for i, sent in enumerate(tokenized_sentences):
tokenized_sentences[i] = [
w if w in word_to_index else unknown_token for w in sent
]
print("\nExample sentence: '%s'" % sentences[0])
print("\nExample sentence after Pre-processing: '%s'" % tokenized_sentences[0])
# Create the training data
X_train = np.asarray([[word_to_index[w] for w in sent[:-1]]
for sent in tokenized_sentences])
y_train = np.asarray([[word_to_index[w] for w in sent[1:]]
for sent in tokenized_sentences])
np.random.seed(10)
model = RNN(vocabulary_size)
o, s = model.forward_propagation(X_train[10])
print(o.shape)
print(o)
predictions = model.predict(X_train[10])
print(predictions.shape)
print(predictions)
print("-------------------------------------------")
'''
Cross Entropy loss is L(y, o) = -\cfrac{1}{N}\sum_{n\in N} y_n \log o_n
'''
E_loss = np.log(vocabulary_size)
print('Expected loss for random predictions is {}'.format(E_loss))
