurish': 772, 'sat': 773, 'silent': 774, 'past': 775, 'sellers': 776, 'adventure': 777, 'youd': 778, 'arent': 779, 'shallow': 780, 'history': 781, 'james': 782, 'sounds': 783, 'china': 784, 'feels': 785, 'british': 786, 'total': 787, 'kill': 788, 'mostly': 789, 'hit': 790, 'ends': 791, 'keep': 792, 'girls': 793, 'during': 794, 'further': 795, 'qualities': 796, 'name': 797, 'toilet': 798, 'vampire': 799, 'gore': 800, 'nudity': 801, 'gratuitous': 802, 'either': 803, 'powerful': 804, 'basic': 805, 'wow': 806, 'others': 807, 'drama': 808, 'school': 809, 'actresses': 810, 'list': 811, 'audience': 812, 'ranks': 813, 'jr': 814, 'robin': 815, 'wants': 816, 'suck': 817, 'produced': 818, 'filmmaker': 819, 'tim': 820, 'said': 821, 'mediocre': 822, 'vampires': 823, 'brooks': 824, 'sucked': 825, 'kid': 826, 'dance': 827, 'compelling': 828, 'talking': 829, 'combination': 830, 'mia': 831, 'surprisingly': 832, 'tom': 833, 'reasons': 834, 'popular': 835, 'starring': 836, 'terrific': 837, 'pity': 838, 'breasts': 839, 'clear': 840, 'turns': 841, 'hear': 842, 'songs': 843, 'mom': 844, 'kept': 845, 'hundred': 846, 'subtle': 847, 'emotions': 848, 'higher': 849, 'pacino': 850, 'stunning': 851, 'melodrama': 852, 'awesome': 853, 'incredible': 854, 'twice': 855, 'fresh': 856, 'stand': 857, 'lovely': 858, 'strong': 859, 'future': 860, 'cares': 861, 'sailors': 862, 'length': 863, 'enjoyable': 864, 'talents': 865, 'grease': 866, 'prefer': 867, 'eighties': 868, 'teenager': 869, 'silver': 870, 'lousy': 871, 'kinda': 872, 'cure': 873, 'drink': 874, 'invisible': 875, 'running': 876, 'iii': 877, 'tremors': 878, 'nowhere': 879, 'walk': 880, 'saying': 881, 'forget': 882, 'lowbudget': 883, 'gem': 884, 'guns': 885, 'iq': 886, 'mary': 887, 'la': 888, 'william': 889, 'remake': 890, 'bland': 891, 'cheese': 892, 'experience': 893, 'cameras': 894, 'style': 895, 'points': 896, 'weeks': 897, 'favor': 898, 'damme': 899, 'sports': 900, 'black': 901, 'worked': 902, 'half': 903, 'already': 904, 'twist': 905, 'based': 906, 'page': 907, 'f': 908, 'min': 909, 'overall': 910, 'roberts': 911, 'dumb': 912, 'happy': 913, 'ended': 914, 'relationships': 915, 'coming': 916, 'impression': 917, 'billy': 918, 'normally': 919, 'brings': 920, 'laughter': 921, 'bring': 922, 'die': 923, 'paint': 924, 'entire': 925, 'problem': 926, 'magical': 927, '300': 928, 'grade': 929, 'forward': 930, 'fits': 931, 'early': 932, 'pretentious': 933, 'freddy': 934, 'interaction': 935, 'followed': 936, 'jewish': 937, 'costumes': 938, 'warped': 939, 'fights': 940, 'photographed': 941, 'raise': 942, 'stewart': 943, 'blend': 944, 'catch': 945, 'appearance': 946, 'male': 947, 'themselves': 948, 'suggests': 949, 'street': 950, 'actual': 951, 'theatre': 952, 'fails': 953, 'threw': 954, 'sleep': 955, 'cliched': 956, 'pay': 957, 'slam': 958, 'disappointing': 959, 'amused': 960, 'meets': 961, 'cartoons': 962, 'advice': 963, 'olivier': 964, 'racist': 965, 'police': 966, 'worthwhile': 967, 'sets': 968, 'racing': 969, 'having': 970, 'priest': 971, 'technically': 972, 'utterly': 973, 'disturbing': 974, 'rarely': 975, 'tension': 976, 'expecting': 977, 'don': 978, 'johnson': 979, 'cut': 980, 'obvious': 981, '8': 982, 'flick': 983, 'satire': 984, 'redemption': 985, 'screwed': 986, 'ridiculously': 987, 'offer': 988, 'lower': 989, 'minute': 990, 'watchbr': 991, 'finding': 992, '1980': 993, '310': 994, 'claiming': 995, 'elvis': 996, 'carter': 997, 'worthy': 998, 'figure': 999, 'cover': 1000, 'messed': 1001, 'rated': 1002, 'ill': 1003, 'animals': 1004, 'indian': 1005, 'plain': 1006, 'sight': 1007, 'ask': 1008, 'follow': 1009, 'charlie': 1010, 'chaplin': 1011, 'himbr': 1012, 'lets': 1013, 'team': 1014, 'root': 1015, 'provides': 1016, 'saving': 1017, 'grace': 1018, 'pecker': 1019, 'dimensional': 1020, 'viewers': 1021, 'belief': 1022, 'close': 1023, 'local': 1024, 'onebr': 1025, 'supposedly': 1026, 'ways': 1027, 'spot': 1028, 'manages': 1029, 'creditable': 1030, 'branagh': 1031, 'under': 1032, 'missing': 1033, 'empty': 1034, 'clearly': 1035, 'next': 1036, 'ugh': 1037, 'hats': 1038, 'suitable': 1039, 'body': 1040, 'u': 1041, 'werent': 1042, 'hopelessly': 1043, '101': 1044, 'however': 1045, 'generally': 1046, 'entertained': 1047, 'critical': 1048, '30': 1049, 'expected': 1050, 'tedious': 1051, 'despicable': 1052, 'unhappy': 1053, 'complete': 1054, 'western': 1055, 'worlds': 1056, 'funniest': 1057, 'betty': 1058, 'thanks': 1059, 'pet': 1060, 'bore': 1061, 'photography': 1062, 'somewhere': 1063, 'quit': 1064, 'ahead': 1065, 'reasonably': 1066, 'journey': 1067, 'magnificent': 1068, 'duchovny': 1069, 'decide': 1070, 'neither': 1071, 'upon': 1072, 'sums': 1073, 'heads': 1074, 'mood': 1075, 'santa': 1076, 'mixes': 1077, 'satan': 1078, 'whatever': 1079, 'painful': 1080, 'ben': 1081, 'speaks': 1082, 'unbelievable': 1083, 'ii': 1084, 'don´t': 1085, 'recent': 1086, 'rip': 1087, 'gonna': 1088, 'filmbr': 1089, 'mebr': 1090, 'fx': 1091, 'nicole': 1092, 'kidman': 1093, 'gone': 1094, 'pass': 1095, 'returns': 1096, 'killed': 1097, 'favour': 1098, 'scream': 1099, 'needed': 1100, 'sleeping': 1101, 'draws': 1102, 'renewed': 1103, 'race': 1104, 'lacks': 1105, 'front': 1106, 'dying': 1107, 'weather': 1108, 'background': 1109, 'lifeless': 1110, 'attempted': 1111, 'illogical': 1112, 'regarded': 1113, 'suggest': 1114, 'corny': 1115, 'men': 1116, 'lynch': 1117, 'highschool': 1118, 'insufferable': 1119, 'intentions': 1120, 'showed': 1121, 'novel': 1122, 'changed': 1123, 'african': 1124, 'tried': 1125, 'sorta': 1126, 'oneliners': 1127, 'mulder': 1128, 'chemistry': 1129, 'lowest': 1130, 'theaters': 1131, 'zombie': 1132, 'sound': 1133, 'neat': 1134, 'success': 1135, 'lack': 1136, 'channel': 1137, 'violence': 1138, 'greater': 1139, '610': 1140, 'nice': 1141, 