def build(self):
print('***************************build model***************************')
self.model = ModelWrapper.model(conf, train=True, vocab_size=self.vocab_size, labels_num=self.labels_num)
self.model.compile(loss=self.loss, optimizer=self.optimizer, metrics=self.metrics) # optimizer=Adam() keras.optimizers.Adam(lr=args.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
# model.compile(loss=focal_loss(), optimizer='adam', metrics=["accuracy"]) #optimizer=Adam() keras.optimizers.Adam(lr=args.learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-8)
self.model.summary()
def build(self):
print(
'***************************build model***************************'
)
self.model = ModelWrapper.model(conf,
train=True,
vocab_size=self.vocab_size,
labels_num=1)
self.model.compile('adam', 'mse', metrics=['accuracy'])
self.model.summary()
def build(self):
print(
'***************************build model***************************'
)
self.model = ModelWrapper.model(conf,
train=True,
vocab_size=self.vocab_size,
labels_num=1)
adam_optimizer = tf.keras.optimizers.Adam(lr=1e-3,
decay=1e-6,
clipvalue=5)
self.model.compile(loss='binary_crossentropy',
optimizer=adam_optimizer,
metrics=['binary_crossentropy', 'accuracy'])
self.model.summary()
def build(self, model_file=None):
print(
'***************************build model***************************'
)
self.model = ModelWrapper.model(conf,
train=True,
vocab_size=self.vocab_size,
labels_num=self.labels_num)
adam_optimizer = tf.keras.optimizers.Adam(lr=1e-3,
decay=1e-6,
clipvalue=5)
#self.model.compile(loss=[self.focal_loss(gamma=2., alpha=.25)], optimizer=self.optimizer, metrics=self.metrics)
self.model.compile(loss=self.loss,
optimizer=self.optimizer,
metrics=self.metrics)
if not model_file is None:
self.model.load_weights(model_file, by_name=True)
self.model.summary()