def apply_func_on_depth(obj, func, depth, permeable_types=(list, tuple, dict)):
if depth != 0 and isinstance(obj, permeable_types):
if isinstance(obj, (list, tuple)):
processed = list()
for elem in obj:
processed.append(
apply_func_on_depth(elem,
func,
depth - 1,
permeable_types=permeable_types))
if isinstance(obj, LSTMStateTuple):
processed = LSTMStateTuple(
c=processed[0],
h=processed[1],
)
elif isinstance(obj, tuple):
processed = tuple(processed)
return processed
elif isinstance(obj, dict):
processed = dict()
for key, value in obj.items():
processed[key] = apply_func_on_depth(
value, func, depth - 1, permeable_types=permeable_types)
return processed
return func(obj)
def deep_zip(objects, depth, permeable_types=(list, tuple, dict)):
# print("(deep_zip)objects:", objects)
if depth != 0 and isinstance(objects[0], permeable_types):
if isinstance(objects[0], (list, tuple)):
zipped = list()
for comb in zip(*objects):
zipped.append(
deep_zip(comb, depth - 1, permeable_types=permeable_types))
if isinstance(objects[0], LSTMStateTuple):
zipped = LSTMStateTuple(
c=zipped[0],
h=zipped[1],
)
elif isinstance(objects[0], tuple):
zipped = tuple(zipped)
return zipped
elif isinstance(objects[0], dict):
zipped = dict()
for key in objects[0].keys():
values = [obj[key] for obj in objects]
zipped[key] = deep_zip(values,
depth - 1,
permeable_types=permeable_types)
return zipped
return tuple(objects)
def _add_encoder(self, encoder_inputs, seq_len):
with tf.variable_scope('encoder'):
cell_fw = tf.contrib.rnn.LSTMCell(self.hidden_size,
initializer=self.rand_uni_init,
state_is_tuple=True)
cell_bw = tf.contrib.rnn.LSTMCell(self.hidden_size,
initializer=self.rand_uni_init,
state_is_tuple=True)
outputs, states = bidirectional_dynamic_rnn(
cell_fw,
cell_bw,
encoder_inputs,
sequence_length=seq_len,
swap_memory=True,
dtype=tf.float32)
# encoder_outputs, encoder_states = tf.nn.dynamic_rnn(cell_fw, encoder_inputs,
# dtype=tf.float32, sequence_length=seq_len,
# swap_memory=True)
fw_outputs, bw_outputs = outputs
c_fw, h_fw = states[0]
c_bw, h_bw = states[1]
encoder_c = c_fw + c_bw
encoder_h = h_fw + h_bw
# assemble to be a state tuple object
encoder_states = LSTMStateTuple(c=encoder_c, h=encoder_h)
encoder_outputs = fw_outputs + bw_outputs # add outputs
# print(outputs)
# print(states)
return encoder_outputs, encoder_states
def call(self, inputs):
demo, times, values, measurements, dt, lengths = inputs
demo_encoded = self.demo_encoder(demo)
initial_state = LSTMStateTuple(*tf.split(demo_encoded, 2, axis=-1))
values = tf.concat((values, tf.cast(measurements, tf.float32), dt),
axis=-1)
mask = tf.sequence_mask(tf.squeeze(lengths, axis=-1), name='mask')
out = self.rnn(PhasedLSTMInput(times=times, x=values),
mask=mask,
initial_state=initial_state)
return self.output_layer(out)
def call(self, inputs, state):#state
sigmoid = math_ops.sigmoid
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
concat = _linear([inputs, h], 4 * self._num_units + 2 * self.n_chunk, True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
f_master_t = concat[:,:self.n_chunk]
f_master_t = self.cumsum(tf.nn.softmax(f_master_t,axis=-1))
f_master_t = tf.expand_dims(f_master_t,2)
i_master_t = concat[:,self.n_chunk:2*self.n_chunk]
i_master_t = self.cumsum(tf.nn.softmax(i_master_t,axis=-1),'left')
i_master_t = tf.expand_dims(i_master_t,2)
concat = concat[:, 2*self.n_chunk:]
#reshape
concat = tf.reshape(concat,[-1,self.n_chunk*4,self.chunk_size])
f_t = tf.nn.sigmoid(concat[:, :self.n_chunk])
i_t = tf.nn.sigmoid(concat[:, self.n_chunk : 2*self.n_chunk])
o_t = tf.nn.sigmoid(concat[:, 2*self.n_chunk : 3*self.n_chunk])
c_t_hat = tf.tanh(concat[:, 3*self.n_chunk:])
w_t = f_master_t * i_master_t
new_c = w_t*(f_t*tf.reshape(c,[-1,self.n_chunk,self.chunk_size]) + i_t*c_t_hat) + \
(i_master_t-w_t)*c_t_hat + \
(f_master_t-w_t)*tf.reshape(c,[-1,self.n_chunk,self.chunk_size])
new_h = tf.tanh(new_c)*o_t
new_c = tf.reshape(new_c,[-1,self._num_units])
new_h = tf.reshape(new_h,[-1,self._num_units])
# i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
# new_c = (
# c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
# new_h = self._activation(new_c) * sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat([new_c, new_h], 1)
return new_h, new_state
def tuplify_pass(obj, outer_struct=None, outer_key=None):
if isinstance(obj, dict):
if 'name__' in obj:
tuplified = copy.copy(obj)
del tuplified['name__']
if obj['name__'] == "LSTMStateTuple":
tuplified = LSTMStateTuple(**tuplified)
else:
tuplified = namedtuple(obj['name__'],
sorted(tuplified))(**tuplified)
outer_struct[outer_key] = tuplified
else:
for key in obj:
tuplify_pass(obj[key], obj, key)
elif isinstance(obj, list):
for i in range(len(obj)):
tuplify_pass(obj[i], obj, i)
else:
outer_struct[outer_key] = obj
def call(self, inputs, state):
sigmoid = math_ops.sigmoid
one = constant_op.constant(1, dtype=dtypes.int32)
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=one)
gate_inputs = math_ops.matmul(array_ops.concat([inputs, h], 1), self._kernel)
gate_inputs = nn_ops.bias_add(gate_inputs, self._bias)
i, j, f, o = array_ops.split(value=gate_inputs, num_or_size_splits=4, axis=one)
forget_bias_tensor = constant_op.constant(self._forget_bias, dtype=f.dtype)
add = math_ops.add
multiply = math_ops.multiply
new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))), multiply(sigmoid(i), self._activation(j)))
new_h = multiply(self._activation(new_c), sigmoid(o))
# vib
if self.pruning:
std = tf.exp(self._logD * 0.5)
dim = tf.shape(self._logD)[0]
# eps = tf.random.normal(shape=[self.batch_size, dim])
z_scale = tf.cond(self.is_training, lambda: tf.reshape(self._mu, shape=[1, -1]) + tf.random.normal(
shape=[self.batch_size, dim]) * tf.reshape(std, shape=[1, -1]), lambda: (tf.reshape(self._mu, shape=[1,
-1]) + tf.zeros(
shape=[self.batch_size, dim])) * self.get_mask())
new_h = new_h * z_scale
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat([new_c, new_h], 1)
return new_h, new_state
def add_model(self):
with tf.variable_scope("embed_lookup"):
#modify initializer here to add glove/word2vec
embedding = getGlove([wrd for wrd in self.vocab if wrd != '<unk>'],
'wiki_300')
_wrd_embed = tf.get_variable(
'embed_matrix', [len(self.vocab) - 1, self.p.embed_dim],
initializer=tf.constant_initializer(embedding),
regularizer=self.regularizer)
wrd_pad = tf.Variable(tf.zeros([1, self.p.embed_dim]),
trainable=False)
self.embed_matrix = tf.concat([_wrd_embed, wrd_pad], axis=0)
#Embed the source and target sentences. Elmo can be added here
self.enc_inp_embed = tf.nn.embedding_lookup(self.embed_matrix,
self.enc_inp)
self.dec_inp_embed = tf.nn.embedding_lookup(self.embed_matrix,
self.dec_inp)
self.logger.info("Building encoder")
with tf.variable_scope('encoder'):
self.enc_cell = self.build_enc_cell()