'agree': 1142, 'moved': 1143, 'reminded': 1144, 'smart': 1145, 'stories': 1146, 'directed': 1147, 'suffice': 1148, 'josh': 1149, 'leonard': 1150, 'smith': 1151, 'each': 1152, 'ashamed': 1153, 'coupe': 1154, 'desperate': 1155, '6': 1156, 'started': 1157, 'it´s': 1158, 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1219, 'mtv': 1220, 'gags': 1221, 'cell': 1222, 'mad': 1223, 'expectations': 1224, 'excited': 1225, 'country': 1226, 'courage': 1227, 'mummy': 1228, 'average': 1229, 'french': 1230, 'columbo': 1231, 'falk': 1232, 'forbr': 1233, 'chris': 1234, 'nervous': 1235, 'finished': 1236, 'formula': 1237, 'fabulous': 1238, 'ford': 1239, '19': 1240, 'fire': 1241, 'cliches': 1242, 'cameos': 1243, 'quote': 1244, 'buffy': 1245, 'lacking': 1246, 'bothered': 1247, 'soap': 1248, 'opera': 1249, 'bat': 1250, 'eat': 1251, 'humans': 1252, 'fans': 1253, 'drawn': 1254, 'judge': 1255, 'hasnt': 1256, 'pop': 1257, 'event': 1258, 'massacre': 1259, 'innocent': 1260, 'practically': 1261, 'boobs': 1262, 'credits': 1263, 'added': 1264, 'check': 1265, 'red': 1266, 'loren': 1267, 'onto': 1268, 'manchu': 1269, 'hands': 1270, 'opening': 1271, 'training': 1272, 'amusing': 1273, 'hair': 1274, 'says': 1275, 'roth': 1276, 'roles': 1277, 'max': 1278, 'dorothy': 1279, 'fake': 1280, 'tongue': 1281, 'rubbish': 1282, 'lowe': 1283, 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3174, 'cars': 3175, 'blown': 3176, 'descriptionssummaries': 3177, 'wwi': 3178, 'planes': 3179, 'boats': 3180, 'closeups': 3181, 'plane': 3182, 'torpedo': 3183, 'boat': 3184, 'heartily': 3185, 'dan': 3186, 'katzir': 3187, 'simplicity': 3188, 'angle': 3189, 'rejuvinated': 3190, 'lust': 3191, 'frustrated': 3192, 'autobiography': 3193, 'directorproducer': 3194, 'rabins': 3195, 'assassination': 3196, 'vonneguts': 3197, 'known': 3198, 'interestingly': 3199, 'manipulation': 3200, 'nolte': 3201, 'arkin': 3202, 'vonnegut': 3203, 'stranger': 3204, 'odd': 3205, 'details': 3206, 'sadness': 3207, 'definitive': 3208, 'hamlet': 3209, 'cuts': 3210, 'cat': 3211, 'karlofflugosi': 3212, 'collaboration': 3213, 'karloff': 3214, 'overacting': 3215, 'samantha': 3216, 'nick': 3217, 'flirting': 3218, 'hotel': 3219, 'bravo': 3220, 'station': 3221, 'xfiles': 3222, 'continues': 3223, 'scully': 3224, 'replacement': 3225, 'duo': 3226, 'gently': 3227, 'subtly': 3228, 'aids': 3229, 'alluded': 3230, 'gay': 3231, 'age': 3232, 'recommanded1': 3233, 'familys': 3234, 'step': 3235, 'odyssey': 3236, 'heartbreakingly': 3237, 'wistful': 3238, 'memory': 3239, 'watches': 3240, 'dara': 3241, 'tomanovich': 3242, 'dustin': 3243, 'hoffman': 3244, 'cruise': 3245, 'similar': 3246, 'artificial': 3247, 'trivial': 3248, 'comparison': 3249, 'recognizable': 3250, 'faces': 3251, 'personally': 3252, 'sweet': 3253, 'winkler': 3254, 'nonstop': 3255, 'performer': 3256, 'soapoperas': 3257, 'conundrums': 3258, 'testament': 3259, 'ensemble': 3260, 'storytelling': 3261, 'fallible': 3262, 'cope': 3263, 'succeeding': 3264, 'park': 3265, 'factor': 3266, 'naked': 3267, 'h': 3268, 'macy': 3269, 'neve': 3270, 'campbell': 3271, 'donald': 3272, 'traditional': 3273, 'meant': 3274, 'jiggling': 3275, 'suffocates': 3276, 'spectator': 3277, 'extent': 3278, 'objective': 3279, 'trained': 3280, 'psychopath': 3281, 'rediscovering': 3282, 'humanity': 3283, 'near': 3284, 'led': 3285, 'scriptwriters': 3286, 'sentimental': 3287, 'cloudy': 3288, 'wintry': 3289, 'captures': 3290, 'struggle': 3291, 'identity': 3292, 'ongoing': 3293, 'teenage': 3294, 'strangely': 3295, 'documentarynot': 3296, 'unsettling': 3297, 'joy': 3298, 'master': 3299, 'convinced': 3300, 'became': 3301, 'actores': 3302, 'field': 3303, 'drum': 3304, 'cook': 3305, 'unsung': 3306, 'hero': 3307, 'everyones': 3308, 'dream': 3309, 'delivery': 3310, 'baseball': 3311, 'scrutiny': 3312, 'realistic': 3313, 'appears': 3314, 'dennis': 3315, 'quaid': 3316, 'moves': 3317, 'stances': 3318, 'league': 3319, 'pitcher': 3320, 'stooge': 3321, 'shortchristine': 3322, 'mcintyre': 3323, 'oneshe': 3324, 'actressthe': 3325, 'stooges': 3326, 'shemp': 3327, 'larrythis': 3328, 'autumn': 3329, 'link': 3330, 'hypothetically': 3331, 'downfall': 3332, 'socrates': 3333, 'memorized': 3334, 'holds': 3335, 'childhood': 3336, 'brother': 3337, 'respect': 3338, 'fnm': 3339, 'phoenix': 3340, 'behaves': 3341, 'confronting': 3342, 'status': 3343, 'quo': 3344, 'izzard': 3345, 'hysterical': 3346, 'insightful': 3347, 'represents': 3348, 'niche': 3349, 'worldbr': 3350, 'americans': 3351, 'standup': 3352, 'routine': 3353, 'due': 3354, 'palonly': 3355, 'availability': 3356, 'neglected': 3357, 'importance': 3358, 'performing': 3359, 'arts': 3360, 'embedded': 3361, 'rereleased': 3362, 'lord': 3363, 'rings': 3364, 'glued': 3365, 'older': 3366, 'younger': 3367, 'sensationalist': 3368, 'notting': 3369, 'hill': 3370, 'bold': 3371, 'understated': 3372, 'integrity': 3373, 'langlaise': 3374, 'intellectually': 3375, 'monsieur': 3376, 'le': 3377, 'directeur': 3378, 'rohmer': 3379, 'ideal': 3380, 'anime': 3381, 'dubbed': 3382, '1999': 3383, 'meaning': 3384, 'obstinate': 3385, 'description': 3386, 'obviously': 3387, 'ralph': 3388, 'richardson': 3389, 'merle': 3390, 'oberon': 3391, 'created': 3392, 'mrs': 3393, 'zodsworth': 3394, 'donut': 3395, 'post': 3396, 'haste': 3397, 'igor': 3398, 'proof': 3399, 'slovenian': 3400, 'soul': 3401, 'overtaken': 3402, 'rendering': 3403, 'wartime': 3404, 'unknown': 3405, 'faultless': 3406, 'heartrending': 3407, 'brotherly': 3408, 'serbian': 3409, 'everi': 3410, 'wood': 3411, 'engaging': 3412, 'wellmade': 3413, 'liam': 3414, 'neeson': 3415, 'singing': 3416, 'kelly': 3417, 'liberty': 3418, 'meet': 3419, 'break': 3420, 'business': 3421, 'lovers': 3422, 'adrian': 3423, 'pasdar': 3424, 'fascinating': 3425, 'buster': 3426, 