Class Dataset
from sklearn.model_selection import train_test_split
import numpy as np
class dataset(Dataset):
def __init__(self):
self.tf=TfidfVectorizer(max_df=0.99, min_df=0.005)
self.x=self.tf.fit_transform(corpus).toarray()
self.y=list(df.review)
self.x_train,self.x_test,self.y_train,self.y_test=train_test_split(self.x,self.y,test_size=0.2)
self.token2idx=self.tf.vocabulary_
self.idx2token = {idx: token for token, idx in self.token2idx.items()}
print(self.idx2token)
def __getitem__(self,i):
return self.x_train[i, :], self.y_train[i]
def __len__(self):
return self.x_train.shape[0]
Classifier Class
class classifier(nn.Module):
def __init__(self,vocab_size,hidden1,hidden2):
super(classifier,self).__init__()
self.fc1=nn.Linear(vocab_size,hidden1)
self.fc2=nn.Linear(hidden1,hidden2)
self.fc3=nn.Linear(hidden2,1)
def forward(self,inputs):
x=F.relu(self.fc1(inputs.squeeze(1).float()))
x=F.relu(self.fc2(x))
return self.fc3
Training Loop
epochs=10
total=0
model.train()
for epoch in tqdm(range(epochs)):
progress_bar=tqdm_notebook(train_loader,leave=False)
losses=[]
correct=0
for inputs,target in progress_bar:
model.zero_grad()
output=model(inputs)
loss=criterion(output.squeeze(),target.float())
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), 3)
optim.step()
correct += (output == target).float().sum()
progress_bar.set_description(f'Loss: {loss.item():.3f}')
losses.append(loss.item())
total += 1
epoch_loss = sum(losses) / total
train_losses.append(epoch_loss)
tqdm.write(f'Epoch #{epoch + 1}\tTrain Loss: {epoch_loss:.3f}\tAccuracy: {correct/output.shape[0]}')
Error
AttributeError Traceback (most recent call last)
<ipython-input-65-f590a86ab4d3> in <module>
12 model.zero_grad()
13 output=model(inputs)
---> 14 loss=criterion(output.squeeze(),target.float())
15 loss.backward()
16 nn.utils.clip_grad_norm_(model.parameters(), 3)
~\Anaconda3\lib\site-packages\torch\nn\modules\module.py in __getattr__(self, name)
592 return modules[name]
593 raise AttributeError("'{}' object has no attribute '{}'".format(
--> 594 type(self).__name__, name))
595
596 def __setattr__(self, name, value):
AttributeError: 'Linear' object has no attribute 'squeeze'