self.enc_outputs, self.enc_last_state = tf.nn.dynamic_rnn(
cell=self.enc_cell,
inputs=self.enc_inp_embed,
sequence_length=self.enc_inp_len,
dtype=self.p.dtype,
time_major=False,
scope='enc_rnn')
#DED part. Also used for get_hidden
self.enc_outputs_para, self.enc_last_state_para = tf.nn.dynamic_rnn(
cell=self.enc_cell,
inputs=self.dec_inp_embed,
sequence_length=self.dec_inp_len,
dtype=self.p.dtype,
time_major=False,
scope='enc_rnn')
if self.p.use_bidir:
self.fw_cell, self.bw_cell = self.build_bi_enc_cell()
self.enc_outputs, self.enc_last_state_fw, self.enc_last_state_bw = tf.contrib.rnn.stack_bidirectional_dynamic_rnn(
cells_fw=self.fw_cell,
cells_bw=self.bw_cell,
inputs=self.enc_inp_embed,
sequence_length=self.enc_inp_len,
dtype=self.p.dtype,
time_major=False,
scope='bi_enc_rnn')
enc_last_state_fw = [state for state in self.enc_last_state_fw]
enc_last_state_bw = [state for state in self.enc_last_state_bw]
enc_last_state = []
for st, _ in enumerate(enc_last_state_fw):
enc_last_state.append(
LSTMStateTuple(
tf.concat([
enc_last_state_fw[st].c,
enc_last_state_bw[st].c
],
axis=-1),
tf.concat([
enc_last_state_fw[st].h,
enc_last_state_bw[st].h
],
axis=-1)))
self.enc_last_state = tuple(enc_last_state)
self.enc_outputs_para, self.enc_last_state_fw_para, self.enc_last_state_bw_para = tf.contrib.rnn.stack_bidirectional_dynamic_rnn(
cells_fw=self.fw_cell,
cells_bw=self.bw_cell,
inputs=self.dec_inp_embed,
sequence_length=self.dec_inp_len,
dtype=self.p.dtype,
time_major=False,
scope='bi_enc_rnn')
enc_last_state_fw_para = [
state for state in self.enc_last_state_fw_para
]
enc_last_state_bw_para = [
state for state in self.enc_last_state_bw_para
]
enc_last_state = []
for st, _ in enumerate(enc_last_state_fw_para):
enc_last_state.append(
LSTMStateTuple(
tf.concat([
enc_last_state_fw_para[st].c,
enc_last_state_bw_para[st].c
],
axis=-1),
tf.concat([
enc_last_state_fw_para[st].h,
enc_last_state_bw_para[st].h
],
axis=-1)))
self.enc_last_state_para = tuple(enc_last_state)
if self.p.use_gan:
self.transformation = self.build_generator(
self.p.hidden_size *
2 if self.p.use_bidir else self.p.hidden_size)
enc_last_state = [state for state in self.enc_last_state]
enc_last_state[-1] = LSTMStateTuple(
self.enc_last_state[-1].c,
self.enc_last_state[-1].h + 10 * self.transformation)
self.enc_last_state = tuple(enc_last_state)
self.dec_cell, self.dec_initial_state = self.build_dec_cell(
self.p.hidden_size * 2 if self.p.use_bidir else self.p.hidden_size)
self.input_layer = Dense(self.p.hidden_size *
2 if self.p.use_bidir else self.p.hidden_size,
name="input_projection")
self.output_layer = Dense(len(self.vocab), name="output_projection")
if self.p.mode == 'train':
self.logger.info("Building training decoder")
self.dec_inp_embed = self.input_layer(
self.dec_inp_embed
) #decoder inputs dim should match encoder outputs dim
training_helper = seq2seq.TrainingHelper(
inputs=self.dec_inp_embed,
sequence_length=self.dec_inp_len,
time_major=False,
name='training_helper')
training_decoder = seq2seq.BasicDecoder(
cell=self.dec_cell,
helper=training_helper,
initial_state=self.dec_initial_state,
output_layer=self.output_layer)
self.max_decoder_length = tf.reduce_max(self.dec_inp_len)
# res = self.debug([self.dec_inp_embed]); pdb.set_trace()
(self.dec_outputs_train, self.dec_last_state_train,
self.dec_outputs_length_train) = (seq2seq.dynamic_decode(
decoder=training_decoder,
output_time_major=False,
impute_finished=True,
maximum_iterations=self.max_decoder_length))
#since output layer is passed to decoder, logits = output
self.dec_logits_train = self.dec_outputs_train.rnn_output
self.dec_pred_train = tf.argmax(self.dec_logits_train,
axis=-1,
name='decoder_pred_train')
masks = tf.sequence_mask(lengths=self.dec_inp_len,
maxlen=tf.shape(self.dec_inp)[1],
dtype=self.p.dtype,
name='masks')
self.loss = seq2seq.sequence_loss(logits=self.dec_logits_train,
targets=self.dec_out,
weights=masks,
average_across_timesteps=True,
average_across_batch=True)
tf.summary.scalar('loss', self.loss)
elif self.p.mode == 'decode':
self.logger.info("building decoder for inference")
start_tokens = tf.ones([self.p.batch_size], tf.int32) * tf.cast(
self.vocab_table.lookup(tf.constant('<sos>')), tf.int32)
self.start_tokens = start_tokens
# pdb.set_trace()
end_token = tf.cast(self.vocab_table.lookup(tf.constant('<eos>')),
tf.int32)
def embed_and_input_proj(inputs):
return self.input_layer(
tf.nn.embedding_lookup(self.embed_matrix, inputs))
if not self.p.use_beam_search:
self.logger.info("Building greedy decoder")
decoding_helper = seq2seq.GreedyEmbeddingHelper(
start_tokens=start_tokens,
end_token=end_token,
embedding=embed_and_input_proj)
inference_decoder = seq2seq.BasicDecoder(
cell=self.dec_cell,
helper=decoding_helper,
initial_state=self.dec_initial_state,
output_layer=self.output_layer)
else:
self.logger.info("Building beam search decoder")
inference_decoder = beam_search_decoder.BeamSearchDecoder(
cell=self.dec_cell,
embedding=embed_and_input_proj,
start_tokens=start_tokens,
end_token=end_token,
initial_state=self.dec_initial_state,
beam_width=self.p.beam_width,
output_layer=self.output_layer)
(self.dec_out_decode, self.dec_last_state_decode,
self.dec_out_length_decode) = (seq2seq.dynamic_decode(
inference_decoder,
output_time_major=False,
maximum_iterations=self.p.max_decode_step))
if not self.p.use_beam_search:
#batchsize X seq_len X 1
self.dec_pred_decode = tf.expand_dims(
self.dec_out_decode.sample_id, -1)
else:
#batch_size X seq_len X beam_width
self.dec_pred_decode = self.dec_out_decode.predicted_ids
encoder_fw_final_state,
encoder_bw_final_state) = tf.nn.bidirectional_dynamic_rnn(
cell_fw=encoder_cell,
cell_bw=encoder_cell,
inputs=encoder_inputs_embedded,
sequence_length=encoder_inputs_length,
dtype=tf.float32,
time_major=True)
# 融合双向 LSTM 的状态
encoder_outputs = tf.concat((encoder_fw_outputs, encoder_bw_outputs), axis=2)
encoder_final_state_c = tf.concat(
(encoder_fw_final_state.c, encoder_bw_final_state.c), axis=1)
encoder_final_state_h = tf.concat(
(encoder_fw_final_state.h, encoder_bw_final_state.h), axis=1)
encoder_final_state = LSTMStateTuple(c=encoder_final_state_c,
h=encoder_final_state_h)
# decoder
decoder_cell = LSTMCell(decoder_hidden_units)
encoder_max_time, batch_size = tf.unstack(tf.shape(encoder_inputs))
decoder_lengths = encoder_inputs_length + 3
"""
Decoder will contain manually specified by us transition step:
output(t) -> output projection(t) -> prediction(t) (argmax) -> input embedding(t+1) -> input(t+1)
"""
assert EOS == 1 and PAD == 0
eos_time_slice = tf.ones((batch_size, ), dtype=tf.int32, name='EOS')
pad_time_slice = tf.zeros((batch_size, ), dtype=tf.int32, name='PAD')
def state_size(self):
return (LSTMStateTuple(self._num_units, self._num_units)
if self._state_is_tuple else 2 * self._num_units)
