def testStepWithGreedyEmbeddingHelper(self):
batch_size = 5
vocabulary_size = 7
cell_depth = vocabulary_size # cell's logits must match vocabulary size
input_depth = 10
start_tokens = np.random.randint(0, vocabulary_size, size=batch_size)
end_token = 1
with self.session(use_gpu=True) as sess:
embeddings = np.random.randn(vocabulary_size,
input_depth).astype(np.float32)
cell = rnn_cell.LSTMCell(vocabulary_size)
helper = helper_py.GreedyEmbeddingHelper(embeddings, start_tokens,
end_token)
my_decoder = basic_decoder.BasicDecoder(
cell=cell,
helper=helper,
initial_state=cell.zero_state(
dtype=dtypes.float32, batch_size=batch_size))
output_size = my_decoder.output_size
output_dtype = my_decoder.output_dtype
self.assertEqual(
basic_decoder.BasicDecoderOutput(cell_depth,
tensor_shape.TensorShape([])),
output_size)
self.assertEqual(
basic_decoder.BasicDecoderOutput(dtypes.float32, dtypes.int32),
output_dtype)
(first_finished, first_inputs, first_state) = my_decoder.initialize()
(step_outputs, step_state, step_next_inputs,
step_finished) = my_decoder.step(
constant_op.constant(0), first_inputs, first_state)
batch_size_t = my_decoder.batch_size
self.assertTrue(isinstance(first_state, rnn_cell.LSTMStateTuple))
self.assertTrue(isinstance(step_state, rnn_cell.LSTMStateTuple))
self.assertTrue(
isinstance(step_outputs, basic_decoder.BasicDecoderOutput))
self.assertEqual((batch_size, cell_depth), step_outputs[0].get_shape())
self.assertEqual((batch_size,), step_outputs[1].get_shape())
self.assertEqual((batch_size, cell_depth), first_state[0].get_shape())
self.assertEqual((batch_size, cell_depth), first_state[1].get_shape())
self.assertEqual((batch_size, cell_depth), step_state[0].get_shape())
self.assertEqual((batch_size, cell_depth), step_state[1].get_shape())
sess.run(variables.global_variables_initializer())
sess_results = sess.run({
"batch_size": batch_size_t,
"first_finished": first_finished,
"first_inputs": first_inputs,
"first_state": first_state,
"step_outputs": step_outputs,
"step_state": step_state,
"step_next_inputs": step_next_inputs,
"step_finished": step_finished
})
expected_sample_ids = np.argmax(
sess_results["step_outputs"].rnn_output, -1)
expected_step_finished = (expected_sample_ids == end_token)
expected_step_next_inputs = embeddings[expected_sample_ids]
self.assertAllEqual([False, False, False, False, False],
sess_results["first_finished"])
self.assertAllEqual(expected_step_finished, sess_results["step_finished"])
self.assertEqual(output_dtype.sample_id,
sess_results["step_outputs"].sample_id.dtype)
self.assertAllEqual(expected_sample_ids,
sess_results["step_outputs"].sample_id)
self.assertAllEqual(expected_step_next_inputs,
sess_results["step_next_inputs"])
def _build_decoder(self, encoder_outputs, encoder_state):
with tf.name_scope("seq_decoder"):
batch_size = self.batch_size
# sequence_length = tf.fill([self.batch_size], self.num_steps)
if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
sequence_length = self.iterator.target_length
else:
sequence_length = self.iterator.source_length
if (self.mode !=
tf.contrib.learn.ModeKeys.TRAIN) and self.beam_width > 1:
batch_size = batch_size * self.beam_width
encoder_outputs = beam_search_decoder.tile_batch(
encoder_outputs, multiplier=self.beam_width)
encoder_state = nest.map_structure(
lambda s: beam_search_decoder.tile_batch(
s, self.beam_width), encoder_state)
sequence_length = beam_search_decoder.tile_batch(
sequence_length, multiplier=self.beam_width)
single_cell = single_rnn_cell(self.hparams.unit_type,
self.num_units, self.dropout)
decoder_cell = MultiRNNCell(
[single_cell for _ in range(self.num_layers_decoder)])
decoder_cell = InputProjectionWrapper(decoder_cell,
num_proj=self.num_units)
attention_mechanism = create_attention_mechanism(
self.hparams.attention_mechanism,
self.num_units,
memory=encoder_outputs,
source_sequence_length=sequence_length)
decoder_cell = wrapper.AttentionWrapper(
decoder_cell,
attention_mechanism,
attention_layer_size=self.num_units,
output_attention=True,
alignment_history=False)
