def __init__(self):
# set log level to debug
tf.sg_verbosity(10)
# batch size
self.batch_size = 1
# vocabulary size
self.voca_size = sttwdata.voca_size
# mfcc feature of audio
self.x = tf.placeholder(dtype=tf.sg_floatx,
shape=(self.batch_size, None, 20))
# encode audio feature
self.logit = get_logit(self.x, voca_size=self.voca_size)
# sequence length except zero-padding
self.seq_len = tf.not_equal(self.x.sg_sum(axis=2),
0.).sg_int().sg_sum(axis=1)
# run network
self.session = tf.Session()
tf.sg_init(self.session)
self.saver = tf.train.Saver()
self.saver.restore(self.session,
tf.train.latest_checkpoint('asset/train'))
def sg_reverse_seq(tensor, opt):
r"""Reverses variable length slices.
Before applying the pure tensorflow function tf.reverse_sequence,
this function calculates sequence lengths by counting non-zeros.
For example,
```
tensor = [[1, 2, 3, 0, 0], [4, 5, 0, 0, 0]]
tensor.sg_reverse_seq()
=> [[3 2 1 0 0]
[5 4 0 0 0]]
```
Args:
tensor: A 2-D `Tensor` (automatically given by chain).
opt:
dim: Dimension to reverse. Default is 1.
name : If provided, it replaces current tensor's name.
Returns:
A `Tensor` with the same shape and type as `tensor`.
"""
# default sequence dimension
opt += tf.sg_opt(dim=1)
seq_len = tf.not_equal(tensor,
tf.zeros_like(tensor)).sg_int().sg_sum(dims=opt.dim)
return tf.reverse_sequence(tensor, seq_len, opt.dim, name=opt.name)
def init_model():
global x, y
# set log level to debug
tf.sg_verbosity(10)
#
# hyper parameters
#
batch_size = 1 # batch size
#
# inputs
#
# vocabulary size
voca_size = data.voca_size
# print(voca_size)
# mfcc feature of audio
x = tf.placeholder(dtype=tf.sg_floatx, shape=(batch_size, None, 20))
# sequence length except zero-padding
seq_len = tf.not_equal(x.sg_sum(axis=2), 0.).sg_int().sg_sum(axis=1)
# encode audio feature
logit = get_logit(x, voca_size=voca_size)
# ctc decoding
decoded, _ = tf.nn.ctc_beam_search_decoder(
logit.sg_transpose(perm=[1, 0, 2]), seq_len, merge_repeated=False)
# to dense tensor
y = tf.sparse_to_dense(decoded[0].indices, decoded[0].dense_shape,
decoded[0].values) + 1
def __init__(self, mode="train"):
'''
Args:
mode: A string. Either "train" or "test"
'''
self.char2idx, self.idx2char = load_char_vocab()
self.word2idx, self.idx2word = load_word_vocab()
if mode == "train":
self.x, self.y, self.num_batch = get_batch_data()
else:
self.x = tf.placeholder(tf.int32, [None, Hyperparams.seqlen])
self.emb_x = tf.sg_emb(name='emb_x',
voca_size=len(self.char2idx),
dim=Hyperparams.embed_dim)
self.enc = self.x.sg_lookup(emb=self.emb_x)
with tf.sg_context(size=5, act='relu', bn=True):
for _ in range(20):
dim = self.enc.get_shape().as_list()[-1]
self.enc += self.enc.sg_conv1d(
dim=dim) # (64, 50, 300) float32
self.enc = self.enc.sg_conv1d(size=1,
dim=len(self.word2idx),
act='linear',
bn=False) # (64, 50, 21293) float32
# self.logits = self.enc.sg_mean(dims=[1], keep_dims=False) # (64, 21293) float32
# Weighted Sum. Updated on Feb. 15, 2017.
def make_weights(size):
weights = tf.range(1, size + 1, dtype=tf.float32)
weights *= 1. / ((1 + size) * size // 2)
weights = tf.expand_dims(weights, 0)
weights = tf.expand_dims(weights, -1)
return weights
self.weights = make_weights(Hyperparams.seqlen) # (1, 50, 1)
self.enc *= self.weights # Broadcasting
self.logits = self.enc.sg_sum(axis=[1], keep_dims=False) # (64, 21293)
if mode == "train":
self.ce = self.logits.sg_ce(target=self.y,
mask=False,
one_hot=False)
self.istarget = tf.not_equal(self.y, tf.ones_like(
self.y)).sg_float() # 1: Unkown
self.reduced_loss = ((self.ce * self.istarget).sg_sum()) / (
self.istarget.sg_sum() + 1e-5)
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
def sg_ce(tensor, opt):
r"""Returns softmax cross entropy loss between `tensor` and `target`.
