Created
February 7, 2019 22:18
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# Defining a method to generate the next character | |
def predict(net, char, h=None, top_k=None): | |
''' Given a character, predict the next character. | |
Returns the predicted character and the hidden state. | |
''' | |
# tensor inputs | |
x = np.array([[net.char2int[char]]]) | |
x = one_hot_encode(x, len(net.chars)) | |
inputs = torch.from_numpy(x) | |
if(train_on_gpu): | |
inputs = inputs.cuda() | |
# detach hidden state from history | |
h = tuple([each.data for each in h]) | |
# get the output of the model | |
out, h = net(inputs, h) | |
# get the character probabilities | |
p = F.softmax(out, dim=1).data | |
if(train_on_gpu): | |
p = p.cpu() # move to cpu | |
# get top characters | |
if top_k is None: | |
top_ch = np.arange(len(net.chars)) | |
else: | |
p, top_ch = p.topk(top_k) | |
top_ch = top_ch.numpy().squeeze() | |
# select the likely next character with some element of randomness | |
p = p.numpy().squeeze() | |
char = np.random.choice(top_ch, p=p/p.sum()) | |
# return the encoded value of the predicted char and the hidden state | |
return net.int2char[char], h |
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