iter = 0
# for _ in xrange(100):
while True:
for x_str, target_str in sliding_window(data, seq_length):
# print(x_str, target_str)
inputs = [char_to_ix[ch] for ch in x_str]
targets = [char_to_ix[ch] for ch in target_str]
inputs_one_hot = [one_hots[x] for x in inputs]
targets_one_hot = [one_hots[t] for t in targets]
window = zip(inputs_one_hot, targets_one_hot)
# mean_loss = rnn.learn_window(window, loss, optimizer)
ys, dJdys, mean_loss = rnn.forward_window(window, loss)
rnn.backward_window(dJdys)
if iter % 100 == 0:
append_plot_magnitudes(rnn, mean_loss)
rnn.clip()
rnn.update_weights(optimizer)
if iter % 100 == 0:
sample_and_print(rnn, inputs[0], mean_loss)
show_plots()
plt.pause(0.05)
iter += 1
rnn.reset_memory()