'voice': 3427, 'sequences': 3428, 'hang': 3429, 'chariot': 3430, 'shorts': 3431, 'goat': 3432, 'general': 3433, 'multiple': 3434, 'funnier': 3435, 'twin': 3436, 'sisters': 3437, 'soo': 3438, 'thingy': 3439, 'robbed': 3440, 'oscar': 3441, 'charlies': 3442, 'angels': 3443, 'costars': 3444, 'henner': 3445, 'piscopo': 3446, 'danny': 3447, 'devito': 3448, 'award': 3449, 'nominations': 3450, 'calibre': 3451, 'entranced': 3452, 'resist': 3453, 'swing': 3454, 'hereid': 3455, 'unnoticed': 3456, 'rerelease': 3457, 'publicity': 3458, 'promote': 3459, 'paxtonbr': 3460, 'wilderness': 3461, 'natural': 3462, 'understanding': 3463, 'harmony': 3464, 'nature': 3465, 'extreme': 3466, 'naturalist': 3467, 'treasurebr': 3468, 'definition': 3469, 'continue': 3470, 'strikes': 3471, 'cord': 3472, 'scott': 3473, 'glenn': 3474, 'winch': 3475, 'directors': 3476, 'tetsuo': 3477, 'eisenstein': 3478, 'wesleyan': 3479, 'university': 3480, 'haunts': 3481, 'reveals': 3482, 'frightening': 3483, 'aspects': 3484, 'angers': 3485, 'ratings': 3486, 'geniusbr': 3487, 'christ': 3488, 'sakebr': 3489, 'boost': 3490, 'dee': 3491, 'witherspoon': 3492, 'foundation': 3493, 'riffing': 3494, 'mvovies': 3495, '200': 3496, 'tonight': 3497, 'waters': 3498, '25yrs': 3499, 'rivals': 3500, 'zatoichi': 3501, 'katsu': 3502, 'exciting': 3503, 'swordplay': 3504, 'allactioncomedyheroicsand': 3505, 'actorsgunga': 3506, 'din': 3507, 'remain': 3508, 'moviesexcellent': 3509, 'picturei': 3510, 'farrah': 3511, 'fawcett': 3512, 'gritty': 3513, 'tables': 3514, 'russo': 3515, 'contains': 3516, 'mike': 3517, 'smooth': 3518, 'criminal': 3519, 'mj': 3520, 'rockers': 3521, 'simmons': 3522, 'ozzy': 3523, 'osbourne': 3524, 'masterpieces': 3525, 'antonioni': 3526, 'youth': 3527, 'distraction': 3528, 'happiness': 3529, 'alienation': 3530, 'materialism': 3531, 'honor': 3532, 'corruption': 3533, 'artbr': 3534, 'flicks': 3535, 'darn': 3536, 'chuckles': 3537, 'horrorcomedy': 3538, 'throughly': 3539, 'tire': 3540, 'fred': 3541, 'ginger': 3542, 'pleasure': 3543, 'lucy': 3544, 'grable': 3545, 'car': 3546, 'commercial': 3547, 'shelby': 3548, 'angelina': 3549, 'cagebr': 3550, 'bgbr': 3551, 'loving': 3552, 'colours': 3553, 'discovered': 3554, 'atlantis': 3555, 'explore': 3556, 'golden': 3557, 'someplace': 3558, 'reverted': 3559, 'unravel': 3560, 'elizabeth': 3561, 'meter': 3562, 'girlfriend': 3563, 'tourettes': 3564, 'syndrome': 3565, 'amitabh': 3566, 'bachan': 3567, 'govinda': 3568, 'timepass': 3569, 'imitate': 3570, 'attempting': 3571, 'realise': 3572, 'immediately': 3573, 'toobr': 3574, 'essentially': 3575, 'chinese': 3576, 'ghost': 3577, 'installment': 3578, 'subtlety': 3579, 'tony': 3580, 'leung': 3581, 'cgs': 3582, 'joan': 3583, 'hart': 3584, 'match': 3585, 'clarissa': 3586, 'explains': 3587, 'marvelous': 3588, 'slayer': 3589, 'ronald': 3590, 'colman': 3591, 'othello': 3592, 'persona': 3593, 'brian': 3594, 'palmas': 3595, 'undeniable': 3596, 'virtuosity': 3597, 'camouflage': 3598, 'disguised': 3599, 'psycho': 3600, 'carbon': 3601, 'climax': 3602, 'improvement': 3603, 'rauol': 3604, 'richard': 3605, 'dryfus': 3606, 'dana': 3607, 'delany': 3608, 'babe': 3609, 'following': 3610, 'fate': 3611, 'possess': 3612, 'prize': 3613, 'winchester': 3614, '73': 3615, 'cliche': 3616, 'characterisations': 3617, 'mann': 3618, 'ramones': 3619, 'biased': 3620, 'uplifting': 3621, 'alisan': 3622, 'porter': 3623, 'immensely': 3624, 'mplex': 3625, 'neighbors': 3626, 'ills': 3627, 'putting': 3628, 'bath': 3629, 'community': 3630, 'reviewer': 3631, 'heavy': 3632, 'harsh': 3633, 'realities': 3634, 'playful': 3635, 'dwell': 3636, 'smiles': 3637, 'biography': 3638, 'poetry': 3639, 'narration': 3640, 'sir': 3641, 'redgrave': 3642, 'accessible': 3643, 'exposure': 3644, 'tvvideo': 3645, 'slots': 3646, 'stylized': 3647, 'consequencesbr': 3648, 'timesbr': 3649, 'granted': 3650, 'educational': 3651, 'content': 3652, 'foxes': 3653, 'jodie': 3654, 'foster': 3655, 'cherie': 3656, 'currie': 3657, 'marilyn': 3658, 'kagan': 3659, 'kandice': 3660, 'stroh': 3661, 'radio': 3662, 'donna': 3663, 'rented': 3664, 'blew': 3665, 'engrossing': 3666, 'allison': 3667, 'deans': 3668, 'stands': 3669, 'balances': 3670, 'melancholy': 3671, 'iridescent': 3672, 'energy': 3673, 'weighted': 3674, 'places': 3675, 'passes': 3676, 'danielle': 3677, 'steele': 3678, 'test': 3679, 'andie': 3680, 'macdowell': 3681, 'perspective': 3682, 'victims': 3683, 'provokes': 3684, 'antiracism': 3685, 'sees': 3686, 'historic': 3687, 'bucketfuls': 3688, 'skeltonpowelllahrobrien': 3689, 'dynamite': 3690, 'dorseydriven': 3691, 'powells': 3692, 'exceptional': 3693, 'individual': 3694, 'pizzazz': 3695, 'drawings': 3696, 'begun': 3697, 'i´ve': 3698, 'waited': 3699, '21': 3700, 'nooooo': 3701, 'tip': 3702, 'hat': 3703, 'spinal': 3704, 'tap': 3705, 'anywaythe': 3706, 'peanuts': 3707, 'gangsters': 3708, 'videos': 3709, 'bucks': 3710, 'alone': 3711, 'bias': 3712, 'concered': 3713, 'wilder': 3714, 'numerous': 3715, 'additional': 3716, 'detailing': 3717, 'adventures': 3718, 'larry': 3719, 'smiling': 3720, 'meg': 3721, 'feelgood': 3722, 'millennium': 3723, 'vision': 3724, 'establishes': 3725, 'sf': 3726, 'hail': 3727, 'typically': 3728, 'howard': 3729, 'associated': 3730, 'hunt': 3731, 'travels': 3732, 'japan': 3733, 'sales': 3734, 'pitch': 3735, 'whoa': 3736, 'bound': 3737, 'getgo': 3738, 'west': 3739, 'losewest': 3740, 'losing': 3741, 'grabs': 3742, 'honest': 3743, 'portrayal': 3744, 'paralysis': 3745, 'panders': 3746, 'topnotch': 3747, 'flew': 3748, 'cuckoos': 3749, 'nest': 3750, 'wordofmouth': 3751, 'buzz': 3752, 'disabled': 3753, 'keeps': 3754, 'price': 3755, 'bet': 3756, 'ala': 3757, 'pulp': 3758, 'suicides': 3759, '810': 3760, '1010': 3761, 'references': 3762, 'ladybug´s': 3763, 