ZeroDivisionError

[JamesTV1996]

Trying your method for a different dataset and I am getting a ZeroDivisionError for the Training and Validation Section. I assume that something is not loading properly because there should be no zero values.
Here is the code:

`import pickle
import random

with tf.Session() as sess: # Start Tensorflow Session
display_step = 100
patience = 5

 load = input("\nLoad checkpoint? y/n: ")
 print("")
 saver = tf.train.Saver()

 if load.lower() == 'y':

     print('Loading pre-trained weights for the model...')

     saver.restore(sess, r'C:\Users\james\Desktop\Title Generation - SENG 6245\Dataset250K.csv')
     sess.run(tf.global_variables())
     sess.run(tf.tables_initializer())

     with open(r'C:\Users\james\Desktop\Title Generation - SENG 6245\Dataset250K.csv', 'rb') as fp:
         train_data = pickle.load(fp)

     covered_epochs = train_data['covered_epochs']
     best_loss = train_data['best_loss']
     impatience = 0
    
     print('\nRESTORATION COMPLETE\n')

 else:
     best_loss = 2**30
     impatience = 0
     covered_epochs = 0

     init = tf.global_variables_initializer()
     sess.run(init)
     sess.run(tf.tables_initializer())

 epoch=0
 while (epoch+covered_epochs)<epochs:
    
     print("\n\nSTARTING TRAINING\n\n")
    
     batches_indices = [i for i in range(0, len(train_batches_text))]
     random.shuffle(batches_indices)

     total_train_acc = 0
     total_train_loss = 0

     for i in range(0, len(train_batches_text)):
        
         j = int(batches_indices[i])

         cost,prediction,\
             acc, _ = sess.run([cross_entropy,
                                outputs,
                                accuracy,
                                train_op],
                               feed_dict={tf_text: train_batches_text[j],
                                          tf_embd: embd,
                                          tf_summary: train_batches_summary[j],
                                          tf_true_summary_len: train_batches_true_summary_len[j],
                                          tf_train: True})
        
         total_train_acc += acc
         total_train_loss += cost

         if i % display_step == 0:
             print("Iter "+str(i)+", Cost= " +
                   "{:.3f}".format(cost)+", Acc = " +
                   "{:.2f}%".format(acc*100))
        
         if i % 500 == 0:
            
             idx = random.randint(0,len(train_batches_text[j])-1)
            
            
            
             text = " ".join([idx2vocab.get(vec,"<UNK>") for vec in train_batches_text[j][idx]])
             predicted_summary = [idx2vocab.get(vec,"<UNK>") for vec in prediction[idx]]
             actual_summary = [idx2vocab.get(vec,"<UNK>") for vec in train_batches_summary[j][idx]]
             
             print("\nSample Text\n")
             print(text)
             print("\nSample Predicted Summary\n")
             for word in predicted_summary:
                 if word == '<EOS>':
                     break
                 else:
                     print(word,end=" ")
             print("\n\nSample Actual Summary\n")
             for word in actual_summary:
                 if word == '<EOS>':
                     break
                 else:
                     print(word,end=" ")
             print("\n\n")
            
     print("\n\nSTARTING VALIDATION\n\n")
            
     total_val_loss=0
     total_val_acc=0
            
     for i in range(0, len(val_batches_text)):
        
         if i%100==0:
             print("Validating data # {}".format(i))

         cost, prediction,\
             acc = sess.run([cross_entropy,
                             outputs,
                             accuracy],
                               feed_dict={tf_text: val_batches_text[i],
                                          tf_embd: embd,
                                          tf_summary: val_batches_summary[i],
                                          tf_true_summary_len: val_batches_true_summary_len[i],
                                          tf_train: False})
         
         total_val_loss += cost
         total_val_acc += acc
    
    #Issue Starts Here  
     try:
         avg_val_loss = total_val_loss/len(val_batches_text) 
     except ZeroDivisionError: 
         avg_val_loss = 0
    
     print("\n\nEpoch: {}\n\n".format(epoch+covered_epochs))
     print("Average Training Loss: {:.3f}".format(total_train_loss/len(train_batches_text)))
     print("Average Training Accuracy: {:.2f}".format(100*total_train_acc/len(train_batches_text)))
     print("Average Validation Loss: {:.3f}".format(avg_val_loss))
     print("Average Validation Accuracy: {:.2f}".format(100*total_val_acc/len(val_batches_text)))
           
     if (avg_val_loss < best_loss):
         best_loss = avg_val_loss
         save_data={'best_loss':best_loss,'covered_epochs':covered_epochs+epoch+1}
         impatience=0
         with open('Model_Backup/Seq2seq_summarization.pkl', 'wb') as fp:
             pickle.dump(save_data, fp)
         saver.save(sess, 'Model_Backup/Seq2seq_summarization.ckpt')
         print("\nModel saved\n")
          
     else:
         impatience+=1
          
     if impatience > patience:
           break
          
          
     epoch+=1`

I can get rid of the error with exception handling but I was wondering if you had and idea of why it's not working in the first place.

[JRC1995]

I haven't touched Tensorflow in a while. But wouldn't this be for loading a checkpoint?

saver.restore(sess, r'C:\Users\james\Desktop\Title Generation - SENG 6245\Dataset250K.csv')

It doesn't seem like normal Tensorflow checkpoint.

for i in range(0, len(val_batches_text)):

This is the first mention of val_batches_text that I found in the code.
The issue seems to be that val_batches_text is completely empty that which your validation dataset is empty. Which would mean that the issue is probably somewhere outside this code snippet wherever you are preparing this val_batches_text.

Where are you loading the datasets btw?

[JamesTV1996]

I loaded a dataset that I cleaned from my own pc. I first ran my dataset through the preprocessing program you have posted as well. For my project, I was trying to adapt your implementation so that I can generate a title based off the description of the given problem. I assume this would work for this application. The dataset I used was originally downloaded from the StackOverflow dataset found on Google Cloud API.

[JRC1995]

It should work, but the main error seems to be that len(val_batches_text) is 0. That means the source of the bug is in the code snippet where val_batches_text is being created. For some reason no data is being loaded.
If you are using my pre-processing, it has some heavy filters. Is it possible that no data is passing through the filters when you are you preprocessing your dataset?

[vasanthaganesh]

I've tried what you said in the last comment but the data set is not loading and it return the error as the zero division.
We are using your pre-processing, so please help me to reduce the heavy filter.

[JRC1995]

Probably you have to figure out what max length/min length are suitable for your dataset:
https://github.com/JRC1995/Abstractive-Summarization/blob/master/Data_Pre-Processing.ipynb
You have to probably change them:
"text_max_len = 500
text_min_len = 25
summary_max_len = 30"

[vasanthaganesh]

I have changed the max length/min length values as you said, but it gives the error, how can I rectify this error.
I have used only the dataset reviews.csv with 50000 lines.