Args:
tensor: A `Tensor`. Logits. Unscaled log probabilities.
opt:
target: A `Tensor` with the same length in the first dimension as the `tensor`. Labels.
one_hot: Boolean. Whether to treat the labels as one-hot encoding. Default is False.
mask: Boolean. If True, zeros in the target will be excluded from the calculation.
name: A `string`. A name to display in the tensor board web UI.
Returns:
A 1-D `Tensor` with the same shape as `tensor`.
For example,
```
tensor = [[[2, -1, 3], [3, 1, -2]]]
target = [[2, 1]]
tensor.sg_ce(target=target) => [[ 0.32656264 2.13284516]]
```
For example,
```
tensor = [[2, -1, 3], [3, 1, -2]]
target = [[0, 0, 1], [1, 0, 0]]
tensor.sg_ce(target=target, one_hot=True) => [ 0.32656264 0.13284527]
```
"""
opt += tf.sg_opt(one_hot=False)
assert opt.target is not None, 'target is mandatory.'
if opt.one_hot:
out = tf.identity(
tf.nn.softmax_cross_entropy_with_logits(labels=opt.target,
logits=tensor), 'ce')
else:
out = tf.identity(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=opt.target,
logits=tensor),
'ce')
# masking loss
if opt.mask:
out *= tf.not_equal(opt.target, tf.zeros_like(opt.target)).sg_float()
# add summary
tf.sg_summary_loss(out, name=opt.name)
return out
def sg_ce(tensor, opt):
opt += tf.sg_opt(one_hot=False)
assert opt.target is not None, 'target is mandatory.'
if opt.one_hot:
out = tf.identity(tf.nn.softmax_cross_entropy_with_logits(tensor, opt.target), 'ce')
else:
out = tf.identity(tf.nn.sparse_softmax_cross_entropy_with_logits(tensor, opt.target), 'ce')
# masking loss
if opt.mask:
out *= tf.not_equal(opt.target, tf.zeros_like(opt.target)).sg_float()
# add summary
tf.sg_summary_loss(out)
return out
def sg_to_sparse(tensor, opt):
r"""Converts a dense tensor into a sparse tensor.
See `tf.SparseTensor()` in tensorflow.
Args:
tensor: A `Tensor` with zero-padding (automatically given by chain).
opt:
name: If provided, replace current tensor's name.
Returns:
A `SparseTensor`.
"""
indices = tf.where(tf.not_equal(tensor.sg_float(), 0.))
return tf.SparseTensor(indices=indices,
values=tf.gather_nd(tensor, indices) - 1, # for zero-based index
dense_shape=tf.shape(tensor).sg_cast(dtype=tf.int64))
def __init__(self, mode="train"):
'''
Args:
is_train: Boolean. If True, backprop is executed.
'''
if mode == "train":
self.x, self.y, self.num_batch = get_batch_data(
) # (64, 50) int64, (64, 50) int64, 1636
else: # test
self.x = tf.placeholder(tf.int64, [None, Hyperparams.maxlen])
# make embedding matrix for input characters
pnyn2idx, _, hanzi2idx, _ = load_vocab()
self.emb_x = tf.sg_emb(name='emb_x',
voca_size=len(pnyn2idx),
dim=Hyperparams.embed_dim)
self.enc = self.x.sg_lookup(emb=self.emb_x)
with tf.sg_context(size=5, act='relu', bn=True):
for _ in range(20):
dim = self.enc.get_shape().as_list()[-1]
self.enc += self.enc.sg_conv1d(
dim=dim) # (64, 50, 300) float32
# final fully convolutional layer for softmax
self.logits = self.enc.sg_conv1d(size=1,
dim=len(hanzi2idx),
act='linear',
bn=False) # (64, 50, 5072) float32
if mode == "train":
self.ce = self.logits.sg_ce(target=self.y,
mask=True) # (64, 50) float32
self.istarget = tf.not_equal(self.y, tf.zeros_like(
self.y)).sg_float() # (64, 50) float32
self.reduced_loss = self.ce.sg_sum() / self.istarget.sg_sum(
) # () float32
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
def __init__(self, is_train=True):
'''
Args:
is_train: Boolean. If True, backprop is executed.
