def __init__(self, params, controller, x_train, x_valid, name='child'):
print('-' * 80)
print('Building LM')
self.params = _set_default_params(params)
self.controller = controller
self.sample_arc = tf.unstack(controller.sample_arc)
self.name = name
# train data
(self.x_train, self.y_train, self.num_train_batches,
self.reset_start_idx, self.should_reset,
self.base_bptt) = data_utils.input_producer(x_train,
params.batch_size,
params.bptt_steps,
random_len=True)
params.add_hparam('num_train_steps',
self.num_train_batches * params.num_train_epochs)
# valid data
(self.x_valid, self.y_valid,
self.num_valid_batches) = data_utils.input_producer(
x_valid, params.batch_size, params.bptt_steps)
self._build_params()
self._build_train()
self._build_valid()
def __init__(self, params, x_train, x_valid, x_test, name='language_model'):
print('-' * 80)
print('Building LM')
self.params = _set_default_params(params)
self.name = name
# train data
(self.x_train, self.y_train,
self.num_train_batches, self.reset_start_idx,
self.should_reset, self.base_bptt) = data_utils.input_producer(
x_train, params.batch_size, params.bptt_steps, random_len=True)
params.add_hparam(
'num_train_steps', self.num_train_batches * params.num_train_epochs)
# valid data
(self.x_valid, self.y_valid,
self.num_valid_batches) = data_utils.input_producer(
x_valid, params.batch_size, params.bptt_steps)
# test data
(self.x_test, self.y_test,
self.num_test_batches) = data_utils.input_producer(x_test, 1, 1)
params.add_hparam('start_decay_step',
params.start_decay_epoch * self.num_train_batches)
params.add_hparam('decay_every_step',
params.decay_every_epoch * self.num_train_batches)
self._build_params()
self._build_train()
self._build_valid()
self._build_test()
