def update_state(self, y_true, y_pred):
# y_true: shape [batch_size, sequence_size]
# y_pred: shape [batch_size, sequence_size]
prediction_dtype = y_pred.dtype
prediction_sequence_length = sequence_length_2D(y_pred)
y_true_tensor = tf.convert_to_tensor(y_true, dtype=prediction_dtype)
target_sequence_length = sequence_length_2D(y_true_tensor)
_, masked_corrected_predictions, _, _ = \
masked_accuracy(y_true, y_pred, target_sequence_length)
super().update_state(masked_corrected_predictions)
def update_state(self, y_true, y_pred):
# y_true: shape [batch_size, sequence_size]
# y_pred: shape [batch_size, sequence_size]
prediction_dtype = y_pred.dtype
prediction_sequence_length = sequence_length_2D(y_pred)
y_true_tensor = tf.cast(y_true, dtype=prediction_dtype)
target_sequence_length = sequence_length_2D(y_true_tensor)
edit_distance_val, _ = edit_distance(y_true_tensor,
target_sequence_length, y_pred,
prediction_sequence_length)
super().update_state(edit_distance_val)
def decoder_teacher_forcing(
self,
encoder_output,
target=None,
encoder_end_state=None
):
# ================ Setup ================
batch_size = encoder_output.shape[0]
# Prepare target for decoding
target_sequence_length = sequence_length_2D(target)
start_tokens = tf.tile([self.GO_SYMBOL], [batch_size])
end_tokens = tf.tile([self.END_SYMBOL], [batch_size])
if self.is_timeseries:
start_tokens = tf.cast(start_tokens, tf.float32)
end_tokens = tf.cast(end_tokens, tf.float32)
targets_with_go_and_eos = tf.concat([
tf.expand_dims(start_tokens, 1),
target, # todo tf2: right now cast to tf.int32, fails if tf.int64
tf.expand_dims(end_tokens, 1)], 1)
target_sequence_length_with_eos = target_sequence_length + 1
# Decoder Embeddings
decoder_emb_inp = self.decoder_embedding(targets_with_go_and_eos)
# Setting up decoder memory from encoder output
if self.attention_mechanism is not None:
encoder_sequence_length = sequence_length_3D(encoder_output)
self.attention_mechanism.setup_memory(
encoder_output,
memory_sequence_length=encoder_sequence_length
)
decoder_initial_state = self.build_decoder_initial_state(
batch_size,
encoder_state=encoder_end_state,
dtype=tf.float32
)
decoder = tfa.seq2seq.BasicDecoder(
self.decoder_rnncell,
sampler=self.sampler,
output_layer=self.dense_layer
)
# BasicDecoderOutput
outputs, final_state, generated_sequence_lengths = decoder(
decoder_emb_inp,
initial_state=decoder_initial_state,
sequence_length=target_sequence_length_with_eos
)
logits = outputs.rnn_output
mask = tf.sequence_mask(
generated_sequence_lengths,
maxlen=logits.shape[1],
dtype=tf.float32
)
logits = logits * mask[:, :, tf.newaxis]
return logits # , outputs, final_state, generated_sequence_lengths
def call(self, y_true, y_pred):
# y_true: shape [batch_size, sequence_size]
# y_pred: shape [batch_size, sequence_size, num_classes]
y_pred_tensor = y_pred[LOGITS]
y_true_tensor = tf.cast(y_true, dtype=tf.int64)
# pad the shorter sequence
y_pred_seq_len = tf.shape(y_pred_tensor)[1]
y_true_seq_len = tf.shape(y_true_tensor)[1]
y_pred_pad_len = tf.maximum(0, y_true_seq_len - y_pred_seq_len)
y_true_pad_len = tf.maximum(0, y_pred_seq_len - y_true_seq_len)
y_pred_tensor = tf.pad(y_pred_tensor,
[[0, 0], [0, y_pred_pad_len], [0, 0]])
y_true_tensor = tf.pad(y_true_tensor, [[0, 0], [0, y_true_pad_len]])
longest_sequence_length = tf.maximum(sequence_length_2D(y_true_tensor),
sequence_length_3D(y_pred_tensor))
longest_sequence_length += 1 # for EOS
longest_sequence_length = tf.minimum(longest_sequence_length,
tf.shape(y_true_tensor)[1])
mask = tf.sequence_mask(longest_sequence_length,
maxlen=tf.shape(y_true_tensor)[1],
dtype=tf.float32)
# compute loss based on valid time steps
loss = self.loss_function(y_true_tensor, y_pred_tensor)
loss = loss * mask
