def _get_loss_train_op(self):
# Aggregates loss
loss_rephraser = (tf.get_collection('loss_rephraser_list')[0] + tf.get_collection('loss_rephraser_list')[1] + tf.get_collection('loss_rephraser_list')[2])/3.
self.loss = loss_rephraser
# Creates optimizers
self.vars = collect_trainable_variables([self.xlnet_encoder, self.word_embedder,
self.downmlp, self.rephrase_encoder, self.rephrase_decoder])
# Train Op
self.train_op_pre = get_train_op(
self.loss, self.vars, learning_rate=self.lr, hparams=self._hparams.opt)
# Interface tensors
self.losses = {
"loss": self.loss,
"loss_rephraser": loss_rephraser,
}
self.metrics = {
}
self.train_ops = {
"train_op_pre": self.train_op_pre,
}
self.samples = {
"transferred_yy1_gt": self.text_ids_yy1,
"transferred_yy1_pred": tf.get_collection('yy_pred_list')[0],
"transferred_yy2_gt": self.text_ids_yy2,
"transferred_yy2_pred": tf.get_collection('yy_pred_list')[1],
"transferred_yy3_gt": self.text_ids_yy3,
"transferred_yy3_pred": tf.get_collection('yy_pred_list')[2],
"origin_y1":self.text_ids_y1,
"origin_y2":self.text_ids_y2,
"origin_y3":self.text_ids_y3,
"x1x2":self.x1x2,
"x1xx2":self.x1xx2
}
tf.summary.scalar("loss", self.loss)
tf.summary.scalar("loss_rephraser", loss_rephraser)
self.merged = tf.summary.merge_all()
self.fetches_train_pre = {
"loss": self.train_ops["train_op_pre"],
"loss_rephraser": self.losses["loss_rephraser"],
"merged": self.merged,
}
fetches_eval = {"batch_size": get_batch_size(self.x1x2yx1xx2_ids),
"merged": self.merged,
}
fetches_eval.update(self.losses)
fetches_eval.update(self.metrics)
fetches_eval.update(self.samples)
self.fetches_eval = fetches_eval
def _build_model(self, inputs, vocab, gamma, lambda_g):
"""Builds the model.
"""
embedder = WordEmbedder(vocab_size=vocab.size,
hparams=self._hparams.embedder)
encoder = UnidirectionalRNNEncoder(hparams=self._hparams.encoder)
# text_ids for encoder, with BOS token removed
enc_text_ids = inputs['text_ids'][:, 1:]
enc_outputs, final_state = encoder(embedder(enc_text_ids),
sequence_length=inputs['length'] -
1)
z = final_state[:, self._hparams.dim_c:]
# Encodes label
label_connector = MLPTransformConnector(self._hparams.dim_c)
# Gets the sentence representation: h = (c, z)
labels = tf.cast(tf.reshape(inputs['labels'], [-1, 1]), tf.float32)
c = label_connector(labels)
c_ = label_connector(1 - labels)
h = tf.concat([c, z], 1)
h_ = tf.concat([c_, z], 1)
# Teacher-force decoding and the auto-encoding loss for G
decoder = AttentionRNNDecoder(
memory=enc_outputs,
memory_sequence_length=inputs['length'] - 1,
cell_input_fn=lambda inputs, attention: inputs,
vocab_size=vocab.size,
hparams=self._hparams.decoder)
connector = MLPTransformConnector(decoder.state_size)
g_outputs, _, _ = decoder(initial_state=connector(h),
inputs=inputs['text_ids'],
embedding=embedder,
sequence_length=inputs['length'] - 1)
loss_g_ae = tx.losses.sequence_sparse_softmax_cross_entropy(
labels=inputs['text_ids'][:, 1:],
logits=g_outputs.logits,
sequence_length=inputs['length'] - 1,
average_across_timesteps=True,
sum_over_timesteps=False)
# Gumbel-softmax decoding, used in training
start_tokens = tf.ones_like(inputs['labels']) * vocab.bos_token_id
end_token = vocab.eos_token_id
gumbel_helper = GumbelSoftmaxEmbeddingHelper(embedder.embedding,
start_tokens, end_token,
gamma)