def speller(encoder_outputs,
encoder_state,
decoder_inputs,
source_sequence_length,
target_sequence_length,
mode,
hparams):
batch_size = tf.shape(encoder_outputs)[0]
beam_width = hparams.beam_width
if mode == tf.estimator.ModeKeys.PREDICT and beam_width > 0:
source_sequence_length = tf.contrib.seq2seq.tile_batch(
source_sequence_length, multiplier=beam_width)
encoder_state = tf.contrib.seq2seq.tile_batch(
encoder_state, multiplier=beam_width)
batch_size = batch_size * beam_width
def embedding_fn(ids):
# pass callable object to avoid OOM when using one-hot encoding
if hparams.embedding_size != 0:
target_embedding = tf.get_variable(
'target_embedding', [
hparams.target_vocab_size, hparams.embedding_size],
dtype=tf.float32, initializer=tf.contrib.layers.xavier_initializer())
return tf.nn.embedding_lookup(target_embedding, ids)
else:
return tf.one_hot(ids, hparams.target_vocab_size)
cell_list = []
for layer in range(hparams.num_layers):
with tf.variable_scope('decoder_cell_'.format(layer)):
cell = lstm_cell(hparams.num_units * 2, hparams.dropout, mode)
cell_list.append(cell)
decoder_cell = tf.nn.rnn_cell.MultiRNNCell(cell_list)
projection_layer = tf.layers.Dense(
hparams.target_vocab_size, use_bias=True, name='projection_layer')
initial_state = tuple([LSTMStateTuple(c=tf.concat([es[0].c, es[1].c], axis=-1),
h=tf.concat([es[0].h, es[1].h], axis=-1))
for es in encoder_state[-hparams.num_layers:]])
maximum_iterations = None
if mode != tf.estimator.ModeKeys.TRAIN:
max_source_length = tf.reduce_max(source_sequence_length)
maximum_iterations = tf.to_int32(tf.round(tf.to_float(
max_source_length) * hparams.decoding_length_factor))
if mode == tf.estimator.ModeKeys.TRAIN:
decoder_inputs = embedding_fn(decoder_inputs)
if hparams.sampling_probability > 0.0:
helper = tf.contrib.seq2seq.ScheduledEmbeddingTrainingHelper(
decoder_inputs, target_sequence_length,
embedding_fn, hparams.sampling_probability)
else:
helper = tf.contrib.seq2seq.TrainingHelper(
decoder_inputs, target_sequence_length)
decoder = tf.contrib.seq2seq.BasicDecoder(
decoder_cell, helper, initial_state, output_layer=projection_layer)
elif mode == tf.estimator.ModeKeys.PREDICT and beam_width > 0:
start_tokens = tf.fill(
[tf.div(batch_size, beam_width)], hparams.sos_id)
decoder = tf.contrib.seq2seq.BeamSearchDecoder(
cell=decoder_cell,
embedding=embedding_fn,
start_tokens=start_tokens,
end_token=hparams.eos_id,
initial_state=initial_state,
beam_width=beam_width,
output_layer=projection_layer)
else:
start_tokens = tf.fill([batch_size], hparams.sos_id)
helper = tf.contrib.seq2seq.GreedyEmbeddingHelper(
embedding_fn, start_tokens, hparams.eos_id)
decoder = tf.contrib.seq2seq.BasicDecoder(
decoder_cell, helper, initial_state, output_layer=projection_layer)
decoder_outputs, final_context_state, final_sequence_length = tf.contrib.seq2seq.dynamic_decode(
decoder, maximum_iterations=maximum_iterations)
return decoder_outputs, final_context_state, final_sequence_length
def build_encoding_model(self):
self.fw_inputs = tf.placeholder(dtype=tf.int32,
shape=(None, None, None),
name='fw_inputs')
self.bw_inputs = tf.reverse_sequence(input=self.fw_inputs,
seq_lengths=self.seq_lens,
seq_axis=0,
batch_axis=1,
name='bw_inputs')
self.fw_char_lens = tf.placeholder(dtype=tf.int32,
shape=[None, None],
name='fw_char_lens')
self.bw_char_lens = tf.reverse_sequence(input=self.fw_char_lens,
seq_lengths=self.seq_lens,
seq_axis=0,
batch_axis=1,
name='bw_char_lens')
self.inputs = self.fw_inputs
self.char_lens = self.fw_char_lens
self.bptt = tf.placeholder(dtype=tf.int32, name='bptt', shape=())
# seq_masks = tf.expand_dims(self.seq_masks, axis=-1)
input_shape = tf.shape(self.inputs)
B = input_shape[1]
fw_model = UniModel(self.rnn_layers,
self.projection_dims,
self.skip_connection,
self.is_training,
self.fine_tune_lr[1:] if isinstance(
self.fine_tune_lr, list) else None,
self.reuse,
'LMFW',
is_cpu=self.is_cpu)
bw_model = UniModel(self.rnn_layers,
self.projection_dims,
self.skip_connection,
self.is_training,
self.fine_tune_lr[1:] if isinstance(
self.fine_tune_lr, list) else None,
self.reuse,
'LMBW',
is_cpu=self.is_cpu)
embed_model = Embedding(self.char_vocab_size, self.char_vec_size,
self.reuse, self.char_cnn_options['layers'],
self.char_cnn_options['n_highways'],
self.projection_dims, self.is_training,
self.drop_e)
embed_model.build()
if isinstance(self.fine_tune_lr, list):
embed_custom_lr = apply_custom_lr(self.fine_tune_lr[0])
else:
def embed_custom_lr(x):
return x
fw_model.build(embed_model.output_shape)
bw_model.build(embed_model.output_shape)
initial_states = []
start_max_vals = []
start_mean_vals = []
start_outputs = []
start_last_outputs = []
start_output_shapes = []
projection_dims = self.projection_dims if isinstance(
self.projection_dims, int) and self.projection_dims > 0 else None
for layer in self.rnn_layers:
if self.is_cpu:
zeros = tf.fill(value=0.0, dims=(B, layer['units']))
initial_states.append(LSTMStateTuple(zeros, zeros))
else:
zeros = tf.fill(value=0.0, dims=(1, B, layer['units']))
initial_states.append((zeros, zeros))
dims = projection_dims if self.projection_dims else layer['units']
max_val = tf.fill(value=-1e6, dims=(B, dims))
mean_val = tf.fill(value=0.0, dims=(B, dims))
start_output = tf.fill(value=0.0, dims=(0, B, dims))
start_max_vals.append(max_val)
start_mean_vals.append(mean_val)
start_last_outputs.append(mean_val)
start_outputs.append(start_output)
start_output_shapes.append(tf.TensorShape((None, None, dims)))
max_len = tf.reduce_max(self.seq_lens)
def cond(i, state, max_vals, mean_vals, all_outputs, last_outputs):
return i < max_len
def body(embed, model, inputs, char_lens, sl, bptt, max_len):
def child(i, state, max_vals, mean_vals, all_outputs,
last_outputs):
i_to = tf.minimum(i + bptt, max_len)
slice_inputs = inputs[i:i_to]
slice_char_lens = char_lens[i:i_to]
slice_inputs = embed.call(slice_inputs, slice_char_lens)
slice_inputs = embed_custom_lr(slice_inputs)
output_dict = model.call(slice_inputs, state)
slice_seq_lens = tf.minimum(sl - i, bptt)
mask = tf.expand_dims(tf.transpose(
tf.sequence_mask(slice_seq_lens, dtype=tf.float32),
(1, 0)),
axis=-1)
next_max_vals = []
next_mean_vals = []
new_all_outputs = []
new_last_outputs = []
for max_val, mean_val, outputs, past_outputs, last_output in zip(
max_vals, mean_vals, output_dict['layer_outputs'][1:],
all_outputs, last_outputs):
max_outputs = outputs * mask + (1 - mask) * -1e6
max_val = tf.maximum(max_val,
tf.reduce_max(max_outputs, axis=0))
mean_outputs = outputs * mask
mean_val = (
mean_val * tf.expand_dims(
tf.to_float(tf.minimum(i, sl)), axis=-1) +
tf.reduce_sum(mean_outputs, axis=0)) / tf.expand_dims(
tf.to_float(tf.minimum(i_to, sl)), axis=-1)
next_max_vals.append(max_val)
next_mean_vals.append(mean_val)
new_all_outputs.append(
