callback.py

# Importing dependencies
import numpy as np
import tensorflow.keras as keras

# Class CustomCallback 
class CustomCallback(keras.callbacks.Callback):
 # This method intriduces some randomness in the prediction
 def sample(self, preds, temperature=1.0):
 preds = np.asarray(preds).astype('float64')
 preds = np.log(preds) / temperature
 exp_preds = np.exp(preds)
 preds = exp_preds / np.sum(exp_preds)
 probas = np.random.multinomial(1, preds, 1)
 return np.argmax(probas)
 
 # This method runs after each epoch
 def on_epoch_end(self, epoch, logs={}):
 sample_text = """#if UINTPTR_MAX == 0xffffffff
#define UPB_SIZE(size32, size64) size32
#else
#define UPB_SIZE(size32, size64) size64
#endif"""

 # Printing some info and predicted text
 print('\nCurrently at epoch {}'.format(epoch + 1))
 print('Starter text : {}'.format(sample_text))
 
 for temperature in [0.2, 0.5, 1.0, 1.2]:
 # Selecting first 40 characters
 review = sample_text[0:40]
 review = [ord(i) for i in review]
 
 # Predicting the next characters for 500 times
 for k in range(500): 
 # Predicting using the model
 temp = self.model.predict(np.array([review[k: k + 40]]))

 # Calling the sample method
 temp = self.sample(temp[0], temperature)

 # Appending the predicted charcter
 review.append(temp)
 

 print('\nGenerated text with temperature {}: {}'.format(temperature, ''.join([chr(i) for i in review])))