soft_outputs_, _, soft_length_, = decoder(helper=gumbel_helper,
initial_state=connector(h_))
# Greedy decoding, used in eval
outputs_, _, length_ = decoder(decoding_strategy='infer_greedy',
initial_state=connector(h_),
embedding=embedder,
start_tokens=start_tokens,
end_token=end_token)
# Creates classifier
classifier = Conv1DClassifier(hparams=self._hparams.classifier)
clas_embedder = WordEmbedder(vocab_size=vocab.size,
hparams=self._hparams.embedder)
# Classification loss for the classifier
clas_logits, clas_preds = classifier(
inputs=clas_embedder(ids=inputs['text_ids'][:, 1:]),
sequence_length=inputs['length'] - 1)
loss_d_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.cast(inputs['labels'], tf.float32), logits=clas_logits)
loss_d_clas = tf.reduce_mean(loss_d_clas)
accu_d = tx.evals.accuracy(labels=inputs['labels'], preds=clas_preds)
# Classification loss for the generator, based on soft samples
soft_logits, soft_preds = classifier(
inputs=clas_embedder(soft_ids=soft_outputs_.sample_id),
sequence_length=soft_length_)
loss_g_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.cast(1 - inputs['labels'], tf.float32),
logits=soft_logits)
loss_g_clas = tf.reduce_mean(loss_g_clas)
# Accuracy on soft samples, for training progress monitoring
accu_g = tx.evals.accuracy(labels=1 - inputs['labels'],
preds=soft_preds)
# Accuracy on greedy-decoded samples, for training progress monitoring
_, gdy_preds = classifier(inputs=clas_embedder(ids=outputs_.sample_id),
sequence_length=length_)
accu_g_gdy = tx.evals.accuracy(labels=1 - inputs['labels'],
preds=gdy_preds)
# Aggregates losses
loss_g = loss_g_ae + lambda_g * loss_g_clas
loss_d = loss_d_clas
# Creates optimizers
g_vars = collect_trainable_variables(
[embedder, encoder, label_connector, connector, decoder])
d_vars = collect_trainable_variables([clas_embedder, classifier])
train_op_g = get_train_op(loss_g, g_vars, hparams=self._hparams.opt)
train_op_g_ae = get_train_op(loss_g_ae,
g_vars,
hparams=self._hparams.opt)
train_op_d = get_train_op(loss_d, d_vars, hparams=self._hparams.opt)
# Interface tensors
self.losses = {
"loss_g": loss_g,
"loss_g_ae": loss_g_ae,
"loss_g_clas": loss_g_clas,
"loss_d": loss_d_clas
}
self.metrics = {
"accu_d": accu_d,
"accu_g": accu_g,
"accu_g_gdy": accu_g_gdy,
}
self.train_ops = {
"train_op_g": train_op_g,
"train_op_g_ae": train_op_g_ae,
"train_op_d": train_op_d
}
self.samples = {
"original": inputs['text_ids'][:, 1:],
"transferred": outputs_.sample_id
}
self.fetches_train_g = {
"loss_g": self.train_ops["train_op_g"],
"loss_g_ae": self.losses["loss_g_ae"],
"loss_g_clas": self.losses["loss_g_clas"],
"accu_g": self.metrics["accu_g"],
"accu_g_gdy": self.metrics["accu_g_gdy"],
}
self.fetches_train_d = {
"loss_d": self.train_ops["train_op_d"],
"accu_d": self.metrics["accu_d"]
}
fetches_eval = {"batch_size": get_batch_size(inputs['text_ids'])}
fetches_eval.update(self.losses)
fetches_eval.update(self.metrics)
fetches_eval.update(self.samples)
self.fetches_eval = fetches_eval
def _build_model(self, inputs, vocab, gamma, lambda_g, lambda_z, lambda_z1,
lambda_z2, lambda_ae):
embedder = WordEmbedder(vocab_size=vocab.size,
hparams=self._hparams.embedder)
encoder = UnidirectionalRNNEncoder(hparams=self._hparams.encoder)
enc_text_ids = inputs['text_ids'][:, 1:]