tf.concat((past_outputs, mean_outputs), axis=0))
last_val = get_last_output(mean_outputs, slice_seq_lens)
last_val = tf.where(slice_seq_lens > 0, last_val,
last_output)
new_last_outputs.append(last_val)
return i_to, output_dict[
'states'], next_max_vals, next_mean_vals, new_all_outputs, new_last_outputs
return child
start_i = tf.constant(0, dtype=tf.int32, shape=(), name='start_i')
_, _, fw_layerwise_max, fw_layerwise_avg, fw_outputs, fw_last_output = tf.while_loop(
cond,
body(embed_model, fw_model, self.fw_inputs, self.fw_char_lens,
self.seq_lens, self.bptt, max_len), [
start_i, initial_states, start_max_vals, start_mean_vals,
start_outputs, start_last_outputs
],
[
start_i.get_shape(),
[
LSTMStateTuple(x.get_shape(), y.get_shape())
if self.is_cpu else (x.get_shape(), y.get_shape())
for x, y in initial_states
], [x.get_shape() for x in start_max_vals],
[x.get_shape() for x in start_mean_vals], start_output_shapes,
[x.get_shape() for x in start_last_outputs]
],
swap_memory=True)
_, _, bw_layerwise_max, bw_layerwise_avg, bw_outputs, bw_last_output = tf.while_loop(
cond,
body(embed_model, bw_model, self.bw_inputs, self.bw_char_lens,
self.seq_lens, self.bptt, max_len), [
start_i, initial_states, start_max_vals, start_mean_vals,
start_outputs, start_last_outputs
],
[
start_i.get_shape(),
[
LSTMStateTuple(x.get_shape(), y.get_shape())
if self.is_cpu else (x.get_shape(), y.get_shape())
for x, y in initial_states
], [x.get_shape() for x in start_max_vals],
[x.get_shape() for x in start_mean_vals], start_output_shapes,
[x.get_shape() for x in start_last_outputs]
],
swap_memory=True)
self.layerwise_max = [
tf.concat((fw, bw), axis=-1)
for fw, bw in zip(fw_layerwise_max, bw_layerwise_max)
]
self.layerwise_avg = [
tf.concat((fw, bw), axis=-1)
for fw, bw in zip(fw_layerwise_avg, bw_layerwise_avg)
]
self.layerwise_last = [
tf.concat((fw, bw), axis=-1)
for fw, bw in zip(fw_last_output, bw_last_output)
]
self.timewise_outputs = [
tf.concat((fw,
tf.reverse_sequence(input=bw,
seq_lengths=self.seq_lens,
seq_axis=0,
batch_axis=1)),
axis=-1) for fw, bw in zip(fw_outputs, bw_outputs)
]
self.layerwise_encode = [
tf.concat(out, axis=-1)
for out in zip(fw_layerwise_avg, fw_layerwise_max,
bw_layerwise_avg, bw_layerwise_max)
]
def build_encoder(self, encoder_inputs, encoder_input_lengths):
""" Builds an RNN encoder. Can be configured to be uni- / bi- directional.
Can also enable dropout. Returns outputs of the RNN at each timestep and
also the final state """
with tf.variable_scope("encoder"):
# Embeddings for orthographic characters
char_embeddings = tf.Variable(tf.random_uniform(
(self.n_chars, self.embed_dims), -1.0, 1.0),
name="char_embeddings")
encoder_input_embeddings = tf.nn.embedding_lookup(
char_embeddings, encoder_inputs)
# Unidirectional Run
if not self.bidir:
encoder_cell = self.cell_class_fn(self.hidden_dims)
if self.mode == "training":
encoder_cell = DropoutWrapper(
encoder_cell,
input_keep_prob=1.0 - self.dropout,
output_keep_prob=1.0 - self.dropout,
state_keep_prob=1.0 - self.dropout)
encoder_outputs, encoder_final_state = tf.nn.dynamic_rnn(
encoder_cell,
encoder_input_embeddings,
dtype=tf.float32,
time_major=True)
# Bidirectional Run
else:
with tf.variable_scope("fw"):
fw_encoder_cell = self.cell_class_fn(self.hidden_dims)
if self.mode == "training":
fw_encoder_cell = DropoutWrapper(
fw_encoder_cell,
input_keep_prob=1.0 - self.dropout,
output_keep_prob=1.0 - self.dropout,
state_keep_prob=1.0 - self.dropout)
with tf.variable_scope("bw"):
bw_encoder_cell = self.cell_class_fn(self.hidden_dims)
if self.mode == "training":
bw_encoder_cell = DropoutWrapper(
bw_encoder_cell,
input_keep_prob=1.0 - self.dropout,
output_keep_prob=1.0 - self.dropout,
state_keep_prob=1.0 - self.dropout)
((encoder_fw_outputs, encoder_bw_outputs),
(encoder_fw_final_state,
encoder_bw_final_state)) = (tf.nn.bidirectional_dynamic_rnn(
cell_fw=fw_encoder_cell,
cell_bw=bw_encoder_cell,
inputs=encoder_input_embeddings,
sequence_length=encoder_input_lengths,
dtype=tf.float32,
time_major=True))
# Concat final states of forward and backward run
encoder_final_state_c = tf.concat(
(encoder_fw_final_state.c, encoder_bw_final_state.c), 1)
encoder_final_state_h = tf.concat(
(encoder_fw_final_state.h, encoder_bw_final_state.h), 1)
encoder_final_state = LSTMStateTuple(c=encoder_final_state_c,
h=encoder_final_state_h)
encoder_outputs = tf.concat(
(encoder_fw_outputs, encoder_bw_outputs), -1)
return encoder_outputs, encoder_final_state
def build(self):
"""Build the model"""
print("Building the sequence to sequence model ... ")
vocab_size = self.vocab_size
state_size = self.state_size
enc_layers = self.enc_layers
# Placeholders
with tf.name_scope("placeholders"):
enc_inputs = tf.placeholder(tf.int32, [None, None], "enc_inputs")
inp_lens = tf.placeholder(tf.int32, [None], "inp_lens")
self.drop_out = tf.placeholder(tf.float32, (), "drop_out")
self.enc_inputs = enc_inputs
self.inp_lens = inp_lens
if (self.mode == "train"):
dec_inputs = tf.placeholder(tf.int32, [None, None],
"dec_inputs")
targets = tf.placeholder(tf.int32, [None, None], "targets")
out_lens = tf.placeholder(tf.int32, [None], "out_lens")
self.learning_rate = tf.placeholder(tf.float32, (),
"learning_rate")
self.lambda_kl = tf.placeholder(tf.float32, (), "lambda_kl")
self.dec_inputs = dec_inputs
self.targets = targets
self.out_lens = out_lens
batch_size = tf.shape(enc_inputs)[0]
max_len = tf.shape(enc_inputs)[1]
# Embedding
with tf.variable_scope("embeddings"):
embedding_matrix = tf.get_variable(
name="embedding_matrix",
shape=[vocab_size, state_size],
dtype=tf.float32,
initializer=tf.random_normal_initializer(stddev=0.05))
enc_inputs = tf.nn.embedding_lookup(embedding_matrix, enc_inputs)
if (self.mode == "train"):
dec_inputs = tf.nn.embedding_lookup(embedding_matrix,
dec_inputs)
# Encoder
with tf.variable_scope("encoder"):
# TODO: residual LSTM, layer normalization
# if(self.bidirectional)
# enc_cell_fw = [create_cell(
# "enc-fw-%d" % i, state_size, self.drop_out, self.no_residual)
# for i in range(enc_layers)]
# enc_cell_bw = [create_cell(
# "enc-bw-%d" % i, state_size, self.drop_out, self.no_residual)
# for i in range(enc_layers)]
# else:
enc_cell = [
create_cell("enc-%d" % i, state_size, self.drop_out,
self.no_residual) for i in range(enc_layers)
]
enc_cell = tf.nn.rnn_cell.MultiRNNCell(enc_cell)
enc_outputs, enc_state = tf.nn.dynamic_rnn(
enc_cell,
enc_inputs,
sequence_length=inp_lens,
dtype=tf.float32)
# Decoder
with tf.variable_scope("decoder"):
dec_cell = [
create_cell("dec-%d" % i, state_size, self.drop_out,
self.no_residual) for i in range(enc_layers)
]
dec_cell = tf.nn.rnn_cell.MultiRNNCell(dec_cell)
dec_proj = tf.layers.Dense(
vocab_size,
name="dec_proj",
kernel_initializer=tf.random_normal_initializer(stddev=0.05),
bias_initializer=tf.constant_initializer(0.))