'''
if is_train:
self.x, self.y = get_batch_data() # (16, 100), (16, 100)
else:
# self.x = tf.placeholder(tf.int32, [Hyperparams.batch_size, Hyperparams.maxlen])
self.x = tf.placeholder(tf.int32, [None, Hyperparams.maxlen])
# make embedding matrix for input characters
hangul2idx, _, hanja2idx, _ = load_charmaps()
self.emb_x = tf.sg_emb(name='emb_x', voca_size=len(hangul2idx), dim=Hyperparams.hidden_dim)
# embed table lookup
self.enc = self.x.sg_lookup(emb=self.emb_x).sg_float() # (16, 100, 200)
# loop dilated conv block
for i in range(2):
self.enc = (self.enc
.sg_res_block(size=5, rate=1)
.sg_res_block(size=5, rate=2)
.sg_res_block(size=5, rate=4)
.sg_res_block(size=5, rate=8)
.sg_res_block(size=5, rate=16))
# final fully convolutional layer for softmax
self.logits = self.enc.sg_conv1d(size=1, dim=len(hanja2idx)) # (16, 100, 4543)
if is_train:
self.ce = self.logits.sg_ce(target=self.y, mask=True) # (16, 100)
self.nonzeros = tf.not_equal(self.y, tf.zeros_like(self.y)).sg_float() # (16, 100)
self.reduced_loss = self.ce.sg_sum() / self.nonzeros.sg_sum() # ()
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
def sg_reverse_seq(tensor, opt):
# default sequence dimension
opt += tf.sg_opt(dim=1)
seq_len = tf.not_equal(tensor,
tf.zeros_like(tensor)).sg_int().sg_sum(dims=opt.dim)
return tf.reverse_sequence(tensor, seq_len, opt.dim, name=opt.name)
batch_size = 16 # # inputs # # corpus input tensor ( with QueueRunner ) data = SpeechCorpus(batch_size=batch_size, set_name=tf.sg_arg().set) # mfcc feature of audio x = data.mfcc # target sentence label y = data.label # sequence length except zero-padding seq_len = tf.not_equal(x.sg_sum(axis=2), 0.).sg_int().sg_sum(axis=1) # # Testing Graph # # encode audio feature logit = get_logit(x, voca_size=voca_size) # CTC loss loss = logit.sg_ctc(target=y, seq_len=seq_len) # # run network #
# inputs
#
# corpus input tensor
data = SpeechCorpus(batch_size=batch_size * tf.sg_gpus())
# mfcc feature of audio
inputs = tf.split(data.mfcc, tf.sg_gpus(), axis=0)
# target sentence label
labels = tf.split(data.label, tf.sg_gpus(), axis=0)
# sequence length except zero-padding
seq_len = []
for input_ in inputs:
seq_len.append(
tf.not_equal(input_.sg_sum(axis=2), 0.).sg_int().sg_sum(axis=1))
# parallel loss tower
@tf.sg_parallel
def get_loss(opt):
# encode audio feature
logit = get_logit(opt.input[opt.gpu_index], voca_size=voca_size)
# CTC loss
return logit.sg_ctc(target=opt.target[opt.gpu_index],
seq_len=opt.seq_len[opt.gpu_index])
#
# train
#
import sugartensor as tf
import numpy as np
import librosa
import tensorflow as tfw
from tensorflow.python.framework import graph_util
from model import *
import data
batch_size = 1 # batch size
voca_size = data.voca_size
x = tf.placeholder(dtype=tf.sg_floatx, shape=(batch_size, None, 20))
# sequence length except zero-padding
seq_len = tf.not_equal(x.sg_sum(axis=2), 0.).sg_int().sg_sum(axis=1)
# encode audio feature
logit = get_logit(x, voca_size)
# ctc decoding
decoded, _ = tf.nn.ctc_beam_search_decoder(logit.sg_transpose(perm=[1, 0, 2]), seq_len, merge_repeated=False)