enc_outputs, final_state = encoder(embedder(enc_text_ids),
sequence_length=inputs['length'] -
1)
z = final_state[:, self._hparams.dim_c:]
# -------------------- CLASSIFIER ---------------------
n_classes = self._hparams.num_classes
z_classifier_l1 = MLPTransformConnector(
256, hparams=self._hparams.z_classifier_l1)
z_classifier_l2 = MLPTransformConnector(
64, hparams=self._hparams.z_classifier_l2)
z_classifier_out = MLPTransformConnector(
n_classes if n_classes > 2 else 1)
z_logits = z_classifier_l1(z)
z_logits = z_classifier_l2(z_logits)
z_logits = z_classifier_out(z_logits)
z_pred = tf.greater(z_logits, 0)
z_logits = tf.reshape(z_logits, [-1])
z_pred = tf.to_int64(tf.reshape(z_pred, [-1]))
loss_z_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(inputs['labels']), logits=z_logits)
loss_z_clas = tf.reduce_mean(loss_z_clas)
accu_z_clas = tx.evals.accuracy(labels=inputs['labels'], preds=z_pred)
# -------------------________________---------------------
label_connector = MLPTransformConnector(self._hparams.dim_c)
labels = tf.to_float(tf.reshape(inputs['labels'], [-1, 1]))
c = label_connector(labels)
c_ = label_connector(1 - labels)
h = tf.concat([c, z], 1)
h_ = tf.concat([c_, z], 1)
# Teacher-force decoding and the auto-encoding loss for G
decoder = AttentionRNNDecoder(
memory=enc_outputs,
memory_sequence_length=inputs['length'] - 1,
cell_input_fn=lambda inputs, attention: inputs,
vocab_size=vocab.size,
hparams=self._hparams.decoder)
connector = MLPTransformConnector(decoder.state_size)
g_outputs, _, _ = decoder(initial_state=connector(h),
inputs=inputs['text_ids'],
embedding=embedder,
sequence_length=inputs['length'] - 1)
loss_g_ae = tx.losses.sequence_sparse_softmax_cross_entropy(
labels=inputs['text_ids'][:, 1:],
logits=g_outputs.logits,
sequence_length=inputs['length'] - 1,
average_across_timesteps=True,
sum_over_timesteps=False)
# Gumbel-softmax decoding, used in training
start_tokens = tf.ones_like(inputs['labels']) * vocab.bos_token_id
end_token = vocab.eos_token_id
gumbel_helper = GumbelSoftmaxEmbeddingHelper(embedder.embedding,
start_tokens, end_token,
gamma)
soft_outputs_, _, soft_length_, = decoder(helper=gumbel_helper,
initial_state=connector(h_))
soft_outputs, _, soft_length, = decoder(helper=gumbel_helper,
initial_state=connector(h))
# ---------------------------- SHIFTED LOSS -------------------------------------
_, encoder_final_state_ = encoder(
embedder(soft_ids=soft_outputs_.sample_id),
sequence_length=inputs['length'] - 1)
_, encoder_final_state = encoder(
embedder(soft_ids=soft_outputs.sample_id),
sequence_length=inputs['length'] - 1)
new_z_ = encoder_final_state_[:, self._hparams.dim_c:]
new_z = encoder_final_state[:, self._hparams.dim_c:]
cos_distance_z_ = tf.abs(
tf.losses.cosine_distance(tf.nn.l2_normalize(z, axis=1),
tf.nn.l2_normalize(new_z_, axis=1),
axis=1))
cos_distance_z = tf.abs(
tf.losses.cosine_distance(tf.nn.l2_normalize(z, axis=1),
tf.nn.l2_normalize(new_z, axis=1),
axis=1))
# ----------------------------______________-------------------------------------
# Greedy decoding, used in eval
outputs_, _, length_ = decoder(decoding_strategy='infer_greedy',
initial_state=connector(h_),
embedding=embedder,
start_tokens=start_tokens,
end_token=end_token)
# Creates classifier
classifier = Conv1DClassifier(
hparams=self._hparams.classifier
