Now that we've established our LSTM intuition and built the model, lets train it!

EARLY_STOPPING, CURRENT_CHECKPOINT, CURRENT_ACC, EPOCH = 5, 0, 0, 0
while True:
    lasttime = time.time()
    if CURRENT_CHECKPOINT == EARLY_STOPPING:
        print('break epoch:%d
'%(EPOCH))
        break
        
    train_acc, train_loss, test_acc, test_loss = 0, 0, 0, 0
    for i in range(0, (len(train_X) // batch_size) * batch_size, batch_size):
        batch_x = str_idx(train_X[i:i+batch_size],dictionary,maxlen)
        acc, loss, _ = sess.run([model.accuracy, model.cost, model.optimizer], 
                           feed_dict = {model.X : batch_x, model.Y : train_onehot[i:i+batch_size]})
        train_loss += loss
        train_acc += acc
    
    for i in range(0, (len(test_X) // batch_size) * batch_size, batch_size):
        batch_x = str_idx(test_X[i:i+batch_size],dictionary,maxlen)
        acc, loss = sess.run([model.accuracy, model.cost], 
                           feed_dict = {model.X : batch_x, model.Y : train_onehot[i:i+batch_size]})
        test_loss += loss
        test_acc += acc
    
    train_loss /= (len(train_X) // batch_size)
    train_acc /= (len(train_X) // batch_size)
    test_loss /= (len(test_X) // batch_size)
    test_acc /= (len(test_X) // batch_size)
    
    if test_acc > CURRENT_ACC:
        print('epoch: %d, pass acc: %f, current acc: %f'%(EPOCH,CURRENT_ACC, test_acc))
        CURRENT_ACC = test_acc
        CURRENT_CHECKPOINT = 0
    else:
        CURRENT_CHECKPOINT += 1
        
    print('time taken:', time.time()-lasttime)
    print('epoch: %d, training loss: %f, training acc: %f, valid loss: %f, valid acc: %f
'%(EPOCH,train_loss,
                                                                                          train_acc,test_loss,
                                                                                          test_acc))
    EPOCH += 1

While the LSTM model is getting trained, we can see the logs of each epoch as shown below :

OUTPUT:
('time taken:', 18.061596155166626)
epoch: 10, training loss: 0.015714, training acc: 0.994910, valid loss: 4.252270, valid acc: 0.500000

('time taken:', 17.786305904388428)
epoch: 11, training loss: 0.011198, training acc: 0.995975, valid loss: 4.644272, valid acc: 0.502441

('time taken:', 19.031064987182617)
epoch: 12, training loss: 0.009245, training acc: 0.996686, valid loss: 4.575824, valid acc: 0.499512

('time taken:', 16.996762990951538)
epoch: 13, training loss: 0.006528, training acc: 0.997751, valid loss: 4.449901, valid acc: 0.501953

('time taken:', 17.008245944976807)
epoch: 14, training loss: 0.011770, training acc: 0.995739, valid loss: 4.282045, valid acc: 0.499023

break epoch:15

You will notice that even after using the same configurations of the model, the training time required for the LSTM based model will be more.