# to dense tensor
y = tf.add(tf.sparse_to_dense(decoded[0].indices, decoded[0].dense_shape, decoded[0].values), 1, name="output")
with tf.Session() as sess:
tf.sg_init(sess)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('asset/train'))
graph = tf.get_default_graph()
input_graph_def = graph.as_graph_def()
#
# inputs
#
# corpus input tensor
data = SpeechCorpus(batch_size=batch_size * tf.sg_gpus())
# mfcc feature of audio
inputs = tf.split(data.mfcc, tf.sg_gpus(), axis=0)
# target sentence label
labels = tf.split(data.label, tf.sg_gpus(), axis=0)
# sequence length except zero-padding
seq_len = []
for input_ in inputs:
seq_len.append(tf.not_equal(input_.sg_sum(axis=2), 0.).sg_int().sg_sum(axis=1))
# parallel loss tower
@tf.sg_parallel
def get_loss(opt):
# encode audio feature
logit = get_logit(opt.input[opt.gpu_index], voca_size=voca_size)
# CTC loss
return logit.sg_ctc(target=opt.target[opt.gpu_index], seq_len=opt.seq_len[opt.gpu_index])
#
# train
#
tf.sg_train(lr=0.0001, loss=get_loss(input=inputs, target=labels, seq_len=seq_len),
ep_size=data.num_batch, max_ep=50)
def __init__(self, mode="train"):
# Inputs and Labels
if mode == "train":
self.x, self.y, self.num_batch = get_batch_data(
) # (16, 150) int32, (16, 150) int32, int
self.y_src = tf.concat(
[tf.zeros((Hp.batch_size, 1), tf.int32), self.y[:, :-1]],
1) # (16, 150) int32
else: # inference
self.x = tf.placeholder(tf.int32, shape=(Hp.batch_size, Hp.maxlen))
self.y_src = tf.placeholder(tf.int32,
shape=(Hp.batch_size, Hp.maxlen))
# Load vocabulary
char2idx, idx2char = load_vocab()
# Embedding
emb_x = tf.sg_emb(name='emb_x',
voca_size=len(char2idx),
dim=Hp.hidden_units) # (179, 320)
emb_y = tf.sg_emb(name='emb_y',
voca_size=len(char2idx),
dim=Hp.hidden_units) # (179, 320)
X = self.x.sg_lookup(emb=emb_x) # (16, 150, 320)
Y = self.y_src.sg_lookup(emb=emb_y) # (16, 150, 320)
# Encoding
conv = X.sg_quasi_conv1d(is_enc=True, size=6) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H_zfo1 = pool[Hp.batch_size:] # (16*3, 15, 320) for decoding
conv = pool.sg_quasi_conv1d(is_enc=True, size=2) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H_zfo2 = pool[Hp.batch_size:] # (16*3, 150, 320) for decoding
conv = pool.sg_quasi_conv1d(is_enc=True, size=2) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H_zfo3 = pool[Hp.batch_size:] # (16*3, 150, 320) for decoding
conv = pool.sg_quasi_conv1d(is_enc=True, size=2) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H4 = pool[:Hp.batch_size] # (16, 150, 320) for decoding
H_zfo4 = pool[Hp.batch_size:] # (16*3, 150, 320) for decoding
# Decoding
d_conv = (Y.sg_concat(target=H_zfo1,
axis=0).sg_quasi_conv1d(is_enc=False, size=2))
d_pool = d_conv.sg_quasi_rnn(is_enc=False,
att=False) # (16*4, 150, 320)
d_conv = (d_pool.sg_concat(target=H_zfo2,
axis=0).sg_quasi_conv1d(is_enc=False,
size=2))
d_pool = d_conv.sg_quasi_rnn(is_enc=False,
att=False) # (16*4, 150, 320)
d_conv = (d_pool.sg_concat(target=H_zfo3,
axis=0).sg_quasi_conv1d(is_enc=False,
size=2))
d_pool = d_conv.sg_quasi_rnn(is_enc=False,