) #Conv1DClassifier(hparams=self._hparams.classifier)
discriminator = Conv1DClassifier(hparams=self._hparams.discriminator)
clas_embedder = WordEmbedder(vocab_size=vocab.size,
hparams=self._hparams.embedder)
d_embedder = WordEmbedder(vocab_size=vocab.size,
hparams=self._hparams.embedder)
# Classification loss for the classifier
true_samples = d_embedder(ids=inputs['text_ids'][:, 1:])
clas_logits, clas_preds = discriminator(
inputs=true_samples, sequence_length=inputs['length'] - 1)
# print(clas_logits.shape)
clas_logits, d_logits = tf.split(clas_logits, 2, 1)
clas_logits = tf.squeeze(clas_logits)
d_logits = tf.squeeze(d_logits)
if self._hparams.WGAN:
loss_d_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(inputs['labels']), logits=clas_logits)
accu_d_r = tx.evals.accuracy(labels=inputs['labels'],
preds=(clas_logits >= 0.5))
loss_d_dis = -tf.reduce_mean(d_logits)
# Classification loss for the generator, based on soft samples
fake_samples = d_embedder(soft_ids=soft_outputs_.sample_id)
soft_logits, soft_preds = discriminator(
inputs=fake_samples, sequence_length=soft_length_)
clas_logits, d_logits = tf.split(soft_logits, 2, 1)
clas_logits = tf.squeeze(clas_logits)
d_logits = tf.squeeze(d_logits)
loss_d_dis = loss_d_dis + tf.reduce_mean(
d_logits) # tf.reduce_mean(loss_g_clas)
loss_d_clas = loss_d_clas + lambda_g * tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(1 - inputs['labels']), logits=clas_logits)
accu_d_f = tx.evals.accuracy(labels=1 - inputs['labels'],
preds=(clas_logits >= 0.5))
loss_d_clas = tf.reduce_mean(loss_d_clas)
else:
loss_d_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(inputs['labels']), logits=clas_logits)
loss_d_clas = tf.reduce_mean(loss_d_clas)
loss_d_dis = tf.constant(0., tf.float32)
clas_samples = clas_embedder(ids=inputs['text_ids'][:, 1:])
clas_logits, clas_preds = classifier(inputs=clas_samples,
sequence_length=inputs['length'] -
1)
loss_clas = tf.nn.sigmoid_cross_entropy_with_logits(labels=tf.to_float(
inputs['labels']),
logits=clas_logits)
loss_clas = tf.reduce_mean(loss_clas)
accu_clas = tx.evals.accuracy(labels=inputs['labels'],
preds=clas_preds)
# Classification loss for the generator, based on soft samples
fake_samples = d_embedder(soft_ids=soft_outputs_.sample_id)
soft_logits, soft_preds = discriminator(inputs=fake_samples,
sequence_length=soft_length_)
soft_logits, d_logits = tf.split(soft_logits, 2, 1)
soft_logits = tf.squeeze(soft_logits)
d_logits = tf.squeeze(d_logits)
if self._hparams.WGAN:
loss_g_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(1 - inputs['labels']), logits=soft_logits)
loss_g_clas = tf.reduce_mean(
loss_g_clas) # tf.reduce_mean(loss_g_clas)
normalized_d_logits = d_logits - tf.math.reduce_max(
d_logits, axis=0, keepdims=True)
W = tf.cast(
get_batch_size(inputs['text_ids']),
tf.float32) * tf.exp(normalized_d_logits) / tf.reduce_sum(
tf.exp(normalized_d_logits), 0)
W = tf.stop_gradient(W)
loss_g_dis = -tf.reduce_mean(W * d_logits)
else:
loss_g_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(1 - inputs['labels']), logits=soft_logits)
loss_g_clas = tf.reduce_mean(loss_g_clas)
loss_g_dis = tf.constant(0., tf.float32)
if self._hparams.WGAN:
# WGAN-GP loss
alpha = tf.random_uniform(
shape=[get_batch_size(inputs['text_ids']), 1, 1],
minval=0.,
maxval=1.)