# latent code
if (self.vae):
print("Using vae model")
with tf.variable_scope("latent_code"):
enc_mean = tf.reduce_sum(enc_outputs, 1)
enc_mean /= tf.expand_dims(tf.cast(inp_lens, tf.float32), [1])
z_code = enc_mean
if (self.prior == "gaussian"):
print("Gaussian prior")
latent_proj = tf.layers.Dense(
2 * state_size,
name="latent_proj",
kernel_initializer=tf.random_normal_initializer(
stddev=0.05),
bias_initializer=tf.constant_initializer(0.))
z_loc, z_scale = tf.split(latent_proj(z_code),
[state_size, state_size], 1)
z_mvn = tfd.MultivariateNormalDiag(z_loc, z_scale)
z_sample = z_mvn.sample()
elif (self.prior == "vmf"):
# print("vmf prior")
# latent_proj = tf.layers.Dense(state_size + 1, name="latent_proj",
# kernel_initializer=tf.random_normal_initializer(stddev=0.05),
# bias_initializer=tf.constant_initializer(0.))
# z_mu, z_conc = tf.split(
# latent_proj(z_code), [state_size, 1], 1)
# z_mu /= tf.expand_dims(tf.norm(z_mu, axis=1), axis=1)
# z_conc = tf.reshape(z_conc, [batch_size])
# z_vmf = tfd.VonMisesFisher(z_mu, z_conc)
# z_sample = z_vmf.sample()
pass
dec_init_state = (LSTMStateTuple(c=z_sample, h=z_sample),
LSTMStateTuple(c=z_sample, h=z_sample))
else:
print("Using normal seq2seq, no latent variable")
dec_init_state = enc_state
with tf.variable_scope("decoding"):
# greedy decoding
_, dec_outputs_predict = decoding_infer(self.dec_start_id,
dec_cell,
dec_proj,
embedding_matrix,
dec_init_state,
enc_outputs,
batch_size,
max_len,
inp_lens,
max_len,
self.is_attn,
self.sampling_method,
self.topk_sampling_size,
state_size=self.state_size)
# decoding with forward sampling
# dec_outputs_sampling = decodeing_infer() # TBC
if (self.mode == "train"):
# training decoding
dec_logits_train, _, _, _, _ = decoding_train(
dec_inputs, dec_cell, dec_proj, dec_init_state,
enc_outputs, max_len, inp_lens, max_len, self.is_attn,
self.state_size)
all_variables = slim.get_variables_to_restore()
model_variables = [
var for var in all_variables
if var.name.split("/")[0] == self.model_name
]
print("%s model, variable list:" % self.model_name)
for v in model_variables:
print(" %s" % v.name)
self.model_saver = tf.train.Saver(all_variables, max_to_keep=3)
# loss and optimizer
dec_mask = tf.sequence_mask(out_lens,
max_len,
dtype=tf.float32)
dec_loss = tf.contrib.seq2seq.sequence_loss(
dec_logits_train, targets, dec_mask)
if (self.vae):
if (self.prior == "gaussian"):
standard_normal = tfd.MultivariateNormalDiag(
tf.zeros(state_size), tf.ones(state_size))
prior_prob = standard_normal.log_prob(z_sample) # [B]
posterior_prob = z_mvn.log_prob(z_sample) # [B]
kl_loss = tf.reduce_mean(posterior_prob - prior_prob)
loss = dec_loss + self.lambda_kl * kl_loss
elif (self.prior == "vmf"):
# vmf_mu_0 = tf.ones(state_size) / tf.cast(state_size, tf.float32)
# standard_vmf = tfd.VonMisesFisher(vmf_mu_0, 0)
# prior_prob = standard_vmf.log_prob(z_sample) # [B]
# posterior_prob = z_vmf.log_prob(z_sample) # [B]
# kl_loss = tf.reduce_mean(posterior_prob - prior_prob)
# loss = dec_loss + self.lambda_kl * kl_loss
pass
else:
loss = dec_loss
optimizer = tf.train.AdamOptimizer(self.learning_rate)
train_op = optimizer.minimize(loss)
self.train_output = {"train_op": train_op, "loss": loss}
self.train_output.update(self.inspect)
if (self.vae):
self.train_output["dec_loss"] = dec_loss
self.train_output["kl_loss"] = kl_loss
self.valid_output = {"nll": tf.exp(loss)}
self.infer_output = {"dec_predict": dec_outputs_predict}
else:
self.infer_output = {"dec_predict": dec_outputs_predict}
return
def build_graph(self):
print("building generator graph...")
with tf.variable_scope("seq2seq"):
with tf.variable_scope("embedding"):
# shared by encoder and decoder
# self.embedding = tf.get_variable('embedding', [self.vocab_size, self.embedding_size], dtype=tf.float32,
# trainable=True, initializer=self.rand_uni_init)
# using pretrain word vector
self.embedding = tf.get_variable('embedding', [self.vocab_size, self.embedding_size], dtype=tf.float32,
trainable=True, initializer=tf.constant_initializer(self.pretrain_wv))
with tf.variable_scope("encoder"):
topic_embedded = tf.nn.embedding_lookup(self.embedding, self.topic_input)
# encode topic to representation
topic_average = tf.reduce_mean(topic_embedded, axis=1)
topic_state = tf.layers.dense(topic_average, self.hidden_size)
with tf.variable_scope("decoder"):
def _get_cell(_num_units):
cell = tf.contrib.rnn.BasicLSTMCell(_num_units)
if self.training_flag:
cell = tf.contrib.rnn.DropoutWrapper(cell)
return cell
# single layer
self.initial_state = LSTMStateTuple(c=topic_state, h=topic_state)
self.decoder_cell = _get_cell(self.hidden_size)
self.decoder_input_embedded = tf.nn.embedding_lookup(self.embedding, self.target_input)
self.output_layer = layers_core.Dense(self.vocab_size, use_bias=False)
# pre-train with targets #
helper_pt = tf.contrib.seq2seq.TrainingHelper(
inputs=self.decoder_input_embedded,
sequence_length=self.sequence_lengths,
time_major=False,
)
decoder_pt = tf.contrib.seq2seq.BasicDecoder(
cell=self.decoder_cell,
helper=helper_pt,
initial_state=self.initial_state,
output_layer=self.output_layer
)
outputs_pt, _final_state, sequence_lengths_pt = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder_pt,
output_time_major=False,
maximum_iterations=self.max_len,
swap_memory=True,
impute_finished=True
)
self.logits_pt = outputs_pt.rnn_output
self.g_predictions = tf.nn.softmax(self.logits_pt)
masks = tf.sequence_mask(lengths=self.target_len,
maxlen=self.max_len, dtype=tf.float32, name='masks')
# print("target input:", self.target_input.shape)
# print("logits:", self.logits_pt)
self.target_output = tf.placeholder(tf.int32, [None, None])
self.pretrain_loss = tf.contrib.seq2seq.sequence_loss(
self.logits_pt,
self.target_output,
masks,
average_across_timesteps=True,
average_across_batch=True)
self.global_step = tf.Variable(0, trainable=False)
# gradient clipping
optimizer = tf.train.AdamOptimizer(self.learning_rate)
gradients, v = zip(*optimizer.compute_gradients(self.pretrain_loss))
gradients, _ = tf.clip_by_global_norm(gradients, self.grad_norm)
self.pretrain_updates = optimizer.apply_gradients(zip(gradients, v), global_step=self.global_step)
# infer
helper_i = tf.contrib.seq2seq.GreedyEmbeddingHelper(
self.embedding,
tf.fill([self.batch_size], self.vocab_dict['<GO>']),
end_token=self.vocab_dict['<EOS>']
)
decoder_i = tf.contrib.seq2seq.BasicDecoder(
cell=self.decoder_cell,
helper=helper_i,
initial_state=self.initial_state,
output_layer=self.output_layer
)
outputs_i, _final_state_i, sequence_lengths_i = tf.contrib.seq2seq.dynamic_decode(
decoder=decoder_i,
output_time_major=False,
maximum_iterations=self.max_len,
swap_memory=True,
impute_finished=True
)
sample_id = outputs_i.sample_id
self.infer_tokens = tf.unstack(sample_id, axis=0)
print("generator graph built successfully")
def build(self):
"""Build the model"""
print("Building the Latent BOW - sequence to sequence model ... ")
vocab_size = self.vocab_size
key_size = self.key_size
state_size = self.state_size
enc_layers = self.enc_layers
max_enc_bow = self.max_enc_bow
lambda_enc_loss = self.lambda_enc_loss
# Placeholders
with tf.name_scope("placeholders"):
enc_keys = tf.placeholder(tf.int32, [None, None], "enc_keys")
enc_locs = tf.placeholder(tf.int32, [None, None], "enc_locs")
enc_vals = tf.placeholder(tf.int32, [None, None], "enc_vals")
enc_lens = tf.placeholder(tf.int32, [None], "enc_lens")
self.drop_out = tf.placeholder(tf.float32, (), "drop_out")
self.gumbel_tau = tf.placeholder(tf.float32, (), "gumbel_tau")
self.enc_keys = enc_keys
self.enc_locs = enc_locs