'tribute': 3764, 'lorre': 3765, 'we´ll': 3766, 'talk': 3767, 'flik': 3768, 'reaction': 3769, 'wowyoure': 3770, 'quiet': 3771, 'outtakes': 3772, 'errol': 3773, 'flynns': 3774, 'ward': 3775, 'gut': 3776, 'wrenching': 3777, 'epic': 3778, 'knees': 3779, 'outside': 3780, 'shaolin': 3781, 'temple': 3782, 'learn': 3783, 'dogs': 3784, 'epps': 3785, 'deja': 3786, 'remmi': 3787, 'indie': 3788, 'current': 3789, '27': 3790, 'premium': 3791, 'channels': 3792, 'available': 3793, 'theory': 3794, 'flight': 3795, 'grabbed': 3796, 'held': 3797, 'feelings': 3798, 'bytes': 3799, 'fragile': 3800, 'represented': 3801, 'spain': 3802, 'berlinale': 3803, '2002': 3804, 'grammar': 3805, 'almodovars': 3806, 'filmswell': 3807, 'shouldnt': 3808, 'vh1': 3809, 'breathless': 3810, 'marlene': 3811, 'sleeper': 3812, 'defines': 3813, 'nicholas': 3814, 'cage': 3815, 'intricate': 3816, 'awaybr': 3817, 'whenever': 3818, 'charged': 3819, 'unbelivebly': 3820, '1948': 3821, 'wei': 3822, 'weis': 3823, 'catylast': 3824, 'triangle': 3825, 'oppurunity': 3826, 'shipload': 3827, 'towns': 3828, 'daughters': 3829, 'fathers': 3830, 'extremes': 3831, 'deter': 3832, 'attempts': 3833, 'maidens': 3834, 'aid': 3835, 'dispatch': 3836, 'squad': 3837, 'happen': 3838, 'plummer': 3839, 'dandy': 3840, 'ribaldry': 3841, 'unsurpassed': 3842, 'gena': 3843, 'cheech': 3844, 'funthe': 3845, 'maria': 3846, 'adorable': 3847, 'paulie': 3848, 'sunday': 3849, 'nicely': 3850, 'plenty': 3851, 'hardcore': 3852, 'lauen': 3853, 'montgomery': 3854, 'venus': 3855, 'thankfully': 3856, 'gabriella': 3857, 'hall': 3858, 'violent': 3859, 'highlander': 3860, 'hehe': 3861}encoded_labels = [1 if label =='positive' else 0 for label in labels_split]
encoded_labels = np.array(encoded_labels)# Change input to index
reviews_int = []
for review in reviews_split:
r = [vocab_to_int[w] for w in review.split()]
reviews_int.append(r)
print (reviews_int[0:3])[[1, 118, 29, 1420, 4, 1, 102, 7, 186, 22, 2, 91, 8, 1, 865, 8, 1421, 4, 1422], [1, 92, 276, 8, 866, 20, 325, 1423, 4, 1424, 1425, 325, 7, 17, 8, 1, 77, 9, 18, 1426, 3, 17, 7, 2, 37, 24, 373, 1, 374, 7, 62, 12, 1, 648, 12, 1, 92, 17, 1, 25, 17, 11, 1427, 148, 22, 295, 6, 867, 10, 49, 1, 92, 866], [6, 326, 96, 3, 14, 12, 1, 868, 26, 2, 869, 16, 6, 511, 10, 38, 5, 109, 97, 1428, 1429, 7, 194, 1430, 2, 1431, 26, 2, 296, 195, 50, 1, 426, 11, 246, 23, 12, 1, 868, 3, 7, 2, 54, 24, 14, 20, 24, 427, 4, 24, 119, 210, 2, 91, 8, 138]]## Return features of review_ints, where each review is padded with 0's or truncated to the input seq_length.
def pad_features(reviews_int, seq_length):
features = np.zeros((len(reviews_int), seq_length), dtype = int)
for i, review in enumerate(reviews_int):
review_len = len(review)
if review_len <= seq_length:
zeroes = list(np.zeros(seq_length-review_len))
new = zeroes+review
elif review_len > seq_length:
new = review[0:seq_length]
features[i,:] = np.array(new)
return featuresfrom torch.nn.utils.rnn import pad_sequence
example_reviews = []
for i in range(len(reviews_int)):
example_reviews.append(torch.Tensor(reviews_int[i]))
example_padded = pad_sequence(example_reviews, batch_first=True)seq_length = 200
indexed_reviews = pad_features(reviews_int, seq_length)# Dataloader
# create Tensor Dataset
split_frac = 0.8
train_x = indexed_reviews[0:int(split_frac * len(indexed_reviews))]
train_y = encoded_labels[0:int(split_frac * len(indexed_reviews))]
test_x = indexed_reviews[int(split_frac * len(indexed_reviews)):]
test_y = encoded_labels[int(split_frac * len(indexed_reviews)):]
train_data = TensorDataset(torch.from_numpy(train_x), torch.from_numpy(train_y))
test_data = TensorDataset(torch.from_numpy(test_x), torch.from_numpy(test_y))
batch_size = 50
train_loader = DataLoader(train_data, shuffle=True, batch_size=batch_size)
test_loader = DataLoader(test_data, shuffle=True, batch_size=batch_size)# Check the data
sample_x, sample_y = next(iter(train_loader))
print('Sample input size: ', sample_x.size()) # batch_size, seq_length
print('Sample input: \n', sample_x)
print()
print('Sample label size: ', sample_y.size()) # batch_size
print('Sample label: \n', sample_y)Sample input size: torch.Size([50, 200])
Sample input:
tensor([[ 0, 0, 0, ..., 1110, 102, 202],
[ 0, 0, 0, ..., 2, 989, 929],
[ 0, 0, 0, ..., 3, 20, 3049],
...,
[ 0, 0, 0, ..., 2411, 21, 722],
[ 0, 0, 0, ..., 9, 113, 1744],
[ 0, 0, 0, ..., 50, 1924, 1036]])
Sample label size: torch.Size([50])
Sample label:
tensor([0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1,
0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0])# Sentiment LSTM
class SentimentLSTM(nn.Module):
def __init__(self, vocab_size, output_size, embedding_dim, hidden_dim, n_layers):
super().__init__()
self.output_size = output_size
self.n_layers = n_layers
self.hidden_dim = hidden_dim
self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx = 0)
self.lstm = nn.LSTM(embedding_dim, hidden_dim, n_layers, batch_first = True)
self.output_fc = nn.Linear(hidden_dim, output_size)
self.sig = nn.Sigmoid()
def forward(self, x):
self.batch_size = x.size(0)
embeds = self.embedding(x)
h0, c0 = self.init_hidden()
output, hidden = self.lstm(embeds, (h0, c0))
output = self.output_fc(output[:, -1, :])
output = self.sig(output)
return output
def init_hidden(self):
h0 = torch.zeros(self.n_layers, self.batch_size, self.hidden_dim)
c0 = torch.zeros(self.n_layers, self.batch_size, self.hidden_dim)
return h0, c0# Let's train our model
vocab_size = len(vocab_to_int) + 1 # +1 for the 0 padding
output_size = 1
embedding_dim = 400
hidden_dim = 256
n_layers = 1
sentiment_net = SentimentLSTM(vocab_size, output_size, embedding_dim, hidden_dim, n_layers)
lr = 0.01
n_epochs = 20
counter = 0
criterion = nn.BCELoss()
opt = torch.optim.Adam(sentiment_net.parameters(), lr=lr)
