def __init__(self, **kwargs):
super().__init__(include_accuracy=True, **kwargs)
# Define encoder
with tf.variable_scope('encoder'):
cell_encoder = rnn_cell(num_units=self.config.num_units,
num_cells=self.config.num_cells,
keep_prob=self.keep_prob_ph,
add_dropout=self.add_dropout)
# self.encoder = BidirectionalEncoder(
# cell=cell_encoder,
# parallel_iterations=self.parallel_iterations,
# swap_memory=self.swap_memory
# )
self.encoder = Char2WordEncoder(
cell=cell_encoder,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory)
# Define decoder
with tf.variable_scope('decoder'):
cell_decoder = rnn_cell(
num_units=2 *
self.config.num_units, # Because we use bidirectional encoder
num_cells=self.config.num_cells,
keep_prob=self.keep_prob_ph,
add_dropout=self.add_dropout)
self.decoder = AttentionDecoder(
cell=cell_decoder,
max_iterations=self.config.max_dec_seq_length + 1,
infer_token_prob=self.infer_token_prob,
use_scheduled_sampling=self.config.scheduled_sampling,
alphabet=self.alphabet,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory)
# Inputs for the one-step-at-a-time decoding
self.decoder_inputs = tf.placeholder(tf.int32,
shape=[None],
name='decoder_inputs')
self.decoder_state = tf.placeholder(
tf.float32,
shape=[None, 2, self.config.num_cells, 2 * self.config.num_units],
name='decoder_state')
self.decoder_attention = tf.placeholder(
tf.float32,
shape=[None, 2 * self.config.num_units],
name='decoder_attention')
if not self.prediction_mode:
self.mixed_loss_maintainer = MixedLossMaintainer(
epochs_filename=self.config.epochs_mixed_filename,
verbose=False)
def __init__(self, **kwargs):
super().__init__(**kwargs)
# Define decoder
with tf.variable_scope('decoder'):
cell_decoder = rnn_cell(num_units=self.config.num_units,
num_cells=self.config.num_cells,
keep_prob=self.keep_prob_ph,
add_dropout=self.add_dropout)
self.decoder = Decoder(
cell=cell_decoder,
max_iterations=self.config.max_dec_seq_length + 1,
infer_token_prob=self.infer_token_prob,
alphabet=self.alphabet,
use_scheduled_sampling=False,
check_seq_lengths=True,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory)
# Inputs for the one-step-at-a-time decoding
self.decoder_inputs = tf.placeholder(tf.int32,
shape=[None],
name='decoder_inputs')
self.decoder_state = tf.placeholder(
tf.float32,
shape=[None, 2, self.config.num_cells, self.config.num_units],
name='decoder_state')
def __init__(self, initial_state_attention=False, **kwargs):
# Parameters
self.initial_state_attention = initial_state_attention
RNN.__init__(self, **kwargs)
# Define encoder
with tf.variable_scope('encoder'):
cell_encoder = rnn_cell(num_units=self.config.num_units,
num_cells=self.config.num_cells,
keep_prob=self.keep_prob_ph,
add_dropout=self.add_dropout)
self.encoder = Char2WordEncoder(
cell=cell_encoder,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory)
# Define decoder
with tf.variable_scope('decoder'):
cell_decoder = rnn_cell(
num_units=2 *
self.config.num_units, # Because we use bidirectional encoder
num_cells=self.config.num_cells,
keep_prob=self.keep_prob_ph,
add_dropout=self.add_dropout)
self.decoder = AttentionDecoder(
cell=cell_decoder,
max_iterations=self.config.max_dec_seq_length + 1,
infer_token_prob=self.infer_token_prob,
use_scheduled_sampling=self.config.scheduled_sampling,
alphabet=self.alphabet,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory)
# Inputs for the one-step-at-a-time decoding
self.decoder_inputs = tf.placeholder(tf.int32,
shape=[None],
name='decoder_inputs')
self.decoder_state = tf.placeholder(
tf.float32,
shape=[None, 2, self.config.num_cells, 2 * self.config.num_units],
name='decoder_state')
self.decoder_attention = tf.placeholder(
tf.float32,
shape=[None, 2 * self.config.num_units],
name='decoder_attention')
def __init__(self, **kwargs):
RNN.__init__(self, include_accuracy=True, **kwargs)
# Define encoder
with tf.variable_scope('encoder'):
cell_encoder = rnn_cell(num_units=self.config.num_units,
num_cells=self.config.num_cells,
keep_prob=self.keep_prob_ph,
add_dropout=self.add_dropout)
self.encoder = BidirectionalEncoder(
cell=cell_encoder,
parallel_iterations=self.parallel_iterations,
swap_memory=self.swap_memory)
