def do(self, *args):
init_ = next(self.main_loop.epoch_iterator)["features"][
0: self.text_length, 0:1]
# time x batch
whole_sentence_code = init_
vocab = get_character(self.dataset)
# whole_sentence
whole_sentence = ''
for char in vocab[whole_sentence_code[:, 0]]:
whole_sentence += char
last_output_in = self.generate_in(init_)
last_output_out = self.generate_out(init_)
last_output_forget = self.generate_forget(init_)
layers = len(last_output_in)
time = last_output_in[0].shape[0]
for i in range(layers):
plt.subplot(3, layers, i * 3)
plt.plot(np.arange(time), last_output_in[i][:, 0, 0])
plt.plot(np.arange(time), last_output_in[i][:, 0, 1])
plt.subplot(3, layers, i * 3 + 1)
plt.plot(np.arange(time), last_output_out[i][:, 0, 0])
plt.plot(np.arange(time), last_output_out[i][:, 0, 1])
plt.subplot(3, layers, i * 3 + 2)
plt.plot(np.arange(time), last_output_forget[i][:, 0, 0])
plt.plot(np.arange(time), last_output_forget[i][:, 0, 1])
plt.show()
def do(self, *args):
init_ = next(
self.main_loop.epoch_iterator)["features"][0:self.text_length, 0:1]
# time x batch
whole_sentence_code = init_
vocab = get_character(self.dataset)
# whole_sentence
whole_sentence = ''
for char in vocab[whole_sentence_code[:, 0]]:
whole_sentence += char
last_output_in = self.generate_in(init_)
last_output_out = self.generate_out(init_)
last_output_forget = self.generate_forget(init_)
layers = len(last_output_in)
time = last_output_in[0].shape[0]
for i in range(layers):
plt.subplot(3, layers, i * 3)
plt.plot(np.arange(time), last_output_in[i][:, 0, 0])
plt.plot(np.arange(time), last_output_in[i][:, 0, 1])
plt.subplot(3, layers, i * 3 + 1)
plt.plot(np.arange(time), last_output_out[i][:, 0, 0])
plt.plot(np.arange(time), last_output_out[i][:, 0, 1])
plt.subplot(3, layers, i * 3 + 2)
plt.plot(np.arange(time), last_output_forget[i][:, 0, 0])
plt.plot(np.arange(time), last_output_forget[i][:, 0, 1])
plt.show()
def do(self, *args):
# init is TIME X 1
# This is because in interactive mode,
# self.main_loop.epoch_iterator is not accessible.
if self.interactive_mode:
# TEMPORARY HACK
it = self.main_loop.data_stream.get_epoch_iterator()
init_ = next(
it)[0][
0: self.initial_text_length,
0:1]
else:
init_ = next(
self.main_loop.epoch_iterator)["features"][
0: self.initial_text_length,
0:1]
inputs_ = init_
all_output_probabilities = []
logger.info("\nGeneration:")
for i in range(self.generation_length):
# time x batch x features (1 x 1 x vocab_size)
last_output = self.generate(inputs_)[0][-1:, :, :]
# time x features (1 x vocab_size) '0' is for removing one dim
last_output_probabilities = softmax(last_output[0])
all_output_probabilities += [last_output_probabilities]
# 1 x 1
if self.softmax_sampling == 'argmax':
argmax = True
else:
argmax = False
last_output_sample = sample(last_output_probabilities, argmax)
inputs_ = np.vstack([inputs_, last_output_sample])
# time x batch
whole_sentence_code = inputs_
vocab = get_character(self.dataset)
# whole_sentence
whole_sentence = ''
for char in vocab[whole_sentence_code[:, 0]]:
whole_sentence += char
logger.info(whole_sentence[:init_.shape[0]] + ' ...')
logger.info(whole_sentence)
if self.ploting_path is not None:
all_output_probabilities_array = np.zeros(
(self.generation_length, all_output_probabilities[0].shape[1]))
for i, output_probabilities in enumerate(all_output_probabilities):
all_output_probabilities_array[i] = output_probabilities
probability_plot(all_output_probabilities_array,
whole_sentence[init_.shape[0]:],
vocab, self.ploting_path)
def do(self, *args):
# init is TIME X 1
# This is because in interactive mode,
# self.main_loop.epoch_iterator is not accessible.
if self.interactive_mode:
# TEMPORARY HACK
it = self.main_loop.data_stream.get_epoch_iterator()
init_ = next(it)[0][0:self.initial_text_length, 0:1]
else:
init_ = next(self.main_loop.epoch_iterator)["features"][
0:self.initial_text_length, 0:1]
inputs_ = init_
all_output_probabilities = []
logger.info("\nGeneration:")
for i in range(self.generation_length):
# time x batch x features (1 x 1 x vocab_size)
last_output = self.generate(inputs_)[0][-1:, :, :]
# time x features (1 x vocab_size) '0' is for removing one dim
last_output_probabilities = softmax(last_output[0])
all_output_probabilities += [last_output_probabilities]
# 1 x 1
if self.softmax_sampling == 'argmax':
argmax = True
else:
argmax = False
last_output_sample = sample(last_output_probabilities, argmax)
inputs_ = np.vstack([inputs_, last_output_sample])
# time x batch
whole_sentence_code = inputs_
vocab = get_character(self.dataset)
# whole_sentence
whole_sentence = ''
for char in vocab[whole_sentence_code[:, 0]]:
whole_sentence += char
logger.info(whole_sentence[:init_.shape[0]] + ' ...')
logger.info(whole_sentence)
if self.ploting_path is not None:
all_output_probabilities_array = np.zeros(
(self.generation_length, all_output_probabilities[0].shape[1]))
for i, output_probabilities in enumerate(all_output_probabilities):
all_output_probabilities_array[i] = output_probabilities
probability_plot(all_output_probabilities_array,
whole_sentence[init_.shape[0]:], vocab,
self.ploting_path)