def get_model_and_train_step():
inputs = Input(shape=(1, ))
targets = Input(shape=(1, ))
outputs = testing_utils.Bias()(inputs)
model = Model([inputs, targets], outputs)
model.add_loss(MAE()(targets, outputs))
model.add_loss(math_ops.reduce_mean(mae(targets, outputs)))
return get_ctl_train_step(model)
def test_invalid_constant_input(self):
inputs = Input(shape=(1, ))
outputs = testing_utils.Bias()(inputs)
model = Model(inputs, outputs)
with self.assertRaisesRegex(
ValueError,
'Expected a symbolic Tensors or a callable for the loss value'
):
model.add_loss(1.)
def test_invalid_variable_input(self):
with context.eager_mode():
inputs = Input(shape=(1,))
outputs = testing_utils.Bias()(inputs)
model = Model(inputs, outputs)
with self.assertRaisesRegexp(
ValueError,
'Expected a symbolic Tensors or a callable for the loss value'):
model.add_loss(model.weights[0])
def get_model_and_train_step():
inputs = Input(shape=(1, ))
targets = Input(shape=(1, ))
outputs = testing_utils.Bias()(inputs)
model = Model([inputs, targets], outputs)
def callable_loss():
return math_ops.reduce_sum(model.weights)
model.add_loss(callable_loss)
return get_ctl_train_step(model)
def test_add_entropy_loss_on_functional_model(self):
inputs = Input(shape=(1, ))
targets = Input(shape=(1, ))
outputs = testing_utils.Bias()(inputs)
model = Model([inputs, targets], outputs)
model.add_loss(losses.binary_crossentropy(targets, outputs))
model.compile('sgd', run_eagerly=testing_utils.should_run_eagerly())
with test.mock.patch.object(logging, 'warning') as mock_log:
model.fit([self.x, self.y], batch_size=3, epochs=5)
self.assertNotIn('Gradients do not exist for variables',
str(mock_log.call_args))
def test_loss_on_model_fit(self):
inputs = Input(shape=(1,))
targets = Input(shape=(1,))
outputs = testing_utils.Bias()(inputs)
model = Model([inputs, targets], outputs)
model.add_loss(MAE()(targets, outputs))
model.add_loss(math_ops.reduce_mean(mae(targets, outputs)))
model.compile(
optimizer_v2.gradient_descent.SGD(0.05),
run_eagerly=testing_utils.should_run_eagerly())
history = model.fit([self.x, self.y], batch_size=3, epochs=5)
self.assertAllClose(history.history['loss'], [2., 1.8, 1.6, 1.4, 1.2], 1e-3)
def test_loss_with_sample_weight_on_model_fit(self):
inputs = Input(shape=(1,))
targets = Input(shape=(1,))
sw = Input(shape=(1,))
outputs = testing_utils.Bias()(inputs)
model = Model([inputs, targets, sw], outputs)
model.add_loss(MAE()(targets, outputs, sw))
model.add_loss(3 * math_ops.reduce_mean(sw * mae(targets, outputs)))
model.compile(
optimizer_v2.gradient_descent.SGD(0.025),
run_eagerly=testing_utils.should_run_eagerly())
history = model.fit([self.x, self.y, self.w], batch_size=3, epochs=5)
self.assertAllClose(history.history['loss'], [4., 3.6, 3.2, 2.8, 2.4], 1e-3)
def make_variational_autoencoder(original_dim,
intermediate_dim=512,
latent_dim=2,
reconstruction_loss_fn=binary_crossentropy):
# VAE model = encoder + decoder
# build encoder model
inputs = Input(shape=(original_dim, ), name='encoder_input')
x = Dense(intermediate_dim, activation='relu')(inputs)
z_mean = Dense(latent_dim, name='z_mean')(x)
z_log_var = Dense(latent_dim, name='z_log_var')(x)
# use reparameterization trick to push the sampling out as input
# note that "output_shape" isn't necessary with the TensorFlow backend
z = Lambda(sampling, output_shape=(latent_dim, ),
name='z')([z_mean, z_log_var])
# instantiate encoder model
encoder = Model(inputs, z, name='encoder')
# build decoder model
latent_inputs = Input(shape=(latent_dim, ), name='z_sampling')
x = Dense(intermediate_dim, activation='relu')(latent_inputs)
outputs = Dense(original_dim, activation='sigmoid')(x)
# instantiate decoder model
decoder = Model(latent_inputs, outputs, name='decoder')
# instantiate VAE model
outputs = decoder(encoder(inputs))
