nmt_predict.py

def predict(input):
    hidden=[tf.zeros((1,512)),tf.zeros((1,512))]
    _,enc_h,enc_c=encoder(input,hidden)
    enc_states=[enc_h,enc_c]
    result=[]
    dec_input = tf.expand_dims(input[:,0], 0)
    for t in range(input.shape[1]):
        dec_output,_,_=decoder(dec_input,enc_states)
        
        output_id=tf.math.argmax(dec_output[0],-1)
        output_id=output_id[0].numpy()
        
        if output_id == hindi_tokenizer.word_index['<eos>']:
            return ' '.join(result)
          
        dec_input = tf.expand_dims([output_id], 0)
        result.append(hindi_tokenizer.index_word[output_id])