def valid(self, dropout= identity, smooth= None):
"""-> Model with new fields, teacher forcing
output : f32 (?, dim_tgt) prediction on logit scale
prob : f32 (?, dim_tgt) prediction, soft
pred : i32 (?,) prediction, hard
errt_samp : f32 (?,) errors
loss_samp : f32 (?,) losses
errt : f32 () error rate
loss : f32 () mean loss
"""
with scope('emb_src_'): w = self.position(self.max_src) + dropout(self.emb_src(self.src))
with scope('emb_tgt_'): x = self.position(self.max_tgt) + dropout(self.emb_tgt(self.tgt))
w = self.encode(w, self.mask_src, dropout, name= 'encode_') # bds
x = self.decode(x, self.mask_tgt, w, self.mask_src, dropout, name= 'decode_') # bdt
with scope('logit_'):
y = self.emb_tgt( # ?n
tf.boolean_mask( # ?d
tf.transpose(x, (0,2,1)) # btd <- bdt
, self.mask))
with scope('prob_'): prob = tf.nn.softmax(y, axis= -1)
with scope('pred_'): pred = tf.argmax(y, axis= -1, output_type= tf.int32)
with scope('errt_'):
errt_samp = tf.to_float(tf.not_equal(self.true, pred))
errt = tf.reduce_mean(errt_samp)
with scope('loss_'):
loss_samp = tf.nn.softmax_cross_entropy_with_logits_v2(labels= smooth(self.true), logits= y) \
if smooth else tf.nn.sparse_softmax_cross_entropy_with_logits(labels= self.true, logits= y)
loss = tf.reduce_mean(loss_samp)
return Model(self, output= y, prob= prob, pred= pred
, errt_samp= errt_samp, errt= errt
, loss_samp= loss_samp, loss= loss)
def _eval(self):
gold, pred, output, smooth = self.gold, self.pred, self.output, self.smooth
with tf.variable_scope('acc'):
acc = tf.reduce_mean(tf.to_float(tf.equal(gold, pred)))
with tf.variable_scope('loss'):
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(
logits=output, labels=smooth(gold)))
with tf.variable_scope('prob'):
prob = tf.nn.softmax(output, name='prob')
return Transformer(prob=prob, loss=loss, acc=acc, **self)
def train(self, warmup=4e3, beta1=0.9, beta2=0.98, epsilon=1e-9):
"""-> Transformer with new fields
step : i64 () global update step
lr : f32 () learning rate for the current step
up : update operation
"""
dim, loss = self.dim, self.loss
with tf.variable_scope('lr'):
s = tf.train.get_or_create_global_step()
t = tf.to_float(s + 1)
lr = (dim**-0.5) * tf.minimum(t**-0.5, t * (warmup**-1.5))
up = tf.train.AdamOptimizer(lr, beta1, beta2,
epsilon).minimize(loss, s)
return Transformer(step=s, lr=lr, up=up, **self)
def train(self, dropout= 0.1, smooth= 0.1, warmup= 4e3, beta1= 0.9, beta2= 0.98, epsilon= 1e-9):
"""-> Model with new fields, teacher forcing
step : i64 () global update step
lr : f32 () learning rate for the current step
up : update operation
along with all the fields from `valid`
"""
dropout, smooth = Dropout(dropout, (None, self.dim_emb, None)), Smooth(smooth, self.dim_voc)
self = self.valid(dropout= dropout, smooth= smooth)
with scope('lr'):
s = tf.train.get_or_create_global_step()
t = tf.to_float(s + 1)
lr = (self.dim_emb ** -0.5) * tf.minimum(t ** -0.5, t * (warmup ** -1.5))
# up = tf.train.AdamOptimizer(lr, beta1, beta2, epsilon).minimize(self.loss, s)
return Model(self, dropout= dropout, smooth= smooth, step= s, lr= lr)
def sinusoid(dim, time, freq= 1e-4, array= False):
"""returns a rank-2 tensor of shape `dim, time`, where each column
corresponds to a time step and each row a sinusoid, with
frequencies in a geometric progression from 1 to `freq`.