vae = Model(inputs, outputs, name='vae_mlp')
reconstruction_loss = reconstruction_loss_fn(inputs, outputs)
reconstruction_loss *= original_dim
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='adam')
return vae, encoder
class TransformerSeq2SeqModel(AbstractSeq2SeqModel, TFBasedModel):
custom_objects = {"ExtractLastTokenLayer": ExtractLastTokenLayer}
def _load_config(self, config):
super()._load_config(config)
self.last_token_model = None
self.max_len = self.task_config["max_len"]
self.keep_token_file = self.task_config.get("keep_token_file")
self.keep_token_ids = None
self.extra_tokens = []
self.extra_token_ids = []
self.extra_token_file = self.task_config.get("extra_token_file")
if self.extra_token_file:
self.extra_tokens = load_lines(self.extra_token_file)
def build_model(self,
pretrained_model_path=None,
pretrained_model_tag="bert",
transformer_kwargs={},
h5_file=None,
**kwargs):
with self.get_scope():
if hasattr(self, 'keep_token_ids'):
transformer_kwargs.update(keep_tokens=self.keep_token_ids)
self.nn_model = get_unilm_model(
pretrained_model_path,
pretrained_model_tag="bert",
transformer_kwargs=transformer_kwargs,
h5_file=h5_file)
logger.info("nn model's summary:")
self.nn_model.summary(print_fn=logger.info)
self._update_model_dict("test", self.nn_model)
return self.nn_model
def compile_model(self, optimizer_name: str, optimizer_args: str,
**kwargs):
logger.info(
f"compile model with optimizer_name:{optimizer_name}, optimizer_args:{optimizer_args}"
)
with self.get_scope():
input_ids, segment_ids = self.nn_model.inputs[:2]
prob_vec = self.nn_model(self.nn_model.inputs)
self.train_model = Model(inputs=self.nn_model.inputs,
outputs=prob_vec)
target_token_ids = Lambda(lambda x: x[:, 1:],
name="target_tokens")(input_ids)
prob_vec = Lambda(lambda x: x[:, :-1], name="prob_vec")(prob_vec)
loss_mask = Lambda(lambda x: x[:, 1:], name="loss_mask")(segment_ids)
loss_layer = build_classify_loss_layer(multi_label=False,
with_mask=True)
loss = loss_layer([target_token_ids, prob_vec, loss_mask])
self.train_model.add_loss(loss)
accuracy_func = masked_sparse_categorical_accuracy
metric_layer = MetricLayer(accuracy_func, name="metric_layer")
accuracy = metric_layer([target_token_ids, prob_vec, loss_mask])
self.train_model.add_metric(accuracy,
aggregation="mean",
name="accuracy")
optimizer = OptimizerFactory.create(optimizer_name, optimizer_args)
self.train_model.compile(optimizer=optimizer)
logger.info("training model's summary:")
self.train_model.summary(print_fn=logger.info)
self._update_model_dict("train", self.train_model)
self._build_gen_model()
def _build_gen_model(self):
with self.get_scope():
token_lens = Input(shape=(), dtype=tf.int32, name='token_len')
inputs = self.nn_model.inputs + [token_lens]
prob_vec = self.nn_model.output
extract_last_token_layer = ExtractLastTokenLayer(
name="extract_last_token_layer")
last_prob = extract_last_token_layer([prob_vec, token_lens])
self.gen_model = Model(inputs=inputs,
outputs=last_prob,
name="last_token_model")
logger.info("gen model's summary:")
self.gen_model.summary(print_fn=logger.info)
self._update_model_dict("gen", self.gen_model)
return self.gen_model
def example2feature(self, example: UnionSeq2SeqExample) -> Dict:
src_text = example.text + example.extra_text if example.extra_text else example.text
tgt_txt = example.tgt_text.text if isinstance(
example, LabeledSeq2SeqExample) else None
feature = self.tokenizer.do_tokenize(text=src_text, extra_text=tgt_txt)
origin_token_len = len(feature["segment_ids"]) - sum(
feature["segment_ids"])
feature.update(origin_token_len=origin_token_len)
return feature
def _feature2records(self,
idx,
feature: Dict,
mode: str,
only_copy=False) -> List[Dict]:
record = dict(idx=idx, **feature)
if mode == "gen":
record.update(score=0.)