loss = tf.reduce_sum(loss) / tf.reduce_sum(mask)
return loss
def sequence_sampled_softmax_cross_entropy(targets, train_logits,
decoder_weights, decoder_biases,
num_classes, **loss):
batch_max_targets_sequence_length = tf.shape(targets)[1]
targets_sequence_length = sequence_length_2D(tf.cast(targets, tf.int64))
batch_max_train_logits_sequence_length = tf.shape(train_logits)[1]
logits_pad_len = tf.maximum(
0, batch_max_targets_sequence_length -
batch_max_train_logits_sequence_length)
targets_pad_len = tf.maximum(
0, batch_max_train_logits_sequence_length -
batch_max_targets_sequence_length)
padded_logits = tf.pad(train_logits, [[0, 0], [0, logits_pad_len], [0, 0]])
padded_targets = tf.pad(targets, [[0, 0], [0, targets_pad_len]])
output_exp = tf.cast(tf.reshape(padded_targets, [-1, 1]), tf.int64)
sampled_values = sample_values_from_classes(
output_exp, loss['sampler'], num_classes, loss['negative_samples'],
loss['unique'], loss['class_counts'], loss['distortion'])
if loss['sampler'] == 'fixed_unigram':
# regenerate sampled_values structure for specified samplers
# to handle any zero values in true_expected_count tensor
sampled_values = FixedUnigramCandidateSampler(
sampled_values.sampled_candidates,
# add smoothing constant EPSILON to handle any zero values
tf.add(sampled_values.true_expected_count, EPSILON),
sampled_values.sampled_expected_count)
def _sampled_loss(labels, logits):
labels = tf.cast(labels, tf.int64)
labels = tf.reshape(labels, [-1, 1])
logits = tf.cast(logits, tf.float32)
return tf.cast(
tf.nn.sampled_softmax_loss(weights=tf.transpose(decoder_weights),
biases=decoder_biases,
labels=labels,
inputs=logits,
num_sampled=loss['negative_samples'],
num_classes=num_classes,
sampled_values=sampled_values),
tf.float32)
train_loss = tfa.seq2seq.sequence_loss(padded_logits,
padded_targets,
tf.sequence_mask(
targets_sequence_length,
tf.shape(padded_targets)[1],
dtype=tf.float32),
average_across_timesteps=True,
average_across_batch=False,
softmax_loss_function=_sampled_loss)
return train_loss
def update_state(self, y_true, y_pred):
# y_true: shape [batch_size, sequence_size]
# y_pred: shape [batch_size, sequence_size]
prediction_dtype = y_pred.dtype
y_true_tensor = tf.cast(y_true, dtype=prediction_dtype)
target_sequence_length = sequence_length_2D(y_true_tensor)
masked_corrected_preds = masked_corrected_predictions(
y_true_tensor, y_pred, target_sequence_length)
super().update_state(masked_corrected_preds)
def update_state(self, y_true, y_pred, sample_weight=None):
y_true = tf.cast(y_true, dtype=tf.int64)
targets_sequence_length = sequence_length_2D(y_true)
last_targets = tf.gather_nd(
y_true,
tf.stack([
tf.range(tf.shape(y_true)[0]),
tf.maximum(targets_sequence_length - 1, 0)
],
axis=1))
super().update_state(last_targets, y_pred, sample_weight=sample_weight)
def _predictions_eval(
self,
inputs, # encoder_output, encoder_output_state
training=None
):
logits = self.call(inputs, training=training)
probabilities = tf.nn.softmax(
logits,
name='probabilities_{}'.format(self.name)
)
predictions = tf.argmax(
logits,
-1,
name='predictions_{}'.format(self.name),
output_type=tf.int64
)
# todo tf2: deal with spurious 0s in predictions
generated_sequence_lengths = sequence_length_2D(predictions)
last_predictions = tf.gather_nd(
predictions,
tf.stack(
[tf.range(tf.shape(predictions)[0]),
tf.maximum(
generated_sequence_lengths - 1,
0
)],
axis=1
),
name='last_predictions_{}'.format(self.name)
)
# mask logits
mask = tf.sequence_mask(
generated_sequence_lengths,
maxlen=logits.shape[1],
dtype=tf.float32
)
logits = logits * mask[:, :, tf.newaxis]
return {
PREDICTIONS: predictions,
LAST_PREDICTIONS: last_predictions,
PROBABILITIES: probabilities,
LOGITS: logits
}
def update_state(self, y_true, y_pred, sample_weight=None):