"""
assert not dim % 2
if array:
a = (freq ** ((2 / dim) * np.arange(dim // 2))).reshape(-1, 1) @ (1 + np.arange(time).reshape(1, -1))
return np.concatenate((np.sin(a), np.cos(a)), -1).reshape(dim, time)
else:
assert False # figure out a better way to do this
a = tf.reshape(
freq ** ((2 / dim) * tf.range(dim // 2, dtype= tf.float32))
, (-1, 1)) @ tf.reshape(
1 + tf.range(tf.to_float(time), dtype= tf.float32)
, (1, -1))
return tf.reshape(tf.concat((tf.sin(a), tf.cos(a)), axis= -1), (dim, time))
def data(self, sid, tid, src= None, tgt= None):
"""-> Model with new fields
position : Sinusoid
src_ : i32 (b, ?) source feed, in range `[0, dim_src)`
tgt_ : i32 (b, ?) target feed, in range `[0, dim_tgt)`
src : i32 (b, s) source with `eos` trimmed among the batch
tgt : i32 (b, t) target with `eos` trimmed among the batch, padded with `bos`
mask : b8 (b, t) target sequence mask
true : i32 (?,) target references
max_tgt : i32 () maximum target length
max_src : i32 () maximum source length
mask_tgt : f32 (1, t, t) target attention mask
mask_src : f32 (b, 1, s) source attention mask
"""
src_ = placeholder(tf.int32, (None, None), src, 'src_')
tgt_ = placeholder(tf.int32, (None, None), tgt, 'tgt_')
with scope('src'):
src, msk, max_src = trim(src_, self.eos)
mask_src = tf.log(tf.expand_dims(tf.to_float(msk), axis= 1))
with scope('tgt'):
tgt, msk, max_tgt = trim(tgt_, self.eos)
mask = tf.pad(msk, ((0,0),(1,0)), constant_values= True)
btru = tf.pad(tgt, ((0,0),(1,0)), constant_values= self.bos)
true = tf.pad(tgt, ((0,0),(0,1)), constant_values= self.eos)
true, tgt = tf.boolean_mask(true, mask), btru
max_tgt += 1
mask_tgt = tf.log(tf.expand_dims(causal_mask(max_tgt), axis= 0))
return Model(
position= Sinusoid(self.dim_emb, self.cap)
, src_= src_, mask_src= mask_src, max_src= max_src, src= src
, tgt_= tgt_, mask_tgt= mask_tgt, max_tgt= max_tgt, tgt= tgt
, true= true, mask= mask
, emb_src = self.embeds[sid]
, emb_tgt = self.embeds[tid]
, **self)
def data(self, src=None, tgt=None, len_cap=None):
"""-> Transformer with new fields
src_ : i32 (b, ?) source feed, in range `[0, dim_src)`
tgt_ : i32 (b, ?) target feed, in range `[0, dim_tgt)`
src : i32 (b, s) source with `end` trimmed among the batch
tgt : i32 (b, t) target with `end` trimmed among the batch
mask : f32 (b, s) source mask
gold : i32 (b, t) target one step ahead
position : Sinusoid
setting `len_cap` makes it more efficient for training. you
won't be able to feed it longer sequences, but it doesn't
affect any model parameters.
"""
end, dim = self.end, self.dim
count_not_all = lambda x: tf.reduce_sum(
tf.to_int32(~tf.reduce_all(x, 0)))
with tf.variable_scope('src'):
src = src_ = placeholder(tf.int32, (None, None), src)
len_src = count_not_all(tf.equal(src, end))
src = src[:, :len_src]
with tf.variable_scope('tgt'):
tgt = tgt_ = placeholder(tf.int32, (None, None), tgt)
len_tgt = count_not_all(tf.equal(tgt, end))
tgt, gold = tgt[:, :len_tgt], tgt[:, 1:1 + len_tgt]
return Transformer(position=Sinusoid(dim, len_cap),
src_=src_,
src=src,
mask=tf.to_float(
tf.expand_dims(tf.not_equal(src, end), 1)),
tgt_=tgt_,
tgt=tgt,
gold=gold,
**self)
def vAe(
mode,
src=None,
tgt=None,
# model spec
dim_tgt=8192,
dim_emb=512,
dim_rep=1024,
rnn_layers=3,
bidirectional=True,
bidir_stacked=True,
attentive=False,
logit_use_embed=True,
# training spec
accelerate=1e-4,