# AttentionWrapperState의 cell_state를 encoder의 state으로 설정한다.
initial_state = decoder_cell.zero_state(batch_size=batch_size,
dtype=tf.float32)
embeddings_decoder = tf.get_variable(
"embedding_decoder",
[self.num_decoder_symbols, self.num_units],
initializer=self.initializer,
dtype=tf.float32)
output_layer = Dense(units=self.num_decoder_symbols,
use_bias=True,
name="output_layer")
if self.mode == tf.contrib.learn.ModeKeys.TRAIN:
decoder_inputs = tf.nn.embedding_lookup(
embeddings_decoder, self.iterator.target_in)
decoder_helper = helper.TrainingHelper(
decoder_inputs, sequence_length=sequence_length)
dec = basic_decoder.BasicDecoder(decoder_cell,
decoder_helper,
initial_state,
output_layer=output_layer)
final_outputs, final_state, _ = decoder.dynamic_decode(dec)
output_ids = final_outputs.rnn_output
outputs = final_outputs.sample_id
else:
def embedding_fn(inputs):
return tf.nn.embedding_lookup(embeddings_decoder, inputs)
decoding_length_factor = 2.0
max_encoder_length = tf.reduce_max(self.iterator.source_length)
maximum_iterations = tf.to_int32(
tf.round(
tf.to_float(max_encoder_length) *
decoding_length_factor))
tgt_sos_id = tf.cast(
self.tgt_vocab_table.lookup(tf.constant(self.hparams.sos)),
tf.int32)
tgt_eos_id = tf.cast(
self.tgt_vocab_table.lookup(tf.constant(self.hparams.eos)),
tf.int32)
start_tokens = tf.fill([self.batch_size], tgt_sos_id)
end_token = tgt_eos_id
if self.beam_width == 1:
decoder_helper = helper.GreedyEmbeddingHelper(
embedding=embedding_fn,
start_tokens=start_tokens,
end_token=end_token)
dec = basic_decoder.BasicDecoder(decoder_cell,
decoder_helper,
initial_state,
output_layer=output_layer)
else:
dec = beam_search_decoder.BeamSearchDecoder(
cell=decoder_cell,
embedding=embedding_fn,
start_tokens=start_tokens,
end_token=end_token,
initial_state=initial_state,
output_layer=output_layer,
beam_width=self.beam_width)
final_outputs, final_state, _ = decoder.dynamic_decode(
dec,
# swap_memory=True,
maximum_iterations=maximum_iterations)
if self.mode == tf.contrib.learn.ModeKeys.TRAIN or self.beam_width == 1:
output_ids = final_outputs.sample_id
outputs = final_outputs.rnn_output
else:
output_ids = final_outputs.predicted_ids
outputs = final_outputs.beam_search_decoder_output.scores
return output_ids, outputs
def build(self):
conf = self.conf
name = self.name
job_type = self.job_type
dtype = self.dtype
# Input maps
self.in_table = lookup.MutableHashTable(key_dtype=tf.string,
value_dtype=tf.int64,
default_value=UNK_ID,
shared_name="in_table",
name="in_table",
checkpoint=True)
self.topic_in_table = lookup.MutableHashTable(
key_dtype=tf.string,
value_dtype=tf.int64,
default_value=2,
shared_name="topic_in_table",
name="topic_in_table",
checkpoint=True)
self.out_table = lookup.MutableHashTable(key_dtype=tf.int64,
value_dtype=tf.string,
default_value="_UNK",
shared_name="out_table",
name="out_table",
checkpoint=True)
graphlg.info("Creating placeholders...")
self.enc_str_inps = tf.placeholder(tf.string,
shape=(None, conf.input_max_len),
name="enc_inps")
self.enc_lens = tf.placeholder(tf.int32, shape=[None], name="enc_lens")
self.enc_str_topics = tf.placeholder(tf.string,
shape=(None, None),
name="enc_topics")
self.dec_str_inps = tf.placeholder(
tf.string, shape=[None, conf.output_max_len + 2], name="dec_inps")
self.dec_lens = tf.placeholder(tf.int32, shape=[None], name="dec_lens")
# table lookup
self.enc_inps = self.in_table.lookup(self.enc_str_inps)
self.enc_topics = self.topic_in_table.lookup(self.enc_str_topics)
self.dec_inps = self.in_table.lookup(self.dec_str_inps)
batch_size = tf.shape(self.enc_inps)[0]
with variable_scope.variable_scope(self.model_kind,
dtype=dtype) as scope:
# Create encode graph and get attn states
graphlg.info("Creating embeddings and do lookup...")