true_samples = tf.cond(
tf.less(tf.shape(fake_samples)[1],
tf.shape(true_samples)[1]),
true_fn=lambda: true_samples,
false_fn=lambda: dynamic_padding(true_samples, fake_samples))
fake_samples = tf.cond(
tf.less(tf.shape(fake_samples)[1],
tf.shape(true_samples)[1]),
true_fn=lambda: dynamic_padding(fake_samples, true_samples),
false_fn=lambda: fake_samples)
differences = fake_samples - true_samples # fake_samples[:,:16] - true_samples[:,:16]
interpolates = true_samples + (alpha * differences)
# D(interpolates, is_reuse=True)
soft_logits, _ = discriminator(inputs=interpolates,
sequence_length=inputs['length'] -
1)
_, d_logits = tf.split(soft_logits, 2, 1)
d_logits = tf.squeeze(d_logits)
gradients = tf.gradients(d_logits, [interpolates])[0]
slopes = tf.sqrt(
tf.reduce_sum(tf.square(gradients), reduction_indices=[1, 2]))
gradient_penalty = self._hparams.LAMBDA * tf.reduce_mean(
(slopes - 1.)**2)
else:
gradient_penalty = tf.constant(0., tf.float32)
cfake_samples = clas_embedder(soft_ids=soft_outputs_.sample_id)
# Accuracy on soft samples, for training progress monitoring
soft_logits, soft_preds = classifier(inputs=cfake_samples,
sequence_length=soft_length_)
accu_g = tx.evals.accuracy(labels=1 - inputs['labels'],
preds=soft_preds)
# Accuracy on greedy-decoded samples, for training progress monitoring
_, gdy_preds = classifier(inputs=clas_embedder(ids=outputs_.sample_id),
sequence_length=length_)
accu_g_gdy = tx.evals.accuracy(labels=1 - inputs['labels'],
preds=gdy_preds)
# Aggregates losses
loss_g = lambda_ae * loss_g_ae + \
lambda_g * (self._hparams.ACGAN_SCALE_G*loss_g_clas + loss_g_dis) + \
lambda_z1 * cos_distance_z + cos_distance_z_ * lambda_z2 \
- lambda_z * loss_z_clas
loss_d = self._hparams.ACGAN_SCALE_D * loss_d_clas + loss_d_dis + gradient_penalty
print("\n==========ACSCALE D:{}, G:{}=========\n".format(
self._hparams.ACGAN_SCALE_D, self._hparams.ACGAN_SCALE_G))
loss_z = loss_z_clas
# Creates optimizers
g_vars = collect_trainable_variables(
[embedder, encoder, label_connector, connector, decoder])
d_vars = collect_trainable_variables([d_embedder, discriminator])
clas_vars = collect_trainable_variables([clas_embedder, classifier])
z_vars = collect_trainable_variables(
[z_classifier_l1, z_classifier_l2, z_classifier_out])
train_op_g = get_train_op(loss_g, g_vars, hparams=self._hparams.opt_g)
train_op_g_ae = get_train_op(loss_g_ae,
g_vars,
hparams=self._hparams.opt)
if self._hparams.WGAN:
train_op_d = get_train_op(loss_d,
d_vars,
hparams=self._hparams.opt_d)
else:
train_op_d = get_train_op(loss_d,
d_vars,
hparams=self._hparams.opt)
train_op_c = get_train_op(loss_clas,
clas_vars,
hparams=self._hparams.opt)
train_op_z = get_train_op(loss_z, z_vars, hparams=self._hparams.opt)
# Interface tensors
self.losses = {
"loss_g": loss_g,
"loss_g_ae": loss_g_ae,
"loss_g_clas": loss_g_clas,
"loss_g_dis": loss_g_dis,
"loss_d": loss_d,
"loss_d_clas": loss_d_clas,
"loss_d_dis": loss_d_dis,
"loss_clas": loss_clas,
"loss_gp": gradient_penalty,
"loss_z_clas": loss_z_clas,
"loss_cos_": cos_distance_z_,
"loss_cos": cos_distance_z
}
self.metrics = {
"accu_d_r": accu_d_r,
"accu_d_f": accu_d_f,
"accu_clas": accu_clas,
"accu_g": accu_g,
"accu_g_gdy": accu_g_gdy,
"accu_z_clas": accu_z_clas
}
self.train_ops = {
"train_op_g": train_op_g,
"train_op_g_ae": train_op_g_ae,
"train_op_d": train_op_d,
"train_op_z": train_op_z,
"train_op_c": train_op_c
}
self.samples = {
"original": inputs['text_ids'][:, 1:],
"transferred": outputs_.sample_id,
"z_vector": z,
"labels_source": inputs['labels'],
"labels_target": 1 - inputs['labels'],
"labels_predicted": gdy_preds
}
self.fetches_train_g = {
"loss_g": self.train_ops["train_op_g"],
"loss_g_ae": self.losses["loss_g_ae"],
"loss_g_clas": self.losses["loss_g_clas"],
"loss_g_dis": self.losses["loss_g_dis"],
"loss_shifted_ae1": self.losses["loss_cos"],
"loss_shifted_ae2": self.losses["loss_cos_"],
"accu_g": self.metrics["accu_g"],
"accu_g_gdy": self.metrics["accu_g_gdy"],
"accu_z_clas": self.metrics["accu_z_clas"]
}
self.fetches_train_z = {
"loss_z": self.train_ops["train_op_z"],
"accu_z": self.metrics["accu_z_clas"]
}
self.fetches_train_d = {
"loss_d": self.train_ops["train_op_d"],
"loss_d_clas": self.losses["loss_d_clas"],
"loss_d_dis": self.losses["loss_d_dis"],
"loss_c": self.train_ops["train_op_c"],
"loss_gp": self.losses["loss_gp"],
"accu_d_r": self.metrics["accu_d_r"],
"accu_d_f": self.metrics["accu_d_f"],
"accu_clas": self.metrics["accu_clas"]
}
fetches_eval = {"batch_size": get_batch_size(inputs['text_ids'])}
fetches_eval.update(self.losses)
fetches_eval.update(self.metrics)
fetches_eval.update(self.samples)
self.fetches_eval = fetches_eval
def _build_model(self, inputs, vocab,lambda_ae, gamma, lambda_D):
"""Builds the model.