self.enc_vals = enc_vals
self.enc_lens = enc_lens
enc_targets = tf.placeholder(tf.int32, [None, None], "enc_targets")
dec_inputs = tf.placeholder(tf.int32, [None, None], "dec_inputs")
dec_targets = tf.placeholder(tf.int32, [None, None], "dec_targets")
dec_lens = tf.placeholder(tf.int32, [None], "dec_lens")
self.enc_targets = enc_targets
self.dec_inputs = dec_inputs
self.dec_targets = dec_targets
self.dec_lens = dec_lens
batch_size = tf.shape(enc_keys)[0]
max_enc_len = tf.shape(enc_keys)[1]
max_dec_len = tf.shape(dec_targets)[1]
# Embedding
with tf.variable_scope("embeddings"):
embedding_matrix_vals = tf.get_variable(
name="embedding_matrix_vals",
shape=[vocab_size, state_size],
dtype=tf.float32,
initializer=tf.random_normal_initializer(stddev=0.05))
embedding_matrix_keys = tf.get_variable(
name="embedding_matrix_keys",
shape=[key_size, state_size],
dtype=tf.float32,
initializer=tf.random_normal_initializer(stddev=0.05))
embedding_matrix_locs = tf.get_variable(
name="embedding_matrix_locs",
shape=[100, state_size],
dtype=tf.float32,
initializer=tf.random_normal_initializer(stddev=0.05))
enc_keys = tf.nn.embedding_lookup(embedding_matrix_keys, enc_keys)
enc_vals = tf.nn.embedding_lookup(embedding_matrix_vals, enc_vals)
enc_locs = tf.nn.embedding_lookup(embedding_matrix_locs, enc_locs)
enc_inputs = (enc_keys + enc_vals + enc_locs) / 3.
dec_inputs = tf.nn.embedding_lookup(embedding_matrix_vals, dec_inputs)
# Encoder
with tf.variable_scope("encoder"):
# TODO: residual LSTM, layer normalization
enc_cell = [create_cell(
"enc-%d" % i, state_size, self.drop_out, self.no_residual)
for i in range(enc_layers)]
enc_cell = tf.nn.rnn_cell.MultiRNNCell(enc_cell)
enc_outputs, enc_state = tf.nn.dynamic_rnn(enc_cell, enc_inputs,
sequence_length=enc_lens, dtype=tf.float32)
# Encoder bow prediction
with tf.variable_scope("bow_output"):
bow_topk_prob, gumbel_topk_prob, seq_neighbor_ind, seq_neighbor_prob = \
bow_predict_seq_tag(vocab_size, batch_size, enc_outputs, enc_lens,
max_enc_len, self.is_gumbel, self.gumbel_tau)
seq_neighbor_output = {"seq_neighbor_ind": seq_neighbor_ind,
"seq_neighbor_prob": seq_neighbor_prob}
# Encoder output, loss and metrics
with tf.name_scope("enc_output"):
# top k prediction
bow_pred_prob, pred_ind = tf.nn.top_k(bow_topk_prob, max_enc_bow)
# loss function
enc_targets = _enc_target_list_to_khot(
enc_targets, vocab_size, self.pad_id)
enc_loss = enc_loss_fn(
self.bow_loss_fn, enc_targets, bow_topk_prob, max_enc_bow)
self.train_output = {"enc_loss": enc_loss}
# performance monitor
bow_metrics_dict = bow_train_monitor(
bow_topk_prob, pred_ind, vocab_size, batch_size, enc_targets)
self.train_output.update(bow_metrics_dict)
# Encoder soft sampling
with tf.name_scope("gumbel_topk_sampling"):
sample_ind, sample_prob, sample_memory = bow_gumbel_topk_sampling(
gumbel_topk_prob, embedding_matrix_vals, self.sample_size, vocab_size)
sample_memory_lens = tf.ones(batch_size, tf.int32) * self.sample_size
sample_memory_avg = tf.reduce_mean(sample_memory, 1) # [B, S]
sample_memory_output = {"bow_pred_ind": pred_ind,
"bow_pred_prob": bow_pred_prob,
"sample_memory_ind": sample_ind,
"sample_memory_prob": sample_prob }
# Decoder
# The initial state of the decoder =
# encoder meaning vector z + encoder bow vector b
with tf.variable_scope("decoder"):
dec_cell = [create_cell(
"dec-%d" % i, state_size, self.drop_out, self.no_residual)
for i in range(enc_layers)]
dec_cell = tf.nn.rnn_cell.MultiRNNCell(dec_cell)
dec_proj = tf.layers.Dense(vocab_size, name="dec_proj",
kernel_initializer=tf.random_normal_initializer(stddev=0.05),
bias_initializer=tf.constant_initializer(0.))
dec_ptr_k_proj = [
tf.layers.Dense(state_size, name="dec_ptr_k_proj_%d" % pi,
kernel_initializer=tf.random_normal_initializer(stddev=0.05),
bias_initializer=tf.constant_initializer(0.))
for pi in range(self.num_pointers)]
dec_ptr_g_proj = tf.layers.Dense(1, name="dec_ptr_g_proj",
kernel_initializer=tf.random_normal_initializer(stddev=0.05),
bias_initializer=tf.constant_initializer(0.),
activation=tf.nn.sigmoid)
bow_cond_gate_proj = tf.layers.Dense(1, name="bow_cond_gate_proj",
kernel_initializer=tf.random_normal_initializer(stddev=0.05),
bias_initializer=tf.constant_initializer(0.),
activation=tf.nn.sigmoid)
dec_init_state = []
for l in range(enc_layers):
dec_init_state.append(LSTMStateTuple(c=enc_state[0].c,
h=enc_state[0].h + sample_memory_avg))
dec_init_state = tuple(dec_init_state)
# if(enc_layers == 2):
# dec_init_state = (LSTMStateTuple( c=enc_state[0].c,
# h=enc_state[0].h + sample_memory_avg),
# LSTMStateTuple( c=enc_state[1].c,
# h=enc_state[1].h + sample_memory_avg) )
# elif(enc_layers == 4):
# dec_init_state = (LSTMStateTuple(c=enc_state[0].c,
# h=enc_state[0].h + sample_memory_avg),
# LSTMStateTuple( c=enc_state[1].c,
# h=enc_state[1].h + sample_memory_avg) )
# else: raise Exception('enc_layers not in [2, 4]')
if(self.source_attn):
# [B, M + T, S]
dec_memory = [sample_memory, enc_outputs]
dec_mem_len = [sample_memory_lens, enc_lens]
dec_max_mem_len = [self.sample_size, max_enc_len]
else:
dec_memory = sample_memory
dec_mem_len = sample_memory_lens
dec_max_mem_len = tf.shape(dec_memory)[1]
if(self.bow_cond): bow_cond = sample_memory_avg
else: bow_cond = None
if(self.bow_cond_gate == False): bow_cond_gate_proj = None
(dec_outputs_predict, dec_logits_train, dec_prob_train, pointer_ent,
avg_max_ptr, avg_num_copy) = decode(
self.dec_start_id, dec_inputs,
dec_cell, dec_proj, embedding_matrix_vals,
dec_init_state, dec_memory, dec_mem_len, dec_max_mem_len,
batch_size, max_dec_len, self.sampling_method, self.topk_sampling_size,
state_size, multi_source=True, copy=self.copy, copy_ind=sample_ind,
dec_ptr_g_proj=dec_ptr_g_proj, dec_ptr_k_proj=dec_ptr_k_proj,
bow_cond=bow_cond, bow_cond_gate_proj=bow_cond_gate_proj)
# model saver, before the optimizer
all_variables = slim.get_variables_to_restore()
model_variables = [var for var in all_variables
if var.name.split("/")[0] == self.model_name]
print("%s model, variable list:" % self.model_name)
for v in model_variables: print(" %s" % v.name)
self.model_saver = tf.train.Saver(model_variables, max_to_keep=3)
with tf.variable_scope("optimizer"):
optimizer = tf.train.AdamOptimizer(self.learning_rate)
# decoder output, training and inference, combined with encoder loss
with tf.name_scope("dec_output"):
dec_mask = tf.sequence_mask(dec_lens, max_dec_len, dtype=tf.float32)
if(self.copy == False):
dec_loss = tf.contrib.seq2seq.sequence_loss(
dec_logits_train, dec_targets, dec_mask)
else:
dec_loss = _copy_loss(dec_prob_train, dec_targets, dec_mask)
loss = dec_loss + lambda_enc_loss * enc_loss
train_op = optimizer.minimize(loss)
dec_output = {"train_op": train_op, "dec_loss": dec_loss, "loss": loss}
self.train_output.update(dec_output)
if(self.copy):
pointer_ent =\
tf.reduce_sum(pointer_ent * dec_mask) / tf.reduce_sum(dec_mask)
self.train_output['pointer_ent'] = pointer_ent
avg_max_ptr =\
tf.reduce_sum(avg_max_ptr * dec_mask) / tf.reduce_sum(dec_mask)
self.train_output['avg_max_ptr'] = avg_max_ptr
avg_num_copy = tf.reduce_sum(avg_num_copy * dec_mask, 1)
avg_num_copy = tf.reduce_mean(avg_num_copy)
self.train_output['avg_num_copy'] = avg_num_copy
self.infer_output = {"dec_predict": dec_outputs_predict}
dec_out_mem_ratio = _calculate_dec_out_mem_ratio(dec_outputs_predict,
sample_ind, vocab_size, self.pad_id, self.dec_start_id, self.dec_end_id)
self.infer_output.update(dec_out_mem_ratio)
self.infer_output.update(sample_memory_output)
self.infer_output.update(seq_neighbor_output)
return
def call(self, inputs, state):