t_major_enc_inps = tf.transpose(self.enc_inps)
with ops.device("/cpu:0"):
self.embedding = variable_scope.get_variable(
"embedding", [conf.input_vocab_size, conf.embedding_size])
self.emb_enc_inps = embedding_lookup_unique(
self.embedding, t_major_enc_inps)
self.topic_embedding = variable_scope.get_variable(
"topic_embedding",
[conf.topic_vocab_size, conf.topic_embedding_size],
trainable=False)
self.emb_enc_topics = embedding_lookup_unique(
self.topic_embedding, self.enc_topics)
graphlg.info("Creating out projection weights...")
if conf.out_layer_size != None:
w = tf.get_variable(
"proj_w", [conf.out_layer_size, conf.output_vocab_size],
dtype=dtype)
else:
w = tf.get_variable("proj_w",
[conf.num_units, conf.output_vocab_size],
dtype=dtype)
b = tf.get_variable("proj_b", [conf.output_vocab_size],
dtype=dtype)
self.out_proj = (w, b)
graphlg.info("Creating encoding dynamic rnn...")
with variable_scope.variable_scope("encoder",
dtype=dtype) as scope:
if conf.bidirectional:
cell_fw = CreateMultiRNNCell(conf.cell_model,
conf.num_units,
conf.num_layers,
conf.output_keep_prob)
cell_bw = CreateMultiRNNCell(conf.cell_model,
conf.num_units,
conf.num_layers,
conf.output_keep_prob)
self.enc_outs, self.enc_states = bidirectional_dynamic_rnn(
cell_fw=cell_fw,
cell_bw=cell_bw,
inputs=self.emb_enc_inps,
sequence_length=self.enc_lens,
dtype=dtype,
parallel_iterations=16,
time_major=True,
scope=scope)
fw_s, bw_s = self.enc_states
self.enc_states = tuple([
tf.concat([f, b], axis=1) for f, b in zip(fw_s, bw_s)
])
self.enc_outs = tf.concat(
[self.enc_outs[0], self.enc_outs[1]], axis=2)
else:
cell = CreateMultiRNNCell(conf.cell_model, conf.num_units,
conf.num_layers,
conf.output_keep_prob)
self.enc_outs, self.enc_states = dynamic_rnn(
cell=cell,
inputs=self.emb_enc_inps,
sequence_length=self.enc_lens,
parallel_iterations=16,
scope=scope,
dtype=dtype,
time_major=True)
attn_len = tf.shape(self.enc_outs)[0]
graphlg.info("Preparing init attention and states for decoder...")
initial_state = self.enc_states
attn_states = tf.transpose(self.enc_outs, perm=[1, 0, 2])
attn_size = self.conf.num_units
topic_attn_size = self.conf.num_units
k = tf.get_variable(
"topic_proj",
[1, 1, self.conf.topic_embedding_size, topic_attn_size])
topic_attn_states = nn_ops.conv2d(
tf.expand_dims(self.emb_enc_topics, 2), k, [1, 1, 1, 1],
"SAME")
topic_attn_states = tf.squeeze(topic_attn_states, axis=2)
graphlg.info("Creating decoder cell...")
with variable_scope.variable_scope("decoder",
dtype=dtype) as scope:
cell = CreateMultiRNNCell(conf.cell_model, attn_size,
conf.num_layers,
conf.output_keep_prob)
# topic
if not self.for_deploy:
graphlg.info(
"Embedding decoder inps, tars and tar weights...")