origin_token_len = record["origin_token_len"]
record["token_ids"] = record["token_ids"][:origin_token_len]
record["tokens"] = record["tokens"][:origin_token_len]
record["segment_ids"] = record["segment_ids"][:origin_token_len]
record.update(token_len=len(record["tokens"]))
truncate_record(record=record,
max_len=self.max_len,
keys=["token_ids", "segment_ids", "tokens"])
return [record]
def _gen_predict(self, records, topk, batch_size, only_copy=False):
dataset_type, dataset_shape = self.get_dataset_info(mode="gen")
test_batches = records2batches(records, dataset_shape, batch_size)
logger.info("predicting with tf model...")
pred_tensor_data = []
for batch in test_batches:
pred_batch = self.gen_model(batch, training=False)
pred_tensor_data.extend(pred_batch)
topk_pred_data = tf.math.top_k(pred_tensor_data, topk)
topk_prob_data = topk_pred_data.values.numpy().tolist()
topk_id_data = topk_pred_data.indices.numpy().tolist()
preds = []
for token_ids, probs in zip(topk_id_data, topk_prob_data):
pred = [(i, self.tokenizer.id2token(i), p)
for i, p in zip(token_ids, probs)]
preds.append(pred)
return preds
@discard_kwarg
@log_cost_time
def _post_predict(self,
features,
pred_tensors,
show_detail=False,
threshold=.5) -> List[List[GenText]]:
def _tensor2output(feature, pred_tensor) -> List[str]:
token_len = len(feature["tokens"])
origin_token_len = feature["origin_token_len"]
pred_tensor = pred_tensor[origin_token_len - 1:token_len - 2]
pred_token_ids = tf.argmax(pred_tensor, axis=-1).numpy().tolist()
pred_tokens = [self.tokenizer.id2token(e) for e in pred_token_ids]
if show_detail:
logger.info("pred tokens:")
logger.info(list(zip(pred_token_ids, pred_tokens)))
# text = self.tokenizer.decode(pred_tokens)
text = "".join(pred_tokens)
return [GenText(text=text)]
preds = [
_tensor2output(feature, tensor)
for feature, tensor in zip(features, pred_tensors)
]
return preds
def _predict(self,
data_manager: DataManager,
batch_size,
mode="test",
gen_kwargs={},
max_pred_num=None,
tf_serving_url=None,
show_detail=False,
**kwargs) -> List[List[GenText]]:
if mode == "test":
return super()._predict(data_manager=data_manager,
batch_size=batch_size,
show_detail=show_detail,
max_pred_num=max_pred_num,
tf_serving_url=tf_serving_url,
**kwargs)
dataset = self._pre_process(data_manager=data_manager,
batch_size=batch_size,
mode=mode,
max_num=max_pred_num)
pred_tensors = self._model_predict(dataset=dataset,
model=self.nn_model,
tf_serving_url=tf_serving_url,
show_detail=show_detail)
features = data_manager.get_features(max_num=max_pred_num)
preds = self._post_predict(features=features,
pred_tensors=pred_tensors,
show_detail=show_detail)
return preds
if mode == "gen":
beam_searcher = BeamSearcher(pred_func=self._gen_predict)
records = data_manager.get_records(mode=mode,
return_generator=False,
max_num=max_pred_num)
preds = beam_searcher.run(records=records,
batch_size=batch_size,
show_detail=show_detail,
**gen_kwargs)
assert len(preds) == len(records)
preds = [[
GenText(text="".join(
item["tokens"][item["origin_token_len"]:-1]),
prob=math.exp(item["score"])) for item in pred
] for pred in preds]
return preds
raise ValueError(f"invalid mode:{mode}")
args = parser.parse_args()
models = (encoder, decoder)
data = (x_test, y_test)
# VAE loss = mse_loss or xent_loss + kl_loss
if args.mse:
reconstruction_loss = mse(inputs, outputs)
else:
reconstruction_loss = binary_crossentropy(inputs, outputs)
reconstruction_loss *= original_dim
kl_loss = 1 + z_log_var - K.square(z_mean) - K.exp(z_log_var)
kl_loss = K.sum(kl_loss, axis=-1)
kl_loss *= -0.5
vae_loss = K.mean(reconstruction_loss + kl_loss)
vae.add_loss(vae_loss)
vae.compile(optimizer='adam')
vae.summary()
plot_model(vae, to_file='vae_mlp.png', show_shapes=True)
if args.weights:
vae.load_weights(args.weights)
else:
# train the autoencoder
vae.fit(x_train,
epochs=epochs,
batch_size=batch_size,
validation_data=(x_test, None))
vae.save_weights('vae_mlp_mnist.h5')
plot_results(models, data, batch_size=batch_size, model_name="vae_mlp")