# TODO TF2 account for weights
targets_sequence_length = sequence_length_2D(
tf.convert_to_tensor(y_true, dtype=tf.int64))
last_targets = tf.gather_nd(
y_true,
tf.stack([
tf.range(tf.shape(y_true)[0]),
tf.maximum(targets_sequence_length - 1, 0)
],
axis=1))
last_targets = tf.cast(last_targets, dtype=tf.int64)
super().update_state(last_targets, y_pred)
def call(self, y_true, y_pred):
# y_true: shape [batch_size, sequence_size]
# y_pred: shape [batch_size, sequence_size, num_classes]
if self.from_logits:
y_pred_tensor = y_pred[LOGITS]
else:
y_pred_tensor = y_pred[PROBABILITIES]
y_true_tensor = tf.cast(y_true, dtype=tf.int64)
# pad the shorter sequence (tensor shape 1)
y_pred_tensor_len = tf.shape(y_pred_tensor)[1]
y_true_tensor_len = tf.shape(y_true_tensor)[1]
y_pred_pad_len = tf.maximum(0, y_true_tensor_len - y_pred_tensor_len)
y_true_pad_len = tf.maximum(0, y_pred_tensor_len - y_true_tensor_len)
y_pred_tensor = tf.pad(y_pred_tensor,
[[0, 0], [0, y_pred_pad_len], [0, 0]])
y_true_tensor = tf.pad(y_true_tensor, [[0, 0], [0, y_true_pad_len]])
y_true_seq_len = sequence_length_2D(y_true_tensor)
# longest_sequence_length = tf.maximum(y_true_seq_len,
# sequence_length_3D(y_pred_tensor))
# longest_sequence_length = tf.minimum(longest_sequence_length,
# y_true_seq_len)
# longest_sequence_length += 2 # for EOS
mask = tf.sequence_mask(
y_true_seq_len + 1, # this is for including the eos
# in case of generator and shouldn't impact
# negatively in case of tagger
maxlen=tf.shape(y_true_tensor)[1],
dtype=tf.float32
)
# compute loss based on valid time steps
loss = self.loss_function(y_true_tensor, y_pred_tensor)
loss = loss * mask
loss = tf.reduce_sum(loss) / tf.reduce_sum(mask)
return loss
def call(self, y_true, y_pred):
# y_true: shape [batch_size, sequence_size]
# y_pred: shape [batch_size, sequence_size, num_classes]
y_pred = y_pred[LOGITS]
y_true = tf.convert_to_tensor(y_true, dtype=tf.int64)
# pad the shorter sequence
if y_true.shape[1] > y_pred.shape[1]:
pad = tf.zeros([
y_pred.shape[0], y_true.shape[1] - y_pred.shape[1],
y_pred.shape[2]
],
dtype=y_pred.dtype)
y_pred = tf.concat([y_pred, pad], axis=1)
elif y_pred.shape[1] > y_true.shape[1]:
pad = tf.zeros([
y_true.shape[0],
y_pred.shape[1] - y_true.shape[1],
],
dtype=y_true.dtype)
y_true = tf.concat([y_true, pad], axis=1)
longest_sequence_length = tf.maximum(sequence_length_2D(y_true),
sequence_length_3D(y_pred))
longest_sequence_length += 1 # for EOS
longest_sequence_length = tf.minimum(longest_sequence_length,
y_true.shape[1])
mask = tf.sequence_mask(longest_sequence_length,
maxlen=y_true.shape[1],
dtype=tf.float32)
# compute loss based on valid time steps
loss = self.loss_function(y_true, y_pred)
loss = loss * mask
loss = tf.reduce_sum(loss) / tf.reduce_sum(mask)
return loss
def decoder_teacher_forcing(
self,
encoder_output,
target=None,
encoder_end_state=None
):
# ================ Setup ================
batch_size = tf.shape(encoder_output)[0]
# Prepare target for decoding
target_sequence_length = sequence_length_2D(target)
start_tokens = tf.tile([self.GO_SYMBOL], [batch_size])
end_tokens = tf.tile([self.END_SYMBOL], [batch_size])
if self.is_timeseries:
start_tokens = tf.cast(start_tokens, tf.float32)
end_tokens = tf.cast(end_tokens, tf.float32)
targets_with_go_and_eos = tf.concat([
tf.expand_dims(start_tokens, 1),
target, # right now cast to tf.int32, fails if tf.int64
tf.expand_dims(end_tokens, 1)], 1)
target_sequence_length_with_eos = target_sequence_length + 1
# Decoder Embeddings
decoder_emb_inp = self.decoder_embedding(targets_with_go_and_eos)
# Setting up decoder memory from encoder output
if self.attention_mechanism is not None:
encoder_sequence_length = sequence_length_3D(encoder_output)
self.attention_mechanism.setup_memory(