t_major_dec_inps = tf.transpose(self.dec_inps)
t_major_tars = tf.slice(t_major_dec_inps, [1, 0],
[conf.output_max_len + 1, -1])
t_major_dec_inps = tf.slice(t_major_dec_inps, [0, 0],
[conf.output_max_len + 1, -1])
t_major_tar_wgts = tf.cumsum(tf.one_hot(
self.dec_lens - 1, conf.output_max_len + 1, axis=0),
axis=0,
reverse=True)
with ops.device("/cpu:0"):
emb_dec_inps = embedding_lookup_unique(
self.embedding, t_major_dec_inps)
hp_train = helper.ScheduledEmbeddingTrainingHelper(
inputs=emb_dec_inps,
sequence_length=self.enc_lens,
embedding=self.embedding,
sampling_probability=0.0,
out_proj=self.out_proj,
except_ids=None,
time_major=True)
output_layer = None
my_decoder = AttnTopicDecoder(
cell=cell,
helper=hp_train,
initial_state=initial_state,
attn_states=attn_states,
attn_size=attn_size,
topic_attn_states=topic_attn_states,
topic_attn_size=topic_attn_size,
output_layer=output_layer)
t_major_cell_outs, final_state = decoder.dynamic_decode(
decoder=my_decoder,
output_time_major=True,
maximum_iterations=conf.output_max_len + 1,
scope=scope)
t_major_outs = t_major_cell_outs.rnn_output
# Branch 1 for debugging, doesn't have to be called
self.outputs = tf.transpose(t_major_outs, perm=[1, 0, 2])
L = tf.shape(self.outputs)[1]
w, b = self.out_proj
self.outputs = tf.reshape(self.outputs,
[-1, int(w.shape[0])])
self.outputs = tf.matmul(self.outputs, w) + b
# For masking the except_ids when debuging
#m = tf.shape(self.outputs)[0]
#self.mask = tf.zeros([m, int(w.shape[1])])
#for i in [3]:
# self.mask = self.mask + tf.one_hot(indices=tf.ones([m], dtype=tf.int32) * i, on_value=100.0, depth=int(w.shape[1]))
#self.outputs = self.outputs - self.mask
self.outputs = tf.argmax(self.outputs, axis=1)
self.outputs = tf.reshape(self.outputs, [-1, L])
self.outputs = self.out_table.lookup(
tf.cast(self.outputs, tf.int64))
# Branch 2 for loss
self.loss = dyn_sequence_loss(self.conf, t_major_outs,
self.out_proj, t_major_tars,
t_major_tar_wgts)
self.summary = tf.summary.scalar("%s/loss" % self.name,
self.loss)
# backpropagation
self.build_backprop(self.loss, conf, dtype)
#saver
self.trainable_params.extend(tf.trainable_variables() +
[self.topic_embedding])
need_to_save = self.global_params + self.trainable_params + self.optimizer_params + tf.get_default_graph(
).get_collection("saveable_objects") + [
self.topic_embedding
]
self.saver = tf.train.Saver(need_to_save,
max_to_keep=conf.max_to_keep)
else:
hp_infer = helper.GreedyEmbeddingHelper(
embedding=self.embedding,
start_tokens=tf.ones(shape=[batch_size],
dtype=tf.int32),
end_token=EOS_ID,
out_proj=self.out_proj)
output_layer = None #layers_core.Dense(self.conf.outproj_from_size, use_bias=True)
my_decoder = AttnTopicDecoder(
cell=cell,
helper=hp_infer,
initial_state=initial_state,
attn_states=attn_states,
attn_size=attn_size,
topic_attn_states=topic_attn_states,
topic_attn_size=topic_attn_size,
output_layer=output_layer)
cell_outs, final_state = decoder.dynamic_decode(
decoder=my_decoder, scope=scope, maximum_iterations=40)
self.outputs = cell_outs.sample_id
#lookup
self.outputs = self.out_table.lookup(
tf.cast(self.outputs, tf.int64))
#saver
self.trainable_params.extend(tf.trainable_variables())
self.saver = tf.train.Saver(max_to_keep=conf.max_to_keep)
# Exporter for serving
self.model_exporter = exporter.Exporter(self.saver)
inputs = {
"enc_inps": self.enc_str_inps,
"enc_lens": self.enc_lens
}
outputs = {"out": self.outputs}
self.model_exporter.init(
tf.get_default_graph().as_graph_def(),
named_graph_signatures={
"inputs": exporter.generic_signature(inputs),
"outputs": exporter.generic_signature(outputs)
})
graphlg.info("Graph done")
graphlg.info("")
self.dec_states = final_state
tgt_inputs = tf.random.normal((batch_size, tgt_max_times, num_units),
dtype=tf.float32)
training_helper = helper_py.TrainingHelper(tgt_inputs, tgt_len)
# train helper
train_decoder = basic_decoder.BasicDecoder(
cell=attnRNNCell,
helper=training_helper,
initial_state=attnRNNCell.zero_state(batch_size, tf.float32))
# inference
embedding = tf.get_variable("embedding",
shape=(10, 16),
initializer=tf.random_uniform_initializer())
infer_helper = helper_py.GreedyEmbeddingHelper(
embedding=embedding, # 可以是callable，也可以是embedding矩阵
start_tokens=tf.zeros([batch_size], dtype=tf.int32),
end_token=9)
infer_decoder = basic_decoder.BasicDecoder(
cell=attnRNNCell,
helper=infer_helper,
initial_state=attnRNNCell.zero_state(batch_size, tf.float32))
final_outputs, final_state, final_sequence_lengths = decoder.dynamic_decode(
train_decoder, maximum_iterations=False)
print(final_outputs.rnn_output)
print(final_outputs.sample_id)
print(final_state.cell_state)
print(final_sequence_lengths)
print("--------infer-------------")
final_outputs, final_state, final_sequence_lengths = decoder.dynamic_decode(