sigmoid = math_ops.sigmoid
# Parameters of gates are concatenated into one multiply for efficiency.
if self._state_is_tuple:
c, h = state
else:
c, h = array_ops.split(value=state, num_or_size_splits=2, axis=1)
concat = _linear([inputs, h], 4 * self._num_units + 2 * self.n_chunk,
True)
# i = input_gate, j = new_input, f = forget_gate, o = output_gate
f_master_t = concat[:, :self.n_chunk]
# print(f_master_t.eval(session=self.sess))
# plt.plot(f_master_t[:, 0].eval(), f_master_t[0, :])
# plt.show()
# tf.summary.scalar("f_before", f_master_t)
# tf.summary.histogram("f_before", f_master_t)
#f_master_t = tf.nn.softmax(f_master_t , axis=-1)
#f_master_t = self.differentiable_gumbel_sample(f_master_t , axis=-1, temperature=5)
# tf.summary.histogram("f_act" , f_master_t)
f_master_t = self.cumsum(f_master_t)
# tf.summary.histogram("f_after", f_master_t)
f_master_t = tf.expand_dims(f_master_t, 2)
i_master_t = concat[:, self.n_chunk:2 * self.n_chunk]
#i_master_t = tf.nn.softmax(i_master_t , axis=-1)
#i_master_t = self.differentiable_gumbel_sample(i_master_t , axis=-1, temperature=5)
# tf.summary.histogram("i_act" , i_master_t)
i_master_t = self.cumsum(i_master_t, 'left')
# tf.summary.histogram("i_after", i_master_t)
i_master_t = tf.expand_dims(i_master_t, 2)
concat = concat[:, 2 * self.n_chunk:]
concat = tf.reshape(concat, [-1, self.n_chunk * 4, self.chunk_size])
f_t = tf.nn.sigmoid(concat[:, :self.n_chunk])
i_t = tf.nn.sigmoid(concat[:, self.n_chunk:2 * self.n_chunk])
o_t = tf.nn.sigmoid(concat[:, 2 * self.n_chunk:3 * self.n_chunk])
c_t_hat = tf.tanh(concat[:, 3 * self.n_chunk:])
w_t = f_master_t * i_master_t
new_c = w_t * (f_t * tf.reshape(c , [-1 , self.n_chunk , self.chunk_size]) + i_t * c_t_hat) + \
(i_master_t - w_t) * c_t_hat + \
(f_master_t - w_t) * tf.reshape(c , [-1 , self.n_chunk , self.chunk_size])
new_h = tf.tanh(new_c) * o_t
new_c = tf.reshape(new_c, [-1, self._num_units])
new_h = tf.reshape(new_h, [-1, self._num_units])
# i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
# new_c = (
# c * sigmoid(f + self._forget_bias) + sigmoid(i) * self._activation(j))
# new_h = self._activation(new_c) * sigmoid(o)
if self._state_is_tuple:
new_state = LSTMStateTuple(new_c, new_h)
else:
new_state = array_ops.concat([new_c, new_h], 1)
return new_h, new_state
def build(self):
"""Build the model """
print("Building the bow - sequence to sequence model ... ")
vocab_size = self.vocab_size
state_size = self.state_size
enc_layers = self.enc_layers
max_enc_bow = self.max_enc_bow
num_paraphrase = self.num_paraphrase
# Placeholders
with tf.name_scope("placeholders"):
enc_inputs = tf.placeholder(tf.int32, [None, None], "enc_inputs")
enc_lens = tf.placeholder(tf.int32, [None], "enc_lens")
self.drop_out = tf.placeholder(tf.float32, (), "drop_out")
self.max_len = tf.placeholder(tf.int32, (), "max_len")
dec_bow = tf.placeholder(tf.int32, [None, None], "dec_bow")
dec_bow_len = tf.placeholder(tf.int32, [None], "dec_bow_len")
self.enc_inputs = enc_inputs
self.enc_lens = enc_lens
self.dec_bow = dec_bow
self.dec_bow_len = dec_bow_len
if(self.mode == "train"):
enc_targets = tf.placeholder(tf.int32, [None, None], "enc_targets")
enc_seq2seq_inputs = tf.placeholder(
tf.int32, [None, num_paraphrase, None], "enc_seq2seq_inputs")
enc_seq2seq_targets = tf.placeholder(
tf.int32, [None, num_paraphrase, None], "enc_seq2seq_targets")
enc_seq2seq_lens = tf.placeholder(
tf.int32, [None, num_paraphrase], "enc_seq2seq_lens")
dec_inputs = tf.placeholder(tf.int32, [None, None], "dec_inputs")
dec_targets = tf.placeholder(tf.int32, [None, None], "dec_targets")
dec_lens = tf.placeholder(tf.int32, [None], "dec_lens")
self.enc_targets = enc_targets
self.enc_seq2seq_inputs = enc_seq2seq_inputs
self.enc_seq2seq_targets = enc_seq2seq_targets
self.enc_seq2seq_lens = enc_seq2seq_lens
self.dec_inputs = dec_inputs
self.dec_targets = dec_targets
self.dec_lens = dec_lens
enc_batch_size = tf.shape(enc_inputs)[0]
max_len = self.max_len
dec_batch_size = tf.shape(dec_bow)[0]
max_dec_bow = tf.shape(dec_bow)[1]
# Embedding
with tf.variable_scope("embeddings"):
embedding_matrix = tf.get_variable(
name="embedding_matrix",
shape=[vocab_size, state_size],
dtype=tf.float32,
initializer=tf.random_normal_initializer(stddev=0.05))
enc_inputs = tf.nn.embedding_lookup(embedding_matrix, enc_inputs)
if(self.mode == "train"):
dec_inputs = tf.nn.embedding_lookup(embedding_matrix, dec_inputs)
dec_bow = tf.nn.embedding_lookup(embedding_matrix, dec_bow)
# Encoder
with tf.variable_scope("encoder"):
# TODO: residual LSTM, layer normalization
enc_cell = [create_cell("enc-%d" % i, state_size, self.drop_out)
for i in range(enc_layers)]
enc_cell = tf.nn.rnn_cell.MultiRNNCell(enc_cell)
enc_outputs, enc_state = tf.nn.dynamic_rnn(enc_cell, enc_inputs,
sequence_length=enc_lens, dtype=tf.float32)
# Encoder bow prediction
with tf.variable_scope("bow_output"):
if(self.bow_pred_method == "mix_softmax"):
bow_topk_prob = bow_predict_mix_softmax(
enc_batch_size, vocab_size, max_enc_bow, enc_state)
elif(self.bow_pred_method == "seq_tag"):
bow_topk_prob, _, _, _ = bow_predict_seq_tag(
vocab_size, enc_batch_size, enc_outputs, enc_lens, max_len)
elif(self.bow_pred_method == "seq2seq"):
bow_topk_prob, enc_seq2seq_loss, enc_infer_pred = \
bow_predict_seq2seq(enc_seq2seq_inputs,
enc_seq2seq_targets,
enc_seq2seq_lens,
embedding_matrix,
enc_outputs,
enc_state,
enc_layers,
num_paraphrase,
max_len,
enc_lens,
enc_batch_size,
vocab_size,
state_size,
self.drop_out,
self.dec_start_id)