"""
embedder = WordEmbedder(
vocab_size=vocab.size,
hparams=self._hparams.embedder)
# rnn_cell = tf.nn.rnn_cell.BasicRNNCell(hidden_size)
# 'outputs' is a tensor of shape [batch_size, max_time, cell_state_size]
# defining initial state
encoder = UnidirectionalRNNEncoder(hparams=self._hparams.encoder)
# initial_state=rnn_cell.zero_state(64, dtype=tf.float32)
# text_ids for encoder, with BOS removed
enc_text_ids = inputs['text_ids'][:, 1:]
enc_outputs, final_state = encoder(embedder(enc_text_ids),
sequence_length=inputs['length'] - 1)
z = final_state[:, self._hparams.dim_c:]
# Encodes label
label_connector = MLPTransformConnector(self._hparams.dim_c)
# Gets the sentence representation: h = (c, z)
labels = tf.to_float(tf.reshape(inputs['labels'], [-1, 1]))
c = label_connector(labels)
c_ = label_connector(1 - labels)
h = tf.concat([c, z], 1)
h_ = tf.concat([c_, z], 1)
# Teacher-force decoding and the auto-encoding loss for G
decoder = AttentionRNNDecoder(
memory=enc_outputs,
memory_sequence_length=inputs['length'] - 1,
cell_input_fn=lambda inputs, attention: inputs,
vocab_size=vocab.size,
hparams=self._hparams.decoder)
connector = MLPTransformConnector(decoder.state_size)
g_outputs, _, _ = decoder(
initial_state=connector(h), inputs=inputs['text_ids'],
embedding=embedder, sequence_length=inputs['length'] - 1)
# sequence_sparse_softmax_cross_entropy <---> tf.nn.softmax_cross_entropy_with_logits_v2
# 1. calculate y_hat_softmax: softmax to logits(y_hat)
# 2. compute cross entropy---> y*tf.log(y_hat_softmax)
# 3. Sum over different class for an instance
loss_g_ae = tx.losses.sequence_sparse_softmax_cross_entropy(
labels=inputs['text_ids'][:, 1:],
logits=g_outputs.logits,
sequence_length=inputs['length'] - 1,
average_across_timesteps=True,
sum_over_timesteps=False)
# Gumbel-softmax decoding, used in training
start_tokens = tf.ones_like(inputs['labels']) * vocab.bos_token_id
end_token = vocab.eos_token_id
gumbel_helper = GumbelSoftmaxEmbeddingHelper(
embedder.embedding, start_tokens, end_token, gamma)
soft_outputs_, _, soft_length_, = decoder(
helper=gumbel_helper, initial_state=connector(h_))
# Greedy decoding, used in evaluation
outputs_, _, length_ = decoder(
decoding_strategy='infer_greedy', initial_state=connector(h_),
embedding=embedder, start_tokens=start_tokens, end_token=end_token)
# Creates discriminator
classifier = UnidirectionalRNNClassifier(hparams=self._hparams.classifier)
clas_embedder = WordEmbedder(vocab_size=vocab.size,
hparams=self._hparams.embedder)
# Classification loss for the classifier
clas_logits, clas_preds = classifier(
inputs=clas_embedder(ids=inputs['text_ids'][:, 1:]),
sequence_length=inputs['length'] - 1)
loss_d_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(inputs['labels']), logits=clas_logits)
print("========================================================")
print("Classifier made, number of trainable parameters:")
print(classifier.trainable_variables)
print("========================================================")
prob=tf.nn.sigmoid(clas_logits)
loss_d_clas = tf.reduce_mean(loss_d_clas)