for epoch in range(n_epochs + 1):
for inputs, labels in train_loader:
counter += 1
output = sentiment_net(inputs)
pred = (output.squeeze() > 0.5).float()
acc = torch.mean((pred == labels).float())
loss = criterion(output.squeeze(), labels.float())
opt.zero_grad()
loss.backward()
opt.step()
sentiment_net.eval()
test_losses = []
test_acc = []
for inputs, labels in test_loader:
output = sentiment_net(inputs)
test_loss = criterion(output.squeeze(), labels.float())
test_losses.append(test_loss.item())
pred = (output.squeeze() > 0.5).float()
acc = torch.mean((pred == labels).float())
test_acc.append(acc.item())
sentiment_net.train()
print("Epoch: {}/{} ".format(epoch, n_epochs),
"Step: {} ".format(counter),
"Loss: {:.6f} ".format(loss.item()),
"Acc: {:.6f} ".format(acc.item()),
"Test Loss: {:.6f} ".format(np.mean(test_losses)),
"Test Acc: {:.6f} ".format(np.mean(test_acc)))Epoch: 0/20 Step: 8 Loss: 0.624782 Acc: 0.100000 Test Loss: 1.024876 Test Acc: 0.150000
Epoch: 1/20 Step: 16 Loss: 0.214078 Acc: 0.220000 Test Loss: 1.873867 Test Acc: 0.190000
Epoch: 2/20 Step: 24 Loss: 0.156424 Acc: 0.140000 Test Loss: 2.729647 Test Acc: 0.240000
Epoch: 3/20 Step: 32 Loss: 0.007794 Acc: 0.240000 Test Loss: 3.406398 Test Acc: 0.290000
Epoch: 4/20 Step: 40 Loss: 0.000611 Acc: 0.300000 Test Loss: 3.375081 Test Acc: 0.330000
Epoch: 5/20 Step: 48 Loss: 0.000388 Acc: 0.360000 Test Loss: 3.527202 Test Acc: 0.330000
Epoch: 6/20 Step: 56 Loss: 0.000713 Acc: 0.360000 Test Loss: 3.770726 Test Acc: 0.350000
Epoch: 7/20 Step: 64 Loss: 0.000695 Acc: 0.240000 Test Loss: 4.316965 Test Acc: 0.290000
Epoch: 8/20 Step: 72 Loss: 0.000326 Acc: 0.320000 Test Loss: 4.342142 Test Acc: 0.290000
Epoch: 9/20 Step: 80 Loss: 0.000185 Acc: 0.400000 Test Loss: 4.365341 Test Acc: 0.320000
Epoch: 10/20 Step: 88 Loss: 0.000061 Acc: 0.280000 Test Loss: 4.432155 Test Acc: 0.320000
Epoch: 11/20 Step: 96 Loss: 0.000076 Acc: 0.300000 Test Loss: 4.500142 Test Acc: 0.310000
Epoch: 12/20 Step: 104 Loss: 0.000082 Acc: 0.280000 Test Loss: 4.570614 Test Acc: 0.300000
Epoch: 13/20 Step: 112 Loss: 0.000070 Acc: 0.280000 Test Loss: 4.633649 Test Acc: 0.300000
Epoch: 14/20 Step: 120 Loss: 0.000049 Acc: 0.300000 Test Loss: 4.690726 Test Acc: 0.300000
Epoch: 15/20 Step: 128 Loss: 0.000029 Acc: 0.380000 Test Loss: 4.734747 Test Acc: 0.300000
Epoch: 16/20 Step: 136 Loss: 0.000046 Acc: 0.320000 Test Loss: 4.782884 Test Acc: 0.300000
Epoch: 17/20 Step: 144 Loss: 0.000037 Acc: 0.280000 Test Loss: 4.820999 Test Acc: 0.300000
Epoch: 18/20 Step: 152 Loss: 0.000024 Acc: 0.340000 Test Loss: 4.864908 Test Acc: 0.300000
Epoch: 19/20 Step: 160 Loss: 0.000042 Acc: 0.300000 Test Loss: 4.905285 Test Acc: 0.300000
Epoch: 20/20 Step: 168 Loss: 0.000058 Acc: 0.300000 Test Loss: 4.929110 Test Acc: 0.300000 # Evaluation
sentiment_net.eval()
random.seed(1234)
def get_key(val, my_dict):
for key, value in my_dict.items():
if val == value:
return key
review, label = random.choice(train_data)
review_sentence = [get_key(rev, vocab_to_int) for rev in review]
print("input sentence: ", list(filter(None, review_sentence)))
output = sentiment_net(review.unsqueeze(0)) # additional dimension for batch
sentiment = 'negative' if output <= 0.5 else 'positive'
print('sentiment:', sentiment)
print('============================================================================')
random.seed(7532)
review, label = random.choice(train_data)
review_sentence = [get_key(rev, vocab_to_int) for rev in review]
print("input sentence: ", list(filter(None, review_sentence)))
output = sentiment_net(review.unsqueeze(0)) # additional dimension for batch
sentiment = 'negative' if output <= 0.5 else 'positive'
print('sentiment:', sentiment)input sentence: ['i', 'admit', 'its', 'very', 'silly', 'but', 'ive', 'practically', 'memorized', 'the', 'damn', 'thing', 'it', 'holds', 'a', 'lot', 'of', 'good', 'childhood', 'memories', 'for', 'me', 'my', 'brother', 'and', 'i', 'saw', 'it', 'opening', 'day', 'and', 'i', 'have', 'respect', 'for', 'any', 'movie', 'with', 'fnm', 'on', 'the', 'soundtrack']
sentiment: positive
============================================================================
input sentence: ['this', 'piece', 'aint', 'really', 'worth', 'a', 'comment', 'its', 'simply', 'the', 'worst', 'horror', 'movie', 'i', 'have', 'ever', 'seen', 'the', 'actors', 'are', 'bad', 'as', 'bad', 'can', 'be', 'and', 'the', 'whole', 'plot', 'is', 'so', 'silly', 'it', 'nearly', 'made', 'me', 'cry', 'shame', 'on', 'you', 'i', 'say']
sentiment: negative2. Seq2seq model with attention
Here, you are going to translate French to English using seq2seq model.
- Encoder : GRU
- Decoder : GRU with attention
0) Preprocessing
from __future__ import unicode_literals, print_function, division
from io import open
import unicodedata
import string
import re
import random
import numpy as np
from nltk.translate.bleu_score import sentence_bleu
import torch
import torch.nn as nn
from torch import optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
plt.switch_backend('agg')
import matplotlib.ticker as ticker
%matplotlib inline
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
import warnings
warnings.filterwarnings('ignore')!wget https://www.dropbox.com/s/b4nrxhsbny99vel/eng-fra.txt--2023-03-23 05:10:43-- https://www.dropbox.com/s/b4nrxhsbny99vel/eng-fra.txt
Resolving www.dropbox.com (www.dropbox.com)... 162.125.7.18, 2620:100:6031:18::a27d:5112
Connecting to www.dropbox.com (www.dropbox.com)|162.125.7.18|:443... connected.
HTTP request sent, awaiting response... 302 Found
Location: /s/raw/b4nrxhsbny99vel/eng-fra.txt [following]
--2023-03-23 05:10:44-- https://www.dropbox.com/s/raw/b4nrxhsbny99vel/eng-fra.txt
Reusing existing connection to www.dropbox.com:443.
