How to measure perplexity in tensorflow?

I am using this tutorial code:

import tensorflow as tf

import numpy as np
import os
import time
path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')
# Read, then decode for py2 compat.
text = open(path_to_file, 'rb').read().decode(encoding='utf-8')
# length of text is the number of characters in it
print('Length of text: {} characters'.format(len(text)))
# Take a look at the first 250 characters in text
print(text[:250])
# The unique characters in the file
vocab = sorted(set(text))
print('{} unique characters'.format(len(vocab)))
# Creating a mapping from unique characters to indices
char2idx = {u:i for i, u in enumerate(vocab)}
idx2char = np.array(vocab)

text_as_int = np.array([char2idx[c] for c in text])
print('{')
for char,_ in zip(char2idx, range(20)):
    print('  {:4s}: {:3d},'.format(repr(char), char2idx[char]))
print('  ...\n}')
# Show how the first 13 characters from the text are mapped to integers
print('{} ---- characters mapped to int ---- > {}'.format(repr(text[:13]), text_as_int[:13]))
# The maximum length sentence you want for a single input in characters
# seq_length = 100
seq_length = 50
examples_per_epoch = len(text)//(seq_length+1)

# Create training examples / targets
char_dataset = tf.data.Dataset.from_tensor_slices(text_as_int)

for i in char_dataset.take(5):
    print(idx2char[i.numpy()])
sequences = char_dataset.batch(seq_length+1, drop_remainder=True)

for item in sequences.take(5):
    print(repr(''.join(idx2char[item.numpy()])))
    
def split_input_target(chunk):
    input_text = chunk[:-1]
    target_text = chunk[1:]
    return input_text, target_text

dataset = sequences.map(split_input_target)

for input_example, target_example in  dataset.take(1):
    print('Input data: ', repr(''.join(idx2char[input_example.numpy()])))
    print('Target data:', repr(''.join(idx2char[target_example.numpy()])))

for i, (input_idx, target_idx) in enumerate(zip(input_example[:5], target_example[:5])):
    print("Step {:4d}".format(i))
    print("  input: {} ({:s})".format(input_idx, repr(idx2char[input_idx])))
    print("  expected output: {} ({:s})".format(target_idx, repr(idx2char[target_idx])))

# Batch size
BATCH_SIZE = 64

# Buffer size to shuffle the dataset
# (TF data is designed to work with possibly infinite sequences,
# so it doesn't attempt to shuffle the entire sequence in memory. Instead,
# it maintains a buffer in which it shuffles elements).
BUFFER_SIZE = 10000

dataset = dataset.shuffle(BUFFER_SIZE).batch(BATCH_SIZE, drop_remainder=True)

dataset

# Length of the vocabulary in chars
vocab_size = len(vocab)

# The embedding dimension
embedding_dim = 128
#embedding_dim = 256

# Number of RNN units
rnn_units = 128
#rnn_units = 1024

def build_model(vocab_size, embedding_dim, rnn_units, batch_size):
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(vocab_size, embedding_dim,
                                  batch_input_shape=[batch_size, None]),
        tf.keras.layers.GRU(rnn_units,
                            return_sequences=True,
                            stateful=True,
                            recurrent_initializer='glorot_uniform'),
        tf.keras.layers.GRU(rnn_units,
                            return_sequences=True,
                            stateful=True,
                            recurrent_initializer='glorot_uniform'),
        tf.keras.layers.Dense(vocab_size)
    ])
    return model

model = build_model(
    vocab_size=len(vocab),
    embedding_dim=embedding_dim,
    rnn_units=rnn_units,
    batch_size=BATCH_SIZE)


for input_example_batch, target_example_batch in dataset.take(1):
    example_batch_predictions = model(input_example_batch)
    print(example_batch_predictions.shape, "# (batch_size, sequence_length, vocab_size)")

model.summary()



sampled_indices = tf.random.categorical(example_batch_predictions[0], num_samples=1)
sampled_indices = tf.squeeze(sampled_indices,axis=-1).numpy()

sampled_indices

print("Input: \n", repr("".join(idx2char[input_example_batch[0].numpy()])))
print()
print("Next Char Predictions: \n", repr("".join(idx2char[sampled_indices ])))

def loss(labels, logits):
    return tf.keras.losses.sparse_categorical_crossentropy(labels, logits, from_logits=True)

example_batch_loss = loss(target_example_batch, example_batch_predictions)
print("Prediction shape: ", example_batch_predictions.shape, " # (batch_size, sequence_length, vocab_size)")
print("scalar_loss:      ", example_batch_loss.numpy().mean())

model.compile(optimizer='adam', loss=loss)

# Directory where the checkpoints will be saved
checkpoint_dir = './training_checkpoints'
# Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")

checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
    filepath=checkpoint_prefix,
    save_weights_only=True)

#EPOCHS = 30
EPOCHS = 10

history = model.fit(dataset, epochs=EPOCHS, callbacks=[checkpoint_callback])
tf.train.latest_checkpoint(checkpoint_dir)
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1)

model.load_weights(tf.train.latest_checkpoint(checkpoint_dir))

model.build(tf.TensorShape([1, None]))


model.summary()

I have set the parameters so it runs quickly for testing. I would like to measure the perplexity of the model, say on the training set itself or some other test text. How can I do that?

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To make the question completely self-contained, given the model made above, how would you compute the perplexity of the string "where"?

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