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updated_sentiment_analysis.py
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updated_sentiment_analysis.py
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import torch
from torchtext import data
from torchtext import datasets
import random
SEED = 1234
torch.manual_seed(SEED)
torch.cuda.manual_seed(SEED)
torch.backends.cudnn.deterministic = True
TEXT = data.Field(tokenize='spacy')
LABEL = data.LabelField(dtype=torch.float)
train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)
train_data, valid_data = train_data.split(random_state=random.seed(SEED))
TEXT.build_vocab(train_data, max_size=25000, vectors='glove.6B.100d')
LABEL.build_vocab(train_data)
BATCH_SIZE = 64
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(
(train_data, valid_data, test_data),
batch_size = BATCH_SIZE,
device=device)
import torch.nn as nn
class RNN(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout):
super().__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.rnn = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, bidirectional=bidirectional, dropout=dropout)
self.fc = nn.Linear(hidden_dim*2, output_dim)
self.dropout = nn.Dropout(dropout)
def forward(self, x):
embedded = self.dropout(self.embeeding(x))
output, (hidden, cell) = self.rnn(embedded)
hidden = self.dropout(torch.cat((hiddne[-2,:,:], hidden[-1,:,:]), dim=1))
return self.fc(hidden.squeeze(0))
INPUT_DIM = len(TEXT.vocab)
EMBEDDING_DIM = 100
HIDDEN_DIM = 256
OUTPUT_DIM = 1
N_LAYERS = 2
BIDIRECTIONAL = True
DROPOUT = 0.5
model = RNN(INPUT_DIM, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS, BIDIRECTIONAL, DROPOUT)
pretrained_embeddings = TEXT.vocab.vectors
print(pretrained_embeddings.shape)
model.embedding.weight.data.copy_(pretrained_embeddings)
import torch.optim as optim
optimizer = optim.Adam(model.parameters())
criterion = nn.BCEWithLogitsLoss()
model = model.to(device)
criterion = criterion.to(device)
def binary_accuracy(preds, y):
#round predictions to the closest integer
rounded_preds = torch.round(torch.sigmoid(preds))
correct = (rounded_preds == y).float() #convert into float for division
acc = correct.sum()/len(correct)
return acc
def train(model, iterator, optimizer, criterion):
epoch_loss = 0
epoch_acc = 0
model.train()
for batch in iterator:
optimizer.zero_grad()
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
acc = binary_accuracy(predictions, batch.label)
loss.backward()
optimizer.step()
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
def evaluate(model, iterator, criterion):
epoch_loss = 0
epoch_acc = 0
model.eval()
with torch.no_grad():
for batch in iterator:
predictions = model(batch.text).squeeze(1)
loss = criterion(predictions, batch.label)
acc = binary_accuracy(predictions, batch.label)
epoch_loss += loss.item()
epoch_acc += acc.item()
return epoch_loss / len(iterator), epoch_acc / len(iterator)
N_EPOCHS = 5
for epoch in range(N_EPOCHS):
train_loss, train_acc = train(model, train_iterator, optimizer, criterion)
valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)
print(f'| Epoch: {epoch+1:02} | Train Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}% | Val. Loss: {valid_loss:.3f} | Val. Acc: {valid_acc*100:.2f}% |')
test_loss, test_acc = evaluate(model, test_iterator, criterion)
print(f'| Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}% |')
import spacy
nlp = spacy.load('en')
def predict_sentiment(sentence):
tokenized = [tok.text for tok in nlp.tokenizer(sentence)]
indexed = [TEXT.vocab.stoi[t] for t in tokenized]
tensor = torch.LongTensor(indexed).to(device)
tensor = tensor.unsqueeze(1)
prediction = torch.sigmoid(model(tensor))
return prediction.item()
predict_sentiment("This film is terrible")
predict_sentiment("This film is great")