learn_rate=1e-3,
bos=2,
eos=1):
# dim_tgt : vocab size
# dim_emb : model dimension
# dim_rep : representation dimension
#
# unk=0 for word dropout
assert mode in ('train', 'valid', 'infer')
self = Record(bos=bos, eos=eos)
with scope('step'):
step = self.step = tf.train.get_or_create_global_step()
rate = accelerate * tf.to_float(step)
rate_keepwd = self.rate_keepwd = tf.sigmoid(rate)
rate_anneal = self.rate_anneal = tf.tanh(rate)
rate_update = self.rate_update = learn_rate / (tf.sqrt(rate) + 1.0)
with scope('src'):
src = self.src = placeholder(tf.int32, (None, None), src, 'src')
src = tf.transpose(src) # time major order
src, msk_src, len_src = trim(src, eos)
with scope('tgt'):
tgt = self.tgt = placeholder(tf.int32, (None, None), tgt, 'tgt')
tgt = tf.transpose(tgt) # time major order
tgt, msk_tgt, len_tgt = trim(tgt, eos)
msk_tgt = tf.pad(msk_tgt, ((1, 0), (0, 0)), constant_values=True)
# pads for decoder : lead=[bos]+tgt -> gold=tgt+[eos]
lead, gold = tgt, tf.pad(tgt,
paddings=((0, 1), (0, 0)),
constant_values=eos)
if 'train' == mode:
lead *= tf.to_int32(
tf.random_uniform(tf.shape(lead)) < rate_keepwd)
lead = self.lead = tf.pad(lead,
paddings=((1, 0), (0, 0)),
constant_values=bos)
# s : src length
# t : tgt length plus one padding, either eos or bos
# b : batch size
#
# len_src : b aka s
# msk_src : sb without padding
# msk_tgt : tb with eos
#
# lead : tb with bos
# gold : tb with eos
with scope('embed'):
b = (6 / (dim_tgt / dim_emb + 1))**0.5
embedding = tf.get_variable('embedding', (dim_tgt, dim_emb),
initializer=tf.random_uniform_initializer(
-b, b))
emb_tgt = tf.gather(embedding, lead,
name='emb_tgt') # (t, b) -> (t, b, dim_emb)
emb_src = tf.gather(embedding, src,
name='emb_src') # (s, b) -> (s, b, dim_emb)
with scope('encode'): # (s, b, dim_emb) -> (b, dim_emb)
reverse = partial(tf.reverse_sequence,
seq_lengths=len_src,
seq_axis=0,
batch_axis=1)
if bidirectional and bidir_stacked:
for i in range(rnn_layers):
with scope("rnn{}".format(i + 1)):
emb_fwd, _ = layer_rnn(1, dim_emb, name='fwd')(emb_src)
emb_bwd, _ = layer_rnn(1, dim_emb,
name='bwd')(reverse(emb_src))
hs = emb_src = tf.concat((emb_fwd, reverse(emb_bwd)),
axis=-1)
elif bidirectional:
with scope("rnn"):
emb_fwd, _ = layer_rnn(rnn_layers, dim_emb,
name='fwd')(emb_src)
emb_bwd, _ = layer_rnn(rnn_layers, dim_emb,
name='bwd')(reverse(emb_src))
hs = tf.concat((emb_fwd, reverse(emb_bwd)), axis=-1)
else:
hs, _ = layer_rnn(rnn_layers, dim_emb, name='rnn')(emb_src)
with scope('cata'):
# extract the final states from the outputs: bd <- sbd, b2
h = tf.gather_nd(
hs,
tf.stack(
(len_src - 1, tf.range(tf.size(len_src), dtype=tf.int32)),
axis=1))
if attentive: # todo fixme
# the values are the outputs from all non-padding steps;
# the queries are the final states;
h = layer_nrm(h + tf.squeeze( # bd <- bd1
attention( # bd1 <- bd1, bds, b1s
tf.expand_dims(h, axis=2), # query: bd1 <- bd
tf.transpose(hs, (1, 2, 0)), # value: bds <- sbd
tf.log(
tf.to_float( # -inf,0 mask: b1s <- sb <- bs
tf.expand_dims(tf.transpose(msk_src),
axis=1))),
int(h.shape[-1])),
2))
with scope('latent'): # (b, dim_emb) -> (b, dim_rep) -> (b, dim_emb)
# h = layer_aff(h, dim_emb, name='in')
mu = self.mu = layer_aff(h, dim_rep, name='mu')
lv = self.lv = layer_aff(h, dim_rep, name='lv')
with scope('z'):
h = mu
if 'train' == mode:
h += tf.exp(0.5 * lv) * tf.random_normal(shape=tf.shape(lv))
self.z = h
h = layer_aff(h, dim_emb, name='ex')
with scope('decode'): # (b, dim_emb) -> (t, b, dim_emb) -> (?, dim_emb)
h = self.state_in = tf.stack((h, ) * rnn_layers)
h, _ = _, (self.state_ex, ) = layer_rnn(rnn_layers,
dim_emb,
name='rnn')(
emb_tgt,
initial_state=(h, ))
if 'infer' != mode: h = tf.boolean_mask(h, msk_tgt)
h = layer_aff(h, dim_emb, name='out')
with scope('logits'): # (?, dim_emb) -> (?, dim_tgt)
if logit_use_embed:
logits = self.logits = tf.tensordot(h, (dim_emb**-0.5) *
tf.transpose(embedding), 1)
else:
logits = self.logits = layer_aff(h, dim_tgt)
with scope('prob'):
prob = self.prob = tf.nn.softmax(logits)
with scope('pred'):
pred = self.pred = tf.argmax(logits, -1, output_type=tf.int32)
if 'infer' != mode:
labels = tf.boolean_mask(gold, msk_tgt, name='labels')
with scope('errt'):
errt_samp = self.errt_samp = tf.to_float(tf.not_equal(
labels, pred))
errt = self.errt = tf.reduce_mean(errt_samp)
with scope('loss'):
with scope('loss_gen'):
loss_gen_samp = self.loss_gen_samp = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=labels, logits=logits)
loss_gen = self.loss_gen = tf.reduce_mean(loss_gen_samp)
with scope('loss_kld'):
loss_kld_samp = self.loss_kld_samp = 0.5 * (
tf.square(mu) + tf.exp(lv) - lv - 1.0)
loss_kld = self.loss_kld = tf.reduce_mean(loss_kld_samp)
loss = self.loss = rate_anneal * loss_kld + loss_gen
if 'train' == mode:
with scope('train'):
train_step = self.train_step = tf.train.AdamOptimizer(
rate_update).minimize(loss, step)
return self
def VAE(data, btlnk_dim, data_dim, dense_dim, y_dim, loss_type, accelerate):
def encoder(x, dim_btlnk, dim_x):
x = Normalize(dim_btlnk, "nrm")(tf.nn.elu(Linear(dim_btlnk, dim_x, name= 'lin')(x)))
with tf.variable_scope('latent'):
mu = Linear(dim_btlnk, dim_btlnk, name= 'mu')(x)
lv = Linear(dim_btlnk, dim_btlnk, name= 'lv')(x)
#lv = Linear(dim_btlnk, dim_x, name= 'lv')(x)
#mu = Linear(dim_btlnk, dim_x, name= 'mu')(x)
with tf.variable_scope('z'):
z = mu + tf.exp(0.5 * lv) * tf.random_normal(shape=tf.shape(lv))
return z, mu, lv
def decoder(x, data_dim, btlnk_dim):
x = Linear(data_dim, btlnk_dim)(x)
#return tf.clip_by_value(x, 0.0, 1.0)
return tf.nn.sigmoid(x)
with tf.variable_scope("x"):
x = placeholder(tf.float32, [None, data_dim], data[0], "x")
with tf.variable_scope("y"):
y = placeholder(tf.float32, [None], data[1], "y")
with tf.variable_scope("encoder"):
z, mu, lv = encoder(x, btlnk_dim, data_dim)
with tf.variable_scope("decoder"):
logits = decoder(z, data_dim, btlnk_dim)
with tf.variable_scope("step"):
step = tf.train.get_or_create_global_step()
rate = accelerate * tf.to_float(step)
rate_anneal = tf.tanh(rate)
with tf.variable_scope("loss"):
kl_loss = tf.reduce_mean(0.5 * (tf.square(mu) + tf.exp(lv) - lv - 1.0))
if loss_type == "xtrpy":
#loss_rec = tf.reduce_mean(tf.losses.softmax_cross_entropy(onehot_labels=x, logits=logits))
epsilon = 1e-10
loss_rec = tf.reduce_mean(-tf.reduce_sum(x * tf.log(epsilon+logits) +
(1-x) * tf.log(epsilon+1-logits), axis=1))
else:
loss_rec = tf.reduce_mean(tf.abs(x - logits))
loss = loss_rec + kl_loss*rate_anneal
with tf.variable_scope("AUC"):
anomaly_score = tf.reduce_mean((x-logits)**2, axis=1)
_, auc = tf.metrics.auc(y, anomaly_score)
with tf.variable_scope("train_step"):
train_step = tf.train.AdamOptimizer().minimize(loss, step)