att=False) # (16*4, 150, 320)
d_conv = (d_pool.sg_concat(target=H_zfo4,
axis=0).sg_quasi_conv1d(is_enc=False,
size=2))
concat = H4.sg_concat(target=d_conv, axis=0)
d_pool = concat.sg_quasi_rnn(is_enc=False, att=True) # (16, 150, 320)
logits = d_pool.sg_conv1d(size=1, dim=len(char2idx),
act="linear") # (16, 150, 179)
if mode == 'train':
# cross entropy loss with logits ( for training set )
loss = logits.sg_ce(target=self.y, mask=True)
istarget = tf.not_equal(self.y, 0).sg_float()
self.reduced_loss = (loss.sg_sum()) / (istarget.sg_sum() + 0.00001)
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
else: # inference
self.preds = logits.sg_argmax()
def __init__(self,
x,
y,
num_batch,
vocab_size,
emb_dim,
hidden_dim,
max_ep=240,
infer_shape=(1, 1),
mode="train"):
self.num_batch = num_batch
self.emb_dim = emb_dim
self.hidden_dim = hidden_dim
self.vocab_size = vocab_size
self.max_len_infer = 512
self.max_ep = max_ep
# reuse = len([t for t in tf.global_variables() if t.name.startswith('gen')]) > 0
reuse = (mode == 'infer')
if mode == "train":
self.x = x
self.y = y
elif mode == "infer":
self.x = tf.placeholder(tf.int32, shape=infer_shape)
self.y = tf.placeholder(tf.int32, shape=infer_shape)
with tf.variable_scope("gen_embs", reuse=reuse):
self.emb_x = tf.get_variable("emb_x",
[self.vocab_size, self.emb_dim])
self.emb_y = tf.get_variable("emb_y",
[self.vocab_size, self.emb_dim])
self.X = tf.nn.embedding_lookup(self.emb_x, self.x)
self.Y = tf.nn.embedding_lookup(self.emb_y, self.y)
with tf.sg_context(name='gen', reuse=reuse):
# self.emb_x = tf.Variable(tf.random_uniform([self.vocab_size, self.emb_dim], 0.0, 1.0), name="emb_x")
# self.emb_y = tf.Variable(tf.random_uniform([self.vocab_size, self.emb_dim], 0.0, 1.0), name="emb_y")
# self.emb_x = tf.sg_emb(name='emb_x', voca_size=self.vocab_size, dim=self.emb_dim) # (68,16)
# self.emb_y = tf.sg_emb(name='emb_y', voca_size=self.vocab_size, dim=self.emb_dim) # (68,16)
# self.X = self.x.sg_lookup(emb=self.emb_x) # (8,63,16)
# self.Y = self.y.sg_lookup(emb=self.emb_y) # (8,63,16)
if mode == "train":
self.lstm_layer = self.X.sg_lstm(in_dim=self.emb_dim,
dim=self.vocab_size,
name="lstm") # (8, 63, 68)
self.test = self.lstm_layer.sg_softmax(name="testtt")
print "mazum??"
print self.test
elif mode == "infer":
self.lstm_layer = self.X.sg_lstm(in_dim=self.emb_dim,
dim=self.vocab_size,
last_only=True,
name="lstm")
self.log_prob = tf.log(self.lstm_layer)
# next_token: select by distribution probability, preds: select by argmax
self.multinormed = tf.multinomial(self.log_prob, 1)
self.next_token = tf.cast(
tf.reshape(tf.multinomial(self.log_prob, 1),
[1, infer_shape[0]]), tf.int32)
self.preds = self.lstm_layer.sg_argmax()
if mode == "train":
self.loss = self.lstm_layer.sg_ce(target=self.y)
self.istarget = tf.not_equal(self.y, 0).sg_float()
self.reduced_loss = (self.loss.sg_sum()) / (
self.istarget.sg_sum() + 0.0000001)
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
def sg_to_sparse(tensor, opt):
indices = tf.where(tf.not_equal(tensor.sg_float(), 0.))