accu_d = tx.evals.accuracy(labels=inputs['labels'], preds=clas_preds)
# Classification loss for the generator, based on soft samples
soft_logits, soft_preds = classifier(
inputs=clas_embedder(soft_ids=soft_outputs_.sample_id),
sequence_length=soft_length_)
loss_g_clas = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.to_float(1 - inputs['labels']), logits=soft_logits)
loss_g_clas = tf.reduce_mean(loss_g_clas)
# Accuracy on soft samples, for training progress monitoring
accu_g = tx.evals.accuracy(labels=1 - inputs['labels'], preds=soft_preds)
# Accuracy on greedy-decoded samples, for training progress monitoring
self.my_samples_id=outputs_.sample_id
_, gdy_preds = classifier(
inputs=clas_embedder(ids=outputs_.sample_id),
sequence_length=length_)
accu_g_gdy = tx.evals.accuracy(
labels=1 - inputs['labels'], preds=gdy_preds)
# Aggregates losses
loss_g = (lambda_ae *loss_g_ae) + (lambda_D * loss_g_clas)
loss_d = loss_d_clas
# Summaries for losses
loss_g_ae_summary = tf.summary.scalar(name='loss_g_ae_summary', tensor=loss_g_ae)
loss_g_clas_summary = tf.summary.scalar(name='loss_g_clas_summary', tensor=loss_g_clas)
# Creates optimizers
g_vars = collect_trainable_variables(
[embedder, encoder, label_connector, connector, decoder])
d_vars = collect_trainable_variables([clas_embedder, classifier])
train_op_g = get_train_op(
loss_g, g_vars, hparams=self._hparams.opt)
train_op_g_ae = get_train_op(
loss_g_ae, g_vars, hparams=self._hparams.opt)
train_op_d = get_train_op(
loss_d, d_vars, hparams=self._hparams.opt)
# Interface tensors
self.losses = {
"loss_g": loss_g,
"loss_g_ae": loss_g_ae,
"loss_g_clas": loss_g_clas,
"loss_d": loss_d_clas
}
self.metrics = {
"accu_d": accu_d,
"accu_g": accu_g,
"accu_g_gdy": accu_g_gdy
}
self.train_ops = {
"train_op_g": train_op_g,
"train_op_g_ae": train_op_g_ae,
"train_op_d": train_op_d
}
self.samples = {
"original": inputs['text_ids'],
"original_labels": inputs['labels'],
"transferred": outputs_.sample_id,
"soft_transferred": soft_outputs_.sample_id
}
self.summaries = {
"loss_g_ae_summary": loss_g_ae_summary,
"loss_g_clas_summary": loss_g_clas_summary
}
self.fetches_train_g = {
"loss_g": self.train_ops["train_op_g"],
"loss_g_ae": self.losses["loss_g_ae"],
"loss_g_clas": self.losses["loss_g_clas"],
"accu_g": self.metrics["accu_g"],
"accu_g_gdy": self.metrics["accu_g_gdy"],
"loss_g_ae_summary": self.summaries["loss_g_ae_summary"],
"loss_g_clas_summary": self.summaries["loss_g_clas_summary"]
}
self.fetches_train_d = {
"loss_d": self.train_ops["train_op_d"],
"accu_d": self.metrics["accu_d"],
"y_prob":prob,
"y_pred": clas_preds,
"y_true": inputs['labels'],
"sentences": inputs['text_ids']
}
self.fetches_dev_test_d = {
"y_prob":prob,
"y_pred": clas_preds,
"y_true": inputs['labels'],
"sentences": inputs['text_ids'],
"batch_size": get_batch_size(inputs['text_ids']),
"loss_d":self.losses['loss_d'],
"accu_d": self.metrics["accu_d"],
}
fetches_eval = {"batch_size": get_batch_size(inputs['text_ids'])}
fetches_eval.update(self.losses)
fetches_eval.update(self.metrics)
fetches_eval.update(self.samples)
self.fetches_eval = fetches_eval