return dict(step=step,
x=x,
y=y,
z=z,
mu=mu,
logits=logits,
auc=auc,
train_step=train_step,
loss=loss,
kl_loss=kl_loss,
loss_rec=loss_rec)
def model(mode,
src_dwh,
tgt_dwh,
src_idx=None,
len_src=None,
tgt_img=None,
tgt_idx=None,
len_tgt=None,
num_layers=3,
num_units=512,
learn_rate=1e-3,
decay_rate=1e-2,
dropout=0.1):
assert mode in ('train', 'valid', 'infer')
self = Record()
src_d, src_w, src_h = src_dwh
tgt_d, tgt_w, tgt_h = tgt_dwh
with scope('source'):
# input nodes
src_idx = self.src_idx = placeholder(tf.int32, (None, None), src_idx,
'src_idx') # n s
len_src = self.len_src = placeholder(tf.int32, (None, ), len_src,
'len_src') # n
# time major order
src_idx = tf.transpose(src_idx, (1, 0)) # s n
emb_src = tf.one_hot(src_idx, src_d) # s n v
for i in range(num_layers):
with scope("rnn{}".format(i + 1)):
emb_fwd, _ = tf.contrib.cudnn_rnn.CudnnGRU(
1, num_units, dropout=dropout,
name='fwd')(emb_src, training='train' == mode)
emb_bwd, _ = tf.contrib.cudnn_rnn.CudnnGRU(
1, num_units, dropout=dropout,
name='bwd')(tf.reverse_sequence(emb_src,
len_src,
seq_axis=0,
batch_axis=1),
training='train' == mode)
emb_src = tf.concat(
(emb_fwd,
tf.reverse_sequence(
emb_bwd, len_src, seq_axis=0, batch_axis=1)),
axis=-1)
# emb_src = tf.layers.dense(emb_src, num_units, name= 'reduce_concat') # s n d
emb_src = self.emb_src = tf.transpose(emb_src, (1, 2, 0)) # n d s
with scope('target'):
# input nodes
tgt_img = self.tgt_img = placeholder(tf.uint8,
(None, None, tgt_h, tgt_w),
tgt_img, 'tgt_img') # n t h w
tgt_idx = self.tgt_idx = placeholder(tf.int32, (None, None), tgt_idx,
'tgt_idx') # n t
len_tgt = self.len_tgt = placeholder(tf.int32, (None, ), len_tgt,
'len_tgt') # n
# time major order
tgt_idx = tf.transpose(tgt_idx) # t n
tgt_img = tf.transpose(tgt_img, (1, 0, 2, 3)) # t n h w
tgt_img = flatten(tgt_img, 2, 3) # t n hw
# normalize pixels to binary
tgt_img = tf.to_float(tgt_img) / 255.0
# tgt_img = tf.round(tgt_img)
# todo consider adding noise
# causal padding
fire = self.fire = tf.pad(tgt_img, ((1, 0), (0, 0), (0, 0)),
constant_values=0.0)
true = self.true = tf.pad(tgt_img, ((0, 1), (0, 0), (0, 0)),
constant_values=1.0)
tidx = self.tidx = tf.pad(tgt_idx, ((0, 1), (0, 0)), constant_values=1)
mask_tgt = tf.transpose(tf.sequence_mask(len_tgt + 1)) # t n
with scope('decode'):
# needs to get input from latent space to do attention or some shit
decoder = self.decoder = tf.contrib.cudnn_rnn.CudnnGRU(num_layers,
num_units,
dropout=dropout)
state_in = self.state_in = tf.zeros(
(num_layers, tf.shape(fire)[1], num_units))
x, _ = _, (self.state_ex, ) = decoder(fire,
initial_state=(state_in, ),
training='train' == mode)
# transform mask to -inf and 0 in order to simply sum for whatever the f**k happens next
mask = tf.log(tf.sequence_mask(len_src, dtype=tf.float32)) # n s
mask = tf.expand_dims(mask, 1) # n 1 s
# multi-head scaled dot-product attention
x = tf.transpose(x, (1, 2, 0)) # t n d ---> n d t
attn = Attention(num_units, num_units, 2 * num_units)(x, emb_src, mask)
if 'train' == mode: attn = tf.nn.dropout(attn, 1 - dropout)
x = Normalize(num_units)(x + attn)
x = tf.transpose(x, (2, 0, 1)) # n d t ---> t n d
if 'infer' != mode:
x = tf.boolean_mask(x, mask_tgt)
true = tf.boolean_mask(true, mask_tgt)
tidx = tf.boolean_mask(tidx, mask_tgt)
with scope('output'):
