def compile_model(self, optimizer_name, optimizer_args, rdrop_alpha=None):
logger.info("compiling model...")
with self.get_scope():
token_output = Input(shape=(None, ),
name='token_output',
dtype=tf.int32)
self.train_model = Model(self.nn_model.inputs + [token_output],
self.nn_model.output,
name="train_model")
output = self.train_model.output
loss_mask = Lambda(
function=lambda x: tf.cast(tf.not_equal(x, 0), tf.float32),
name="pred_mask")(token_output)
loss_layer = build_classify_loss_layer(multi_label=False,
with_mask=True)
loss = loss_layer([token_output, output, 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([token_output, output, 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)
def compile_model(self, optimizer_name: str, optimizer_args: dict):
logger.info(
f"compile model with optimizer_name:{optimizer_name}, optimizer_args:{optimizer_args}"
)
with self.get_scope():
classify_output = Input(shape=(self.label_num, None, None),
dtype=tf.float32,
name='classify_output')
token_ids, segment_ids = self.nn_model.inputs
output = self.nn_model([token_ids, segment_ids])
self.train_model = Model(
inputs=[token_ids, segment_ids, classify_output],
outputs=[output])
loss_layer = LossLayer(loss_func=global_pointer_crossentropy,
name="loss_layer")
loss = loss_layer([classify_output, output])
self.train_model.add_loss(loss)
accuracy_func = global_pointer_f1_score
metric_layer = MetricLayer(accuracy_func, name="metric_layer")
metric = metric_layer([classify_output, output])
self.train_model.add_metric(metric,
aggregation="mean",
name="global_pointer_f1_score")
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)
def compile_model(self, optimizer_name, optimizer_args, rdrop_alpha=None):
logger.info("compiling model...")
with self.get_scope():
classify_output = Input(shape=(self.label_num,) if self.multi_label else (), name='classify_output', dtype=tf.float32)
inputs = self.nn_model.inputs
output = self.nn_model.output
loss_input = [classify_output, output]
if rdrop_alpha:
output1 = self.nn_model(inputs)
loss_input.append(output1)
output = Lambda(function=lambda x: sum(x) / len(x), name="avg_pool_layer")([output, output1])
self.train_model = Model(inputs + [classify_output], output, name="train_model")
loss_layer = build_classify_loss_layer(multi_label=self.multi_label, rdrop_alpha=rdrop_alpha)
loss = loss_layer(loss_input)
self.train_model.add_loss(loss)
accuracy_func = binary_accuracy if self.multi_label else sparse_categorical_accuracy
metric_layer = MetricLayer(accuracy_func, name="metric_layer")
accuracy = metric_layer([classify_output, output])
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)
def compile_model(self, optimizer_name: str, optimizer_args: dict,
**kwargs):
logger.info(
f"compile model with optimizer_name:{optimizer_name}, optimizer_args:{optimizer_args}"
)
with self.get_scope():
classify_labels = Input(
shape=(None, self.label_num) if self.multi_label else (None, ),
name='classify_labels',
dtype=tf.int32)
token_ids, segment_ids = self.nn_model.inputs
output = self.nn_model([token_ids, segment_ids])
self.train_model = Model(
inputs=[token_ids, segment_ids, classify_labels],
outputs=[output])
loss_mask = Lambda(
function=lambda x: tf.cast(tf.not_equal(x, 0), tf.float32),
name="pred_mask")(token_ids)
# 计算loss的时候，过滤掉pad token的loss
loss_layer = build_classify_loss_layer(multi_label=self.multi_label,
with_mask=True)
loss = loss_layer([classify_labels, output, loss_mask])
self.train_model.add_loss(loss)
# 计算accuracy的时候，过滤掉pad token 的accuracy
masked_accuracy_func = masked_binary_accuracy if self.multi_label else masked_sparse_categorical_accuracy
metric_layer = MetricLayer(masked_accuracy_func)
masked_accuracy = metric_layer([classify_labels, output, loss_mask])
self.train_model.add_metric(masked_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)
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()