with tf.variable_scope("enc_optimizer"):
enc_optimizer = tf.train.AdamOptimizer(self.learning_rate_enc)
with tf.name_scope("enc_output"):
# top k prediction
pred_prob, pred_ind = tf.nn.top_k(bow_topk_prob, max_enc_bow)
pred_prob_unnorm = pred_prob
pred_prob /= tf.expand_dims(tf.reduce_sum(pred_prob, axis=1), [1])
pred_prob_dec, pred_ind_dec = tf.nn.top_k(bow_topk_prob, self.sample_size)
pred_prob_dec /= tf.expand_dims(tf.reduce_sum(pred_prob_dec, axis=1), [1])
if(self.mode == "train"):
with tf.name_scope("enc_loss"):
# loss function
enc_targets = _enc_target_list_to_khot(
enc_targets, vocab_size, self.pad_id)
enc_bow_loss = enc_loss_fn(
self.bow_loss_fn, enc_targets, bow_topk_prob, max_enc_bow)
if(self.bow_pred_method == "seq2seq"):
# pure sequence to sequence for now
enc_loss = enc_seq2seq_loss + 0.0 * enc_bow_loss
else:
enc_loss = enc_bow_loss
enc_train_op = enc_optimizer.minimize(enc_loss)
# prediction preformance monitor during training
# write this in a function
# TODO: top 10 recall
with tf.name_scope("train_output"):
# encoder training output
self.enc_train_output = { "enc_train_op": enc_train_op,
"enc_bow_loss": enc_bow_loss,
"enc_loss": enc_loss}
bow_metrics_dict = bow_train_monitor(
bow_topk_prob, pred_ind, vocab_size, enc_batch_size, enc_targets)
self.enc_train_output.update(bow_metrics_dict)
if(self.bow_pred_method == "seq2seq"):
self.enc_train_output["enc_seq2seq_loss"] = enc_seq2seq_loss
# encoder inference output
with tf.name_scope("infer_output"):
if(self.bow_pred_method == "seq2seq"):
(infer_overlap, infer_pred_support, infer_target_support, infer_prec,
infer_recl) = bow_seq2seq_metrics(
enc_targets, enc_infer_pred, vocab_size, self.pad_id)
self.enc_infer_output = {
"enc_infer_overlap": infer_overlap,
"enc_infer_pred_support": infer_pred_support,
"enc_infer_target_support": infer_target_support,
"enc_infer_precision": infer_prec,
"enc_infer_recall": infer_recl,
"enc_infer_pred": enc_infer_pred}
else:
self.enc_infer_output = { "pred_prob": pred_prob,
"pred_ind": pred_ind,
"pred_prob_dec": pred_prob_dec,
"pred_ind_dec": pred_ind_dec}
# Decoder bow encoding
# TODO: sampling from encoder topk prediction
with tf.variable_scope("dec_bow_encoding"):
dec_bow_mask = tf.expand_dims(
tf.sequence_mask(dec_bow_len, max_dec_bow, dtype=tf.float32), [2])
# TODO: transformer based encoding, but our primary goal is to test the
# effectiveness of sampling, so we skip it for now
dec_bow_enc = tf.reduce_mean(dec_bow_mask * dec_bow, axis = 1) # [B, S]
with tf.variable_scope("decoder"):
dec_cell = [create_cell("dec-%d" % i, state_size, self.drop_out)
for i in range(enc_layers)]
dec_cell = tf.nn.rnn_cell.MultiRNNCell(dec_cell)
dec_init_state = (LSTMStateTuple(dec_bow_enc, dec_bow_enc),
LSTMStateTuple(dec_bow_enc, dec_bow_enc))
dec_proj = tf.layers.Dense(vocab_size, name="dec_proj",
kernel_initializer=tf.random_normal_initializer(stddev=0.05),
bias_initializer=tf.constant_initializer(0.))
dec_memory = dec_bow
dec_mem_len = dec_bow_len
dec_max_mem_len = max_dec_bow
# greedy decoding
# _, dec_outputs_predict = decoding_infer(self.dec_start_id,
# dec_cell,
# dec_proj,
# embedding_matrix,
# dec_init_state,
# dec_bow,
# dec_batch_size,
# max_len,
# dec_bow_len,
# max_dec_bow,
# self.is_attn)
# if(self.mode == "train"):
# # training decoding
# dec_outputs_train = decoding_train( dec_inputs,
# dec_cell,
# dec_init_state,
# dec_bow,
# max_len,
# dec_bow_len,
# max_dec_bow,
# self.is_attn)
# dec_logits_train = dec_proj(dec_outputs_train)
dec_outputs_predict, dec_logits_train = decode(
self.dec_start_id, dec_inputs,
dec_cell, dec_proj, embedding_matrix,
dec_init_state, dec_memory, dec_mem_len, dec_max_mem_len,
dec_batch_size, max_len, self.sampling_method, self.topk_sampling_size,
state_size, multi_source=False)
all_variables = slim.get_variables_to_restore()
model_variables = [var for var in all_variables
if var.name.split("/")[0] == self.model_name]
print("%s model, variable list:" % self.model_name)
for v in model_variables: print(" %s" % v.name)
self.model_saver = tf.train.Saver(model_variables, max_to_keep=3)
with tf.variable_scope("dec_optimizer"):
dec_optimizer = tf.train.AdamOptimizer(self.learning_rate_dec)
with tf.name_scope("dec_output"):
if(self.mode == "train"):
dec_mask = tf.sequence_mask(dec_lens, max_len, dtype=tf.float32)
dec_loss = tf.contrib.seq2seq.sequence_loss(
dec_logits_train, dec_targets, dec_mask)
dec_train_op = dec_optimizer.minimize(dec_loss)
self.dec_train_output = { "dec_train_op": dec_train_op,
"dec_loss": dec_loss}
self.dec_infer_output = {"dec_predict": dec_outputs_predict}
return
tf.nn.rnn_cell.LSTMCell(
num_units=no_units,
initializer=tf.keras.initializers.glorot_normal(),
state_is_tuple=True)
]))
LSTM_outputs, LSTM_fw_state, LSTM_bw_state = tf.contrib.rnn.stack_bidirectional_dynamic_rnn(
cells_fw=LSTM_fw, cells_bw=LSTM_bw, inputs=train, dtype=tf.float32)
# LSTM_outputs = tf.concat((LSTM_fw_output, LSTM_bw_output), 2)
LSTM_state_c = tf.concat((LSTM_fw_state[-1][0].c, LSTM_bw_state[-1][0].c), 1)
LSTM_state_h = tf.concat((LSTM_fw_state[-1][0].h, LSTM_bw_state[-1][0].h), 1)
LSTM_final_state = LSTMStateTuple(c=LSTM_state_c, h=LSTM_state_h)
output = tf.layers.Dense(2)(LSTM_state_h)
#Defining Loss function
losss = tf.losses.softmax_cross_entropy(target, output)
trainop = tf.train.AdamOptimizer(learning_rate=0.001).minimize(losss)
# losx=[]
saver = tf.train.Saver()
# # with tf.device('/gp):
# # with tf.device('/gpu:0'):
# with tf.Session() as sess:
# sess.run(tf.global_variables_initializer())