y = tf.layers.dense(x, tgt_h * tgt_w, name='dense_img')
z = tf.layers.dense(x, tgt_d, name='logit_idx')
pred = self.pred = tf.clip_by_value(y, 0.0, 1.0)
prob = self.prob = tf.nn.softmax(z)
pidx = self.pidx = tf.argmax(z, axis=-1, output_type=tf.int32)
with scope('losses'):
diff = true - pred
mae = self.mae = tf.reduce_mean(tf.abs(diff), axis=-1)
mse = self.mse = tf.reduce_mean(tf.square(diff), axis=-1)
xid = self.xid = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=z, labels=tidx)
err = self.err = tf.not_equal(tidx, pidx)
loss = tf.reduce_mean(xid)
with scope('update'):
step = self.step = tf.train.get_or_create_global_step()
lr = self.lr = learn_rate / (1.0 +
decay_rate * tf.sqrt(tf.to_float(step)))
if 'train' == mode:
down = self.down = tf.train.AdamOptimizer(lr).minimize(loss, step)
return self
def build(self, x, y, z, loss_type):
d_scale_factor = tf.constant(0.) #tf.constant(0.25)
g_scale_factor = tf.constant(0.) #tf.constant(1 - 0.75/2)
with scope("x"):
x = placeholder(tf.float32, [None, self.dim_x], x, "x")
with scope("y"):
y = placeholder(tf.float32, [None], y, "y")
with scope("z"):
z = placeholder(tf.float32, [None, self.dim_noise], z, "z")
zx, mu, lv, hl_e = self.enc(x)
gzx = self.gen(zx)
#gz = self.gen(z)
dx, hl_dx = self.dis(x)
dgzx, hl_dgzx = self.dis(gzx)
#dgz, hl_dgz = self.dis(gz)
with tf.variable_scope("step"):
step = tf.train.get_or_create_global_step()
rate = self.accelerate * tf.to_float(step)
rate_anneal = tf.tanh(rate)
with scope("loss"):
dx_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(dx) - d_scale_factor, logits=dx))
dgzx_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.zeros_like(dgzx), logits=dgzx))
#dgz_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.zeros_like(dgz), logits=dgz))
d_loss = dx_loss + dgzx_loss #+ dgz_loss
kl_loss = tf.reduce_mean(0.5 *
(tf.square(mu) + tf.exp(lv) - lv - 1.0))
gzx_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(dgzx) - g_scale_factor, logits=dgzx))
if loss_type == "xtrpy":
epsilon = 1e-10
ftr_loss = tf.reduce_mean(
-tf.reduce_sum(x * tf.log(epsilon + gzx) +
(1 - x) * tf.log(epsilon + 1 - gzx),
axis=1))
g_loss = gzx_loss / 10 + ftr_loss / 5 + kl_loss * rate_anneal
else:
ftr_loss = tf.reduce_mean(tf.abs(x - gzx))
g_loss = gzx_loss / 2 + ftr_loss * 10 + kl_loss * rate_anneal
with scope("AUC"):
_, auc_gzx = tf.metrics.auc(y, tf.reduce_mean((x - gzx)**2,
axis=1))
_, auc_dx = tf.metrics.auc(y, tf.nn.sigmoid(dx))
_, auc_dgzx = tf.metrics.auc(y, tf.nn.sigmoid(dgzx))
g_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="generator")
g_vars.append(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="encoder"))
print(g_vars)
d_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="discriminator")
print(d_vars)
with scope('train_step'):
#optimizer = tf.train.RMSPropOptimizer()
optimizer = tf.train.AdamOptimizer()
d_step = optimizer.minimize(d_loss, step, var_list=d_vars)
g_step = optimizer.minimize(g_loss, step, var_list=g_vars)
return VAEGAN(
self,
step=step,
x=x,
y=y,
z=z,
zx=zx,
mu=mu,
lv=lv,
m=tf.reduce_mean(mu),
l=tf.reduce_mean(lv)
#, gz=gz
,
gzx=gzx,
auc_gzx=auc_gzx,
auc_dx=auc_dx,
auc_dgzx=auc_dgzx,
g_step=g_step,
d_step=d_step,
g_loss=g_loss,
d_loss=d_loss
#,gz_loss=gz_loss
,
gzx_loss=gzx_loss,
ftr_loss=ftr_loss,
kl_loss=kl_loss,
dx_loss=dx_loss
#, dgz_loss=dgz_loss
,
dgzx_loss=dgzx_loss)