import pandas as pd
#text label
train = pd.read_csv(“train.csv”, sep=’,’)
np.random.seed(123)
torch.manual_seed(123)
torch.cuda.manual_seed(123)
torch.backends.cudnn.deterministic = True
from keras.preprocessing import text, sequence
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
create tokens
tokenizer = Tokenizer(num_words = 1000)
tokenizer.fit_on_texts(x_train)
word_index = tokenizer.word_index
convert texts to padded sequences
x_train = tokenizer.texts_to_sequences(x_train)
x_train = pad_sequences(x_train, maxlen = 70)
!wget http://nlp.stanford.edu/data/glove.840B.300d.zip
!unzip glove*.zip
EMBEDDING_FILE = ‘glove.840B.300d.txt’
embeddings_index = {}
for i, line in enumerate(open(EMBEDDING_FILE)):
val = line.split()
embeddings_index[val[0]] = np.asarray(val[1:], dtype=‘float32’)
embeddings_index = {}
f = open(r’glove.840B.300d.txt’, encoding=‘utf8’)
for line in f:
values = line.split()
word = ‘’.join(values[:-300])
coefs = np.asarray(values[-300:], dtype=‘float32’)
embeddings_index[word] = coefs
f.close()
embedding_matrix = np.zeros((len(word_index) + 1, 300))
for word, i in word_index.items():
embedding_vector = embeddings_index.get(word)
if embedding_vector is not None:
embedding_matrix[i] = embedding_vector
import torch.nn as nn
import torch.nn.functional as F
max_features = 1000
embed_size = 64
class Model(nn.Module):
def init(self):
super(Model, self).init()
## Embedding Layer, Add parameter
self.embedding = nn.Embedding(max_features, embed_size)
et = torch.tensor(embedding_matrix, dtype=torch.float32)
self.embedding.weight = nn.Parameter(et)
self.embedding.weight.requires_grad = False
self.embedding_dropout = nn.Dropout2d(0.1)
self.lstm = nn.LSTM(300, 40)
self.linear = nn.Linear(40, 16)
self.out = nn.Linear(16, 1)
self.relu = nn.ReLU()
def forward(self, x):
h_embedding = self.embedding(x)
h_lstm, _ = self.lstm(h_embedding)
max_pool, _ = torch.max(h_lstm, 1)
linear = self.relu(self.linear(max_pool))
out = self.out(linear)
return out
model = Model()
from torch.utils.data import DataLoader
from torch import optim
## Create training and validation sets:
from torch.utils.data import TensorDataset
## create training and validation split
split_size = int(0.8 * len(train))
index_list = list(range(len(train)))
train_idx, valid_idx = index_list[:split_size], index_list[split_size:]
## create iterator objects for train and valid datasets
x_tr = torch.tensor(x_train[train_idx], dtype=torch.long)
y_tr = torch.tensor(y_train[train_idx], dtype=torch.float32)
train = TensorDataset(x_tr, y_tr)
trainloader = DataLoader(train, batch_size=128)
x_val = torch.tensor(x_train[valid_idx], dtype=torch.long)
y_val = torch.tensor(y_train[valid_idx], dtype=torch.float32)
valid = TensorDataset(x_val, y_val)
validloader = DataLoader(valid, batch_size=128)
#Define loss and optimizers:
loss_function = nn.BCEWithLogitsLoss(reduction='mean')
optimizer = optim.Adam(model.parameters())
#Training the model:
## run for 50 Epochs
for epoch in range(1, 51):
train_loss, valid_loss = [], []
training part
model.train()
for data, target in trainloader:
optimizer.zero_grad()
output = model(data)
loss = loss_function(output, target.view(-1,1))
loss.backward()
optimizer.step()
train_loss.append(loss.item())
evaluation part
model.eval()
for data, target in validloader:
output = model(data)
loss = loss_function(output, target.view(-1,1))
valid_loss.append(loss.item())
print ("Epoch:", epoch, "Training Loss: ", np.mean(train_loss), "Valid Loss: ", np.mean(valid_loss))
#Finally, we can obtain the predictions:
dataiter = iter(validloader)
data, labels = dataiter.next()
output = model(data)
_, preds_tensor = torch.max(output, 1)
preds = np.squeeze(preds_tensor.numpy())
## Actual vs Pred
labels[:20], preds[:20]
** I am getting all predictions as zero. How to resolve the same **