encoder_output,
memory_sequence_length=encoder_sequence_length
)
decoder_initial_state = self.build_decoder_initial_state(
batch_size,
encoder_state=encoder_end_state,
dtype=tf.float32
)
# use Ludwig custom BasicDecoder
decoder = BasicDecoder(
self.decoder_rnncell,
sampler=self.sampler,
output_layer=self.dense_layer
)
# BasicDecoderOutput
outputs, final_state, generated_sequence_lengths = decoder(
decoder_emb_inp,
initial_state=decoder_initial_state,
sequence_length=target_sequence_length_with_eos
)
logits = outputs.rnn_output
# mask = tf.sequence_mask(
# generated_sequence_lengths,
# maxlen=tf.shape(logits)[1],
# dtype=tf.float32
# )
# logits = logits * mask[:, :, tf.newaxis]
# append a trailing 0, useful for
# those datapoints that reach maximum length
# and don't have a eos at the end
logits = tf.pad(
logits,
[[0, 0], [0, 1], [0, 0]]
)
# EXPECTED SIZE OF RETURNED TENSORS
# logits: shape[batch_size, seq_size, num_classes] used for evaluation
# projection_input: shape[batch_size, seq_size, state_size] for sampled softmax
return {
LOGITS: logits,
PROJECTION_INPUT: outputs.projection_input
}
def __call__(self,
output_feature,
targets,
hidden,
hidden_size,
regularizer,
is_timeseries=False):
logging.info(' hidden shape: {0}'.format(hidden.shape))
if len(hidden.shape) != 3:
raise ValueError(
'Decoder inputs rank is {}, but should be 3 [batch x sequence x hidden] '
'when using a tagger sequential decoder. '
'Consider setting reduce_output to null / None if a sequential encoder / combiner is used.'
.format(len(hidden.shape)))
if is_timeseries:
output_feature['num_classes'] = 1
if not self.regularize:
regularizer = None
sequence_length = tf.shape(hidden)[1]
if self.attention:
hidden, hidden_size = feed_forward_memory_attention(
hidden, hidden, hidden_size)
targets_sequence_length = sequence_length_2D(targets)
initializer_obj = get_initializer(self.initializer)
class_weights = tf.get_variable('weights',
initializer=initializer_obj([
hidden_size,
output_feature['num_classes']
]),
regularizer=regularizer)
logging.debug(' weights: {0}'.format(class_weights))
class_biases = tf.get_variable('biases',
[output_feature['num_classes']])
logging.debug(' biases: {0}'.format(class_biases))
hidden_reshape = tf.reshape(hidden, [-1, hidden_size])
logits_to_reshape = tf.matmul(hidden_reshape,
class_weights) + class_biases
logits = tf.reshape(
logits_to_reshape,
[-1, sequence_length, output_feature['num_classes']])
logging.debug(' logits: {0}'.format(logits))
if is_timeseries:
probabilities_sequence = tf.zeros_like(logits)
predictions_sequence = tf.reshape(logits, [-1, sequence_length])
else:
probabilities_sequence = tf.nn.softmax(
logits, name='probabilities_{}'.format(output_feature['name']))
predictions_sequence = tf.argmax(logits,
-1,
name='predictions_{}'.format(
output_feature['name']),
output_type=tf.int32)
predictions_sequence_length = sequence_length_3D(hidden)
return predictions_sequence, probabilities_sequence, \
predictions_sequence_length, \
probabilities_sequence, targets_sequence_length, \
logits, hidden, class_weights, class_biases
def recurrent_decoder(encoder_outputs,
targets,
max_sequence_length,
vocab_size,
cell_type='rnn',
state_size=256,
embedding_size=50,
num_layers=1,
attention_mechanism=None,
beam_width=1,
projection=True,
tied_target_embeddings=True,
embeddings=None,
initializer=None,
regularizer=None,
is_timeseries=False):
with tf.variable_scope('rnn_decoder',
reuse=tf.AUTO_REUSE,
regularizer=regularizer):
# ================ Setup ================
if beam_width > 1 and is_timeseries:
raise ValueError('Invalid beam_width: {}'.format(beam_width))
GO_SYMBOL = vocab_size
END_SYMBOL = 0
batch_size = tf.shape(encoder_outputs)[0]