return tf.SparseTensor(
indices=indices,
values=tf.gather_nd(tensor, indices) - 1, # for zero-based index
shape=tf.shape(tensor).sg_cast(dtype=tf.int64))
def __init__(self, mode="train"):
# Inputs and Labels
if mode == "train":
self.x, self.y, self.num_batch = get_batch_data() # (16, 150) int32, (16, 150) int32, int
self.y_src = tf.concat([tf.zeros((Hp.batch_size, 1), tf.int32), self.y[:, :-1]], 1) # (16, 150) int32
else: # inference
self.x = tf.placeholder(tf.int32, shape=(Hp.batch_size, Hp.maxlen))
self.y_src = tf.placeholder(tf.int32, shape=(Hp.batch_size, Hp.maxlen))
# Load vocabulary
char2idx, idx2char = load_vocab()
# Embedding
def embed(inputs, vocab_size, embed_size, variable_scope):
'''
inputs = tf.expand_dims(tf.range(5), 0) => (1, 5)
_embed(inputs, 5, 10) => (1, 5, 10)
'''
with tf.variable_scope(variable_scope):
lookup_table = tf.get_variable('lookup_table',
dtype=tf.float32,
shape=[vocab_size, embed_size],
initializer=tf.truncated_normal_initializer())
return tf.nn.embedding_lookup(lookup_table, inputs)
X = embed(self.x, vocab_size=len(char2idx), embed_size=Hp.hidden_units, variable_scope='X') # (179, 320)
Y = embed(self.y_src, vocab_size=len(char2idx), embed_size=Hp.hidden_units, variable_scope='Y') # (179, 320)
# Y = tf.concat((tf.zeros_like(Y[:, :1, :]), Y[:, :-1, :]), 1)
# Encoding
conv = X.sg_quasi_conv1d(is_enc=True, size=6) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H_zfo1 = pool[Hp.batch_size:] # (16*3, 15, 320) for decoding
conv = pool.sg_quasi_conv1d(is_enc=True, size=2) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H_zfo2 = pool[Hp.batch_size:] # (16*3, 150, 320) for decoding
conv = pool.sg_quasi_conv1d(is_enc=True, size=2) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H_zfo3 = pool[Hp.batch_size:] # (16*3, 150, 320) for decoding
conv = pool.sg_quasi_conv1d(is_enc=True, size=2) # (16*3, 150, 320)
pool = conv.sg_quasi_rnn(is_enc=True, att=False) # (16*4, 150, 320)
H4 = pool[:Hp.batch_size] # (16, 150, 320) for decoding
H_zfo4 = pool[Hp.batch_size:] # (16*3, 150, 320) for decoding
# Decoding
d_conv = (Y.sg_concat(target=H_zfo1, axis=0)
.sg_quasi_conv1d(is_enc=False, size=2))
d_pool = d_conv.sg_quasi_rnn(is_enc=False, att=False) # (16*4, 150, 320)
d_conv = (d_pool.sg_concat(target=H_zfo2, axis=0)
.sg_quasi_conv1d(is_enc=False, size=2))
d_pool = d_conv.sg_quasi_rnn(is_enc=False, att=False) # (16*4, 150, 320)
d_conv = (d_pool.sg_concat(target=H_zfo3, axis=0)
.sg_quasi_conv1d(is_enc=False, size=2))
d_pool = d_conv.sg_quasi_rnn(is_enc=False, att=False) # (16*4, 150, 320)
d_conv = (d_pool.sg_concat(target=H_zfo4, axis=0)
.sg_quasi_conv1d(is_enc=False, size=2))
concat = H4.sg_concat(target=d_conv, axis=0)
d_pool = concat.sg_quasi_rnn(is_enc=False, att=True) # (16, 150, 320)
logits = d_pool.sg_conv1d(size=1, dim=len(char2idx), act="linear") # (16, 150, 179)
if mode=='train':
# cross entropy loss with logits ( for training set )
self.loss = logits.sg_ce(target=self.y, mask=True)
istarget = tf.not_equal(self.y, 0).sg_float()
self.reduced_loss = (self.loss.sg_sum()) / (istarget.sg_sum() + 1e-8)
