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))
Actual_loos = model.calc_loss(X_train[:1000], y_train[:1000])
print('Actual loss for random predictions is {}'.format(Actual_loos))
losses = train_with_sgd(model,
X_train[:100],
y_train[:100],
nepoch=10,
evaluate_loss_after=1)
### Lets generate the text now
num_sentences = 10
senten_min_length = 7
for i in range(num_sentences):
sent = []