# ================ Projection ================
# Project the encoder outputs to the size of the decoder state
encoder_outputs_size = encoder_outputs.shape[-1]
if projection and encoder_outputs_size != state_size:
with tf.variable_scope('projection'):
encoder_output_rank = len(encoder_outputs.shape)
if encoder_output_rank > 2:
sequence_length = tf.shape(encoder_outputs)[1]
encoder_outputs = tf.reshape(encoder_outputs,
[-1, encoder_outputs_size])
encoder_outputs = fc_layer(encoder_outputs,
encoder_outputs.shape[-1],
state_size,
activation=None,
initializer=initializer)
encoder_outputs = tf.reshape(
encoder_outputs, [-1, sequence_length, state_size])
else:
encoder_outputs = fc_layer(encoder_outputs,
encoder_outputs.shape[-1],
state_size,
activation=None,
initializer=initializer)
# ================ Targets sequence ================
# Calculate the length of inputs and the batch size
with tf.variable_scope('sequence'):
targets_sequence_length = sequence_length_2D(targets)
start_tokens = tf.tile([GO_SYMBOL], [batch_size])
end_tokens = tf.tile([END_SYMBOL], [batch_size])
if is_timeseries:
start_tokens = tf.cast(start_tokens, tf.float32)
end_tokens = tf.cast(end_tokens, tf.float32)
targets_with_go_and_eos = tf.concat([
tf.expand_dims(start_tokens, 1), targets,
tf.expand_dims(end_tokens, 1)
], 1)
logging.debug(
' targets_with_go: {0}'.format(targets_with_go_and_eos))
targets_sequence_length_with_eos = targets_sequence_length + 1 # the EOS symbol is 0 so it's not increasing the real length of the sequence
# ================ Embeddings ================
if is_timeseries:
targets_embedded = tf.expand_dims(targets_with_go_and_eos, -1)
targets_embeddings = None
else:
with tf.variable_scope('embedding'):
if embeddings is not None:
embedding_size = embeddings.shape.as_list()[-1]
if tied_target_embeddings:
state_size = embedding_size
elif tied_target_embeddings:
embedding_size = state_size
if embeddings is not None:
embedding_go = tf.get_variable(
'embedding_GO',
initializer=tf.random_uniform([1, embedding_size],
-1.0, 1.0))
targets_embeddings = tf.concat([embeddings, embedding_go],
axis=0)
else:
initializer_obj = get_initializer(initializer)
targets_embeddings = tf.get_variable(
'embeddings',
initializer=initializer_obj(
[vocab_size + 1, embedding_size]),
regularizer=regularizer)
logging.debug(
' targets_embeddings: {0}'.format(targets_embeddings))
targets_embedded = tf.nn.embedding_lookup(
targets_embeddings,
targets_with_go_and_eos,
name='decoder_input_embeddings')
logging.debug(' targets_embedded: {0}'.format(targets_embedded))
# ================ Class prediction ================
if tied_target_embeddings:
class_weights = tf.transpose(targets_embeddings)
else:
initializer_obj = get_initializer(initializer)
class_weights = tf.get_variable('class_weights',
initializer=initializer_obj(
[state_size, vocab_size + 1]),
regularizer=regularizer)
logging.debug(' class_weights: {0}'.format(class_weights))
class_biases = tf.get_variable('class_biases', [vocab_size + 1])
logging.debug(' class_biases: {0}'.format(class_biases))
projection_layer = Projection(class_weights, class_biases)
# ================ RNN ================
initial_state = encoder_outputs
with tf.variable_scope('rnn_cells') as vs:
# Cell
cell_fun = get_cell_fun(cell_type)
if num_layers == 1:
cell = cell_fun(state_size)
if cell_type.startswith('lstm'):
initial_state = LSTMStateTuple(c=initial_state,
h=initial_state)
elif num_layers > 1:
cell = MultiRNNCell(
[cell_fun(state_size) for _ in range(num_layers)],
state_is_tuple=True)
if cell_type.startswith('lstm'):
initial_state = LSTMStateTuple(c=initial_state,
h=initial_state)
initial_state = tuple([initial_state] * num_layers)
else:
raise ValueError(
'num_layers in recurrent decoser: {}. '
'Number of layers in a recurrenct decoder cannot be <= 0'.