HTTP request sent, awaiting response... 302 Found
Location: https://uc479854c369aa8d0d3c6af241ee.dl.dropboxusercontent.com/cd/0/inline/B4zNgJXVfPgfwRUowo-bRqQdXcixRJwda0M13KRmPTMkgigxZsZF4jjJIm2WvkCAH6iRFZSvyv-zBJI07jriYj1m_wVNgc57Rxwq5PRyjNW0ZIWRfWIMGWlRUQ5do9qLeWG8xnzAPea2A0OWPgCKH8TmRjhrGfre1F6_H1gloEH86Q/file# [following]
--2023-03-23 05:10:45-- https://uc479854c369aa8d0d3c6af241ee.dl.dropboxusercontent.com/cd/0/inline/B4zNgJXVfPgfwRUowo-bRqQdXcixRJwda0M13KRmPTMkgigxZsZF4jjJIm2WvkCAH6iRFZSvyv-zBJI07jriYj1m_wVNgc57Rxwq5PRyjNW0ZIWRfWIMGWlRUQ5do9qLeWG8xnzAPea2A0OWPgCKH8TmRjhrGfre1F6_H1gloEH86Q/file
Resolving uc479854c369aa8d0d3c6af241ee.dl.dropboxusercontent.com (uc479854c369aa8d0d3c6af241ee.dl.dropboxusercontent.com)... 162.125.81.15, 2620:100:6031:15::a27d:510f
Connecting to uc479854c369aa8d0d3c6af241ee.dl.dropboxusercontent.com (uc479854c369aa8d0d3c6af241ee.dl.dropboxusercontent.com)|162.125.81.15|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 9541158 (9.1M) [text/plain]
Saving to: ‘eng-fra.txt’
eng-fra.txt 100%[===================>] 9.10M 11.7MB/s in 0.8s
2023-03-23 05:10:46 (11.7 MB/s) - ‘eng-fra.txt’ saved [9541158/9541158]We will need a unique index per word to use as the inputs and targets of the networks. To keep track of all this, we will use a class called Lang which has word -> index (word2index) and index -> word (index2word) dictionaries, as well as a count of each word word2count to use to later replace rare words.
SOS_token = 0
EOS_token = 1
# Since there is a lot of example sentences and we want to train quickly, we will trim the data set to only relatively short and simple sentences.
# Here, the maximum length is 10 words (including ending punctuation)
# We are filtering to sentences that translate to the form "I am" or "He is" etc
MAX_LENGTH = 10
eng_prefixes = (
"i am ", "i m ",
"he is", "he s ",
"she is", "she s ",
"you are", "you re ",
"we are", "we re ",
"they are", "they re "
)
# The full process for preparing the data is:
# - Read text file and split into lines, split lines into pairs
# - Normalize text, filter by length and content
# - Make word lists from sentences in pairs
# Tokenizer
# We’ll need a unique index per word to use as the inputs and targets of the networks later.
class Lang:
def __init__(self, name):
self.name = name
self.word2index = {}
self.word2count = {}
self.index2word = {0: "SOS", 1: "EOS"}
self.n_words = 2 # Count SOS and EOS
def addSentence(self, sentence):
for word in sentence.split(' '):
self.addWord(word)
def addWord(self, word):
if word not in self.word2index:
self.word2index[word] = self.n_words
self.word2count[word] = 1
self.index2word[self.n_words] = word
self.n_words += 1
else:
self.word2count[word] += 1
# Turn a Unicode string to plain ASCII
# The file are all in Unicode, to simplify we will turn Unicode characters to ASCII, make everything lowercase, and trim most punctuation.
# For more information, refer https://stackoverflow.com/a/518232/2809427
def unicodeToAscii(s):
return ''.join(
c for c in unicodedata.normalize('NFD', s)
if unicodedata.category(c) != 'Mn'
)
# Lowercase, trim, and remove non-letter characters
def normalizeString(s):
s = unicodeToAscii(s.lower().strip())
s = re.sub(r"([.!?])", r" \1", s)
s = re.sub(r"[^a-zA-Z.!?]+", r" ", s)
return s
# Read file
# If you want to translate Other Language -> English, then set `reverse = True`
def readLangs(lang1, lang2, reverse=False):
print("Reading lines...")
# Read the file and split into lines
lines = open('./%s-%s.txt' % (lang1, lang2), encoding='utf-8').\
read().strip().split('\n')
# Split every line into pairs and normalize
pairs = [[normalizeString(s) for s in l.split('\t')] for l in lines]
# Reverse pairs, make Lang instances
if reverse:
pairs = [list(reversed(p)) for p in pairs]
input_lang = Lang(lang2)
output_lang = Lang(lang1)
else:
input_lang = Lang(lang1)
output_lang = Lang(lang2)
return input_lang, output_lang, pairs
def filterPair(p):
return len(p[0].split(' ')) < MAX_LENGTH and \
len(p[1].split(' ')) < MAX_LENGTH and \
p[1].startswith(eng_prefixes)
def filterPairs(pairs):
return [pair for pair in pairs if filterPair(pair)]
def prepareData(lang1, lang2, reverse=False):
input_lang, output_lang, pairs = readLangs(lang1, lang2, reverse)
print("Read %s sentence pairs" % len(pairs))
pairs = filterPairs(pairs)
print("Trimmed to %s sentence pairs" % len(pairs))
print("Counting words...")
for pair in pairs:
input_lang.addSentence(pair[0])
output_lang.addSentence(pair[1])
print("Counted words:")
print(input_lang.name, input_lang.n_words)
print(output_lang.name, output_lang.n_words)
return input_lang, output_lang, pairs
input_lang, output_lang, pairs = prepareData('eng', 'fra', True)
print(random.choice(pairs))Reading lines...
Read 135842 sentence pairs
Trimmed to 10599 sentence pairs
Counting words...
Counted words:
fra 4345
eng 2803
['nous ne sommes pas a la maison .', 'we re not home .']1) Encoder
The encoder of a seq2seq network is a GRU that outpus some value for every word from the input sentence. For every input word, the encoder outputs a vector and a hidden state, and uses the hidden state for the next input word.
class Encoder(nn.Module):
def __init__(self, input_size, hidden_size):
super().__init__()
self.hidden_size = hidden_size
self.embedding = nn.Embedding(input_size, hidden_size)
self.gru = nn.GRU(hidden_size, hidden_size)
def forward(self, x, hidden):
embedded = self.embedding(x).view(1, 1, -1)
output, hidden = self.gru(embedded, hidden)
return output, hidden
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device = device)2) Decoder
The decoder is another GRU that takes the output vector(s) and outputs a sequence of words to create the translation. Here, we are going to use Attention Decoder
class AttnDecoder(nn.Module):
def __init__(self, hidden_size, output_size, max_length=MAX_LENGTH):
super().__init__()
self.hidden_size = hidden_size
self.output_size = output_size
self.max_length = max_length
self.embedding = nn.Embedding(self.output_size, self.hidden_size)
self.attn_matrix = nn.Parameter(data = torch.ones((self.hidden_size, self.hidden_size)), requires_grad = True)
self.gru = nn.GRU(self.hidden_size * 2, self.hidden_size)
self.out = nn.Linear(self.hidden_size, self.output_size)
def forward(self, x, hidden, encoder_outputs):
embedded = self.embedding(x).view(1, 1, -1)
attn_weights = torch.matmul(torch.matmul(hidden[0], self.attn_matrix), torch.transpose(encoder_outputs, 0, 1)) # 1 x max_length
attn_weights = F.softmax(attn_weights, dim=1)
attn_applied = torch.bmm(attn_weights.unsqueeze(1), encoder_outputs.view(1, -1, self.hidden_size))
input_gru = torch.cat((attn_applied[0], embedded[0]), dim=1)
output, hidden = self.gru(input_gru.unsqueeze(0), hidden)
output = F.log_softmax(self.out(output[0]), dim=1)
return output, hidden, attn_weights
def initHidden(self):
return torch.zeros(1, 1, self.hidden_size, device=device)3) Train our model
Preparing Training Data
To train, for each pair we will need an input tensor (indexes of the words in the input sentence) and target tensor (indexes of the words in the target sentence). While creating these vectors we will append the EOS token to both sequences.
def indexesFromSentence(lang, sentence):
return [lang.word2index[word] for word in sentence.split(' ')]
def tensorFromSentence(lang, sentence):
indexes = indexesFromSentence(lang, sentence)
indexes.append(EOS_token)
return torch.tensor(indexes, dtype=torch.long, device=device).view(-1, 1)
def tensorsFromPair(pair):
input_tensor = tensorFromSentence(input_lang, pair[0])
target_tensor = tensorFromSentence(output_lang, pair[1])
return (input_tensor, target_tensor)BLEU(Bilingual Evaluation Understudy) score
Like FID score you are already familiar with, there is a metric called BLEU score in NLP. BLEU score is a metric for evaluating a generated sentence to a reference sentence.