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
else: # inference
self.preds = logits.sg_argmax()
def __init__(self, mode="train"):
# Inputs and Labels
if mode == "train":
self.x, self.y, self.num_batch = get_batch_data(
) # (16, 150) int32, (16, 150) int32, int
self.y_src = tf.concat(
axis=1,
values=[tf.zeros((Hp.bs, 1), tf.int32),
self.y[:, :-1]]) # (16, 150) int32
else: # inference
self.x = tf.placeholder(tf.int32, shape=(Hp.bs, Hp.maxlen))
self.y_src = tf.placeholder(tf.int32, shape=(Hp.bs, Hp.maxlen))
# Load vocabulary
self.char2idx, self.idx2char = load_vocab()
# Embedding
self.emb_x = tf.sg_emb(name='emb_x',
voca_size=len(self.char2idx),
dim=Hp.hd) # (179, 320)
self.emb_y = tf.sg_emb(name='emb_y',
voca_size=len(self.char2idx),
dim=Hp.hd) # (179, 320)
self.X = self.x.sg_lookup(emb=self.emb_x) # (16, 150, 320)
self.Y = self.y_src.sg_lookup(emb=self.emb_y) # (16, 150, 320)
# Encoding
self.conv = self.X.sg_quasi_conv1d(is_enc=True,
size=6) # (16*4, 150, 320)
self.pool = self.conv.sg_quasi_rnn(is_enc=True,
att=False) # (16*4, 150, 320)
self.H_zfo1 = self.pool[Hp.bs:] # (16*3, 15, 320) for decoding
self.conv = self.pool.sg_quasi_conv1d(is_enc=True,
size=2) # (16*4, 150, 320)
self.pool = self.conv.sg_quasi_rnn(is_enc=True,
att=False) # (16*4, 150, 320)
self.H_zfo2 = self.pool[Hp.bs:] # (16*3, 150, 320) for decoding
self.conv = self.pool.sg_quasi_conv1d(is_enc=True,
size=2) # (16*4, 150, 320)
self.pool = self.conv.sg_quasi_rnn(is_enc=True,
att=False) # (16*4, 150, 320)
self.H_zfo3 = self.pool[Hp.bs:] # (16*3, 150, 320) for decoding
self.conv = self.pool.sg_quasi_conv1d(is_enc=True,
size=2) # (16*4, 150, 320)
self.pool = self.conv.sg_quasi_rnn(is_enc=True,
att=False) # (16*4, 150, 320)
self.H4 = self.pool[:Hp.bs]
self.H_zfo4 = self.pool[Hp.bs:] # (16*3, 150, 320) for decoding
# Decoding
self.dec = self.Y.sg_concat(target=self.H_zfo1, dim=0)
self.d_conv = self.dec.sg_quasi_conv1d(is_enc=False, size=2)
self.d_pool = self.d_conv.sg_quasi_rnn(is_enc=False,
att=False) # (16*4, 150, 320)
self.d_conv = (self.d_pool.sg_concat(
target=self.H_zfo2, dim=0).sg_quasi_conv1d(is_enc=False, size=2))
self.d_pool = self.d_conv.sg_quasi_rnn(is_enc=False,
att=False) # (16*4, 150, 320)
self.d_conv = (self.d_pool.sg_concat(
target=self.H_zfo3, dim=0).sg_quasi_conv1d(is_enc=False, size=2))
self.d_pool = self.d_conv.sg_quasi_rnn(is_enc=False,
att=False) # (16*4, 150, 320)
self.d_conv = (self.d_pool.sg_concat(
target=self.H_zfo4, dim=0).sg_quasi_conv1d(is_enc=False, size=2))
self.concat = self.H4.sg_concat(target=self.d_conv, dim=0)
self.d_pool = self.concat.sg_quasi_rnn(is_enc=False,
att=True) # (16*4, 150, 320)
self.logits = self.d_pool.sg_conv1d(size=1,
dim=len(self.char2idx),
act="linear") # (16, 150, 179)
self.preds = self.logits.sg_argmax()
if mode == 'train':
# cross entropy loss with logits ( for training set )
self.loss = self.logits.sg_ce(target=self.y, mask=True)
self.istarget = tf.not_equal(self.y, 0).sg_float()
self.reduced_loss = (self.loss.sg_sum()) / (
self.istarget.sg_sum() + 0.00001)
tf.sg_summary_loss(self.reduced_loss, "reduced_loss")