format(num_layers))
# Attention
if attention_mechanism is not None:
if attention_mechanism == 'bahdanau':
attention_mechanism = tf.contrib.seq2seq.BahdanauAttention(
num_units=state_size,
memory=encoder_outputs,
memory_sequence_length=sequence_length_3D(
encoder_outputs))
elif attention_mechanism == 'luong':
attention_mechanism = tf.contrib.seq2seq.LuongAttention(
num_units=state_size,
memory=encoder_outputs,
memory_sequence_length=sequence_length_3D(
encoder_outputs))
else:
raise ValueError(
'Attention mechanism {} not supported'.format(
attention_mechanism))
cell = tf.contrib.seq2seq.AttentionWrapper(
cell, attention_mechanism, attention_layer_size=state_size)
initial_state = cell.zero_state(dtype=tf.float32,
batch_size=batch_size)
initial_state = initial_state.clone(
cell_state=reduce_sequence(encoder_outputs, 'last'))
for v in tf.global_variables():
if v.name.startswith(vs.name):
logging.debug(' {}: {}'.format(v.name, v))
# ================ Decoding ================
def decode(initial_state,
cell,
helper,
beam_width=1,
projection_layer=None):
# The decoder itself
if beam_width > 1:
# Tile inputs for beam search decoder
beam_initial_state = tf.contrib.seq2seq.tile_batch(
initial_state, beam_width)
decoder = tf.contrib.seq2seq.BeamSearchDecoder(
cell=cell,
embedding=targets_embeddings,
start_tokens=start_tokens,
end_token=END_SYMBOL,
initial_state=beam_initial_state,
beam_width=beam_width,
output_layer=projection_layer)
else:
decoder = BasicDecoder(cell=cell,
helper=helper,
initial_state=initial_state,
output_layer=projection_layer)
# The decoding operation
outputs = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder,
output_time_major=False,
impute_finished=False if beam_width > 1 else True,
maximum_iterations=max_sequence_length)
return outputs
# ================ Decoding helpers ================
if is_timeseries:
train_helper = TimeseriesTrainingHelper(
inputs=targets_embedded,
sequence_length=targets_sequence_length_with_eos)
final_outputs_pred, final_state_pred, final_sequence_lengths_pred = decode(
initial_state,
cell,
train_helper,
projection_layer=projection_layer)
eval_logits = final_outputs_pred.rnn_output
train_logits = final_outputs_pred.projection_input
predictions_sequence = tf.reshape(eval_logits, [batch_size, -1])
predictions_sequence_length_with_eos = final_sequence_lengths_pred
else:
train_helper = tf.contrib.seq2seq.TrainingHelper(
inputs=targets_embedded,
sequence_length=targets_sequence_length_with_eos)
final_outputs_train, final_state_train, final_sequence_lengths_train = decode(
initial_state,
cell,
train_helper,
projection_layer=projection_layer)
eval_logits = final_outputs_train.rnn_output
train_logits = final_outputs_train.projection_input
# train_predictions = final_outputs_train.sample_id
pred_helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding=targets_embeddings,
start_tokens=start_tokens,
end_token=END_SYMBOL)
final_outputs_pred, final_state_pred, final_sequence_lengths_pred = decode(
initial_state,
cell,
pred_helper,
beam_width,
projection_layer=projection_layer)
if beam_width > 1:
predictions_sequence = final_outputs_pred.beam_search_decoder_output.predicted_ids[:, :,
0]
# final_outputs_pred..predicted_ids[:,:,0] would work too, but it contains -1s for padding
predictions_sequence_scores = final_outputs_pred.beam_search_decoder_output.scores[:, :,
0]
predictions_sequence_length_with_eos = final_sequence_lengths_pred[:,
0]
else:
predictions_sequence = final_outputs_pred.sample_id
predictions_sequence_scores = final_outputs_pred.rnn_output
predictions_sequence_length_with_eos = final_sequence_lengths_pred
logging.debug(' train_logits: {0}'.format(train_logits))
logging.debug(' eval_logits: {0}'.format(eval_logits))
logging.debug(' predictions_sequence: {0}'.format(predictions_sequence))
logging.debug(' predictions_sequence_scores: {0}'.format(
predictions_sequence_scores))
return predictions_sequence, predictions_sequence_scores, predictions_sequence_length_with_eos, \
targets_sequence_length_with_eos, eval_logits, train_logits, class_weights, class_biases