BLEU score is calculated by comparing n-gram matches between each candidate translation to the reference translations. BLEU compares the n-gram of the candidate translation with n-gram of the reference translation to count the number of matches. These matches are independent of the positions where they occur.
NLTK provides the sentence_bleu() function for evaluating a candidate sentence against one or more reference sentences.
For more information about BLEU, please refer these websites:
https://donghwa-kim.github.io/BLEU.html
https://wikidocs.net/31695
https://machinelearningmastery.com/calculate-bleu-score-for-text-python/
https://towardsdatascience.com/bleu-bilingual-evaluation-understudy-2b4eab9bcfd1
hidden_size = 256
teacher_forcing_ratio = 0.5
max_length = MAX_LENGTH
n_iters = 75000 # Change this! (maybe 75000?)
n_epochs = 4
lr = 0.001
encoder = Encoder(input_size = input_lang.n_words, hidden_size = hidden_size).to(device)
decoder = AttnDecoder(hidden_size=hidden_size, output_size=output_lang.n_words, max_length=max_length).to(device)
encoder_opt = optim.Adam(encoder.parameters(), lr=lr)
decoder_opt = optim.Adam(decoder.parameters(), lr=lr)
criterion = nn.NLLLoss()
training_pairs = [tensorsFromPair(random.choice(pairs)) for i in range(n_iters)]
for epoch in range(n_epochs + 1):
for iter in range(1, n_iters + 1):
training_pair = training_pairs[iter - 1]
input_tensor = training_pair[0]
target_tensor = training_pair[1]
# Encoder
encoder_hidden = encoder.initHidden()
input_length = input_tensor.size(0)
target_length = target_tensor.size(0)
encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
loss = 0
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)
encoder_outputs[ei] = encoder_output[0, 0]
# Decoder
output_sentence = [output_lang.index2word[t.item()] for t in target_tensor]
decoder_input = torch.tensor([[SOS_token]], device=device)
decoder_hidden = encoder_hidden
# Teacher forcing: Feed the target as the next input
use_teacher_forcing = True if random.random() < teacher_forcing_ratio else False
decoded_sentence = []
if use_teacher_forcing:
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
topv, topi = decoder_output.topk(1)
decoded_sentence.append(output_lang.index2word[topi.item()])
loss += criterion(decoder_output, target_tensor[di])
decoder_input = target_tensor[di] # Teacher forcing
else:
for di in range(target_length):
decoder_output, decoder_hidden, decoder_attention = decoder(decoder_input, decoder_hidden, encoder_outputs)
topv, topi = decoder_output.topk(1) # value, indice
decoded_sentence.append(output_lang.index2word[topi.item()])
decoder_input = topi.squeeze().detach() # detach from history as input
loss += criterion(decoder_output, target_tensor[di])
if decoder_input.item() == EOS_token:
break
encoder_opt.zero_grad()
decoder_opt.zero_grad()
loss.backward()
encoder_opt.step()
decoder_opt.step()
# BLEU score
bleu_score = sentence_bleu([output_sentence[:-1]], decoded_sentence[:-1])
if iter % 5000 == 0:
print('epoch: {}, iter: {}, loss: {:.6f}, bleu_score: {:.6f}'.format(epoch, iter, loss.item() / target_length, bleu_score))epoch: 0, iter: 5000, loss: 1.821757, bleu_score: 0.000000
epoch: 0, iter: 10000, loss: 1.692030, bleu_score: 0.000000
epoch: 0, iter: 15000, loss: 2.845087, bleu_score: 0.000000
epoch: 0, iter: 20000, loss: 3.225454, bleu_score: 0.000000
epoch: 0, iter: 25000, loss: 2.780622, bleu_score: 0.000000
epoch: 0, iter: 30000, loss: 1.886840, bleu_score: 0.000000
epoch: 0, iter: 35000, loss: 4.271745, bleu_score: 0.000000
epoch: 0, iter: 40000, loss: 0.835489, bleu_score: 0.612975
epoch: 0, iter: 45000, loss: 0.992656, bleu_score: 0.000000
epoch: 0, iter: 50000, loss: 0.504560, bleu_score: 0.000000
epoch: 0, iter: 55000, loss: 3.065766, bleu_score: 0.000000
epoch: 0, iter: 60000, loss: 3.125405, bleu_score: 0.000000
epoch: 0, iter: 65000, loss: 1.571585, bleu_score: 0.000000
epoch: 0, iter: 70000, loss: 1.231350, bleu_score: 0.431670
epoch: 0, iter: 75000, loss: 0.150940, bleu_score: 1.000000
epoch: 1, iter: 5000, loss: 0.169276, bleu_score: 1.000000
epoch: 1, iter: 10000, loss: 0.365926, bleu_score: 0.000000
epoch: 1, iter: 15000, loss: 0.571658, bleu_score: 0.431670
epoch: 1, iter: 20000, loss: 4.475314, bleu_score: 0.000000
epoch: 1, iter: 25000, loss: 0.781832, bleu_score: 0.000000
epoch: 1, iter: 30000, loss: 0.732695, bleu_score: 0.000000
epoch: 1, iter: 35000, loss: 4.505051, bleu_score: 0.000000
epoch: 1, iter: 40000, loss: 0.193568, bleu_score: 1.000000
epoch: 1, iter: 45000, loss: 0.192233, bleu_score: 1.000000
epoch: 1, iter: 50000, loss: 0.128053, bleu_score: 1.000000
epoch: 1, iter: 55000, loss: 1.581624, bleu_score: 0.000000
epoch: 1, iter: 60000, loss: 4.188882, bleu_score: 0.000000
epoch: 1, iter: 65000, loss: 0.434905, bleu_score: 0.000000
epoch: 1, iter: 70000, loss: 0.842955, bleu_score: 0.330316
epoch: 1, iter: 75000, loss: 0.076965, bleu_score: 1.000000
epoch: 2, iter: 5000, loss: 0.106611, bleu_score: 1.000000
epoch: 2, iter: 10000, loss: 0.395491, bleu_score: 0.000000
epoch: 2, iter: 15000, loss: 0.188229, bleu_score: 0.660633
epoch: 2, iter: 20000, loss: 1.577839, bleu_score: 0.330316
epoch: 2, iter: 25000, loss: 0.120699, bleu_score: 1.000000
epoch: 2, iter: 30000, loss: 0.380407, bleu_score: 0.000000
epoch: 2, iter: 35000, loss: 3.869645, bleu_score: 0.000000
epoch: 2, iter: 40000, loss: 0.213595, bleu_score: 1.000000
epoch: 2, iter: 45000, loss: 0.334616, bleu_score: 1.000000
epoch: 2, iter: 50000, loss: 0.646786, bleu_score: 0.000000
epoch: 2, iter: 55000, loss: 1.592758, bleu_score: 0.000000
epoch: 2, iter: 60000, loss: 2.219345, bleu_score: 0.000000
epoch: 2, iter: 65000, loss: 0.733926, bleu_score: 0.000000
epoch: 2, iter: 70000, loss: 2.928756, bleu_score: 0.377079
epoch: 2, iter: 75000, loss: 0.113679, bleu_score: 1.000000
epoch: 3, iter: 5000, loss: 0.035027, bleu_score: 1.000000
epoch: 3, iter: 10000, loss: 0.261213, bleu_score: 0.000000
epoch: 3, iter: 15000, loss: 1.265551, bleu_score: 0.610195
epoch: 3, iter: 20000, loss: 1.268290, bleu_score: 0.485492
epoch: 3, iter: 25000, loss: 0.122621, bleu_score: 1.000000
epoch: 3, iter: 30000, loss: 0.164578, bleu_score: 1.000000
epoch: 3, iter: 35000, loss: 3.336488, bleu_score: 0.000000
epoch: 3, iter: 40000, loss: 0.079437, bleu_score: 1.000000
epoch: 3, iter: 45000, loss: 0.245700, bleu_score: 1.000000
epoch: 3, iter: 50000, loss: 0.310768, bleu_score: 0.000000
epoch: 3, iter: 55000, loss: 1.558132, bleu_score: 0.000000
epoch: 3, iter: 60000, loss: 1.114159, bleu_score: 0.000000
epoch: 3, iter: 65000, loss: 0.145097, bleu_score: 1.000000
epoch: 3, iter: 70000, loss: 1.147664, bleu_score: 0.354948
epoch: 3, iter: 75000, loss: 0.171217, bleu_score: 1.000000
epoch: 4, iter: 5000, loss: 0.050408, bleu_score: 1.000000
epoch: 4, iter: 10000, loss: 0.058864, bleu_score: 1.000000
epoch: 4, iter: 15000, loss: 0.194019, bleu_score: 0.660633
epoch: 4, iter: 20000, loss: 4.035614, bleu_score: 0.000000
epoch: 4, iter: 25000, loss: 0.373141, bleu_score: 0.000000
epoch: 4, iter: 30000, loss: 0.067279, bleu_score: 1.000000
epoch: 4, iter: 35000, loss: 4.816620, bleu_score: 0.000000
epoch: 4, iter: 40000, loss: 0.173593, bleu_score: 1.000000
epoch: 4, iter: 45000, loss: 0.106525, bleu_score: 1.000000
epoch: 4, iter: 50000, loss: 0.138610, bleu_score: 1.000000
epoch: 4, iter: 55000, loss: 1.495542, bleu_score: 0.000000
epoch: 4, iter: 60000, loss: 1.353909, bleu_score: 0.000000
epoch: 4, iter: 65000, loss: 0.059054, bleu_score: 1.000000
epoch: 4, iter: 70000, loss: 0.526115, bleu_score: 0.368894
epoch: 4, iter: 75000, loss: 0.075167, bleu_score: 1.0000004) Evaluate our model
Evaluation is mostly the same as training, but there are no targets so we simply feed the decoder's predictions back to itself for each step (NO TEACHER FORCING) Every time it predicts a word we add it to the output string, and if it predicts the EOS token we stop there. We also store the decoder’s attention outputs for display later.
## > input sentence
## = true output sentence
## < predicted sentence
def evaluate(encoder, decoder, sentence, output_sentence, max_length=max_length):
with torch.no_grad():
input_tensor = tensorFromSentence(input_lang, sentence)
input_length = input_tensor.size()[0]
output_tensor = tensorFromSentence(output_lang, output_sentence)
output_sent = [output_lang.index2word[t.item()] for t in output_tensor]
encoder_hidden = encoder.initHidden()
encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)
encoder_outputs[ei] += encoder_output[0, 0]
decoder_input = torch.tensor([[SOS_token]], device=device) # SOS
decoder_hidden = encoder_hidden
decoded_words = []
decoder_attentions = torch.zeros(max_length, max_length)
for di in range(max_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
decoder_attentions[di] = decoder_attention.data
topv, topi = decoder_output.data.topk(1)
if topi.item() == EOS_token:
decoded_words.append('<EOS>')
break
else:
decoded_words.append(output_lang.index2word[topi.item()])
decoder_input = topi.squeeze().detach()
bleu_score = sentence_bleu([output_sent[:-1]], decoded_words[:-1])
return decoded_words, decoder_attentions[:di + 1], bleu_score
def evaluateRandomly(encoder, decoder, n=10):
for i in range(n):
pair = random.choice(pairs)
print('>', pair[0])
print('=', pair[1])
output_words, attentions, bleu_score = evaluate(encoder, decoder, pair[0], pair[1])
output_sentence = ' '.join(output_words)
print('<', output_sentence)
print('BLEU score: {:.6f}'.format(bleu_score))
print('')
evaluateRandomly(encoder, decoder)> nous allons faire tout ce que nous pouvons .
= we re going to do everything we can .
< we re going to do everything we can . <EOS>
BLEU score: 1.000000
> tu es un merveilleux ami .
= you re a wonderful friend .
< you re wonderful friend . <EOS>
BLEU score: 0.000000
> vous etes un de ces pauvres types !
= you re such a jerk .
< you re such a jerk . <EOS>
BLEU score: 1.000000
> tu gagnes n est ce pas ?
= you re winning aren t you ?
< you re winning aren t you ? <EOS>
BLEU score: 1.000000
> je ne suis pas un professionnel .
= i m not a professional .
< i m not a professional . <EOS>
BLEU score: 1.000000
> vous n etes pas en forme .
= you re not fit .
< you re not fit . <EOS>
BLEU score: 1.000000
> nous sommes tous nes fous .
= we are all born mad .
< we re all crazy . <EOS>
BLEU score: 0.000000
> je fais la diete .
= i m fasting .
< i m fasting . <EOS>
BLEU score: 1.000000
> vous etes la meilleure .
= you re the best .
< you re the greatest . <EOS>
BLEU score: 0.000000
> je suis toujours contente .
= i m always happy .
< i m always happy . <EOS>
BLEU score: 1.000000def evaluate_attn(encoder, decoder, sentence, max_length=max_length):
with torch.no_grad():
input_tensor = tensorFromSentence(input_lang, sentence)
input_sentence = [input_lang.index2word[t.item()] for t in input_tensor]
input_length = input_tensor.size()[0]
encoder_hidden = encoder.initHidden()
encoder_outputs = torch.zeros(max_length, encoder.hidden_size, device=device)
for ei in range(input_length):
encoder_output, encoder_hidden = encoder(input_tensor[ei], encoder_hidden)
encoder_outputs[ei] += encoder_output[0, 0]
decoder_input = torch.tensor([[SOS_token]], device=device) # SOS
decoder_hidden = encoder_hidden
decoded_words = []
decoder_attentions = torch.zeros(max_length, max_length)
for di in range(max_length):
decoder_output, decoder_hidden, decoder_attention = decoder(
decoder_input, decoder_hidden, encoder_outputs)
decoder_attentions[di] = decoder_attention.data
topv, topi = decoder_output.data.topk(1)
if topi.item() == EOS_token:
decoded_words.append('<EOS>')
break
else:
decoded_words.append(output_lang.index2word[topi.item()])
decoder_input = topi.squeeze().detach()
return decoded_words, decoder_attentions[:di + 1]
output_words, attentions = evaluate_attn(encoder, decoder, "tu as parfaitement raison .")
print(output_words)
plt.matshow(attentions.numpy())
plt.show()['you', 're', 'right', '.', '<EOS>']
def showAttention(input_sentence, output_words, attentions):
# Set up figure with colorbar
fig = plt.figure()
ax = fig.add_subplot(111)
cax = ax.matshow(attentions.numpy(), cmap='bone')
fig.colorbar(cax)
# Set up axes
ax.set_xticklabels([''] + input_sentence.split(' ') +
['<EOS>'], rotation=90)
ax.set_yticklabels([''] + output_words)
# Show label at every tick
ax.xaxis.set_major_locator(ticker.MultipleLocator(1))
ax.yaxis.set_major_locator(ticker.MultipleLocator(1))
plt.show()
def evaluateAndShowAttention(input_sentence):
output_words, attentions = evaluate_attn(
encoder, decoder, input_sentence)
print('input =', input_sentence)
print('output =', ' '.join(output_words))
showAttention(input_sentence, output_words, attentions)
evaluateAndShowAttention("elle a cinq ans de moins que moi .")
print()
evaluateAndShowAttention("elle est trop petit .")
print()
evaluateAndShowAttention("je ne crains pas de mourir .")
print()
evaluateAndShowAttention("c est un jeune directeur plein de talent .")
input = elle a cinq ans de moins que moi .
output = she is five years younger than me . <EOS>
input = elle est trop petit .
output = she is too young . <EOS>
input = je ne crains pas de mourir .
output = i m not scared of dying . <EOS>
input = c est un jeune directeur plein de talent .
output = he s a talented young . <EOS>
If you train your model more changing hyper parameters, you will get a decent result like below.
Reference
- AI504: Programming for AI Lecture at KAIST AI
AI researcher