def get_optimizer(self, multitask):
"""Get the optimizer."""
optconf = self.config['solver']['optimizer']
method = optconf['name']
learning_rate = self.get_learning_rate()
if method == 'adadelta':
opt = tf.train.AdadeltaOptimizer(learning_rate)
elif method == 'adam':
opt = tf.train.AdamOptimizer(learning_rate)
elif method == 'adagrad':
opt = tf.train.AdagradOptimizer(learning_rate)
elif method == 'momentum':
opt = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)
elif method == 'rmsprop':
opt = tf.train.RMSPropOptimizer(learning_rate)
elif method == 'gradientdecent':
opt = tf.train.GradientDescentOptimizer(learning_rate)
elif method == 'lazyadam':
opt = tf.contrib.opt.LazyAdamOptimizer(learning_rate)
elif method == 'weightedadam':
weight_decay = self.config['solver']['optimizer']['weight_decay']
opt = tf.contrib.opt.AdamWOptimizer(weight_decay=weight_decay,
learning_rate=learning_rate)
elif method == 'yellowfin':
opt = optimizer.YFOptimizer(learning_rate)
else:
raise ValueError("Not support optimizer: {}".format(method))
if multitask:
opt = tf.contrib.opt.MultitaskOptimizerWrapper(opt)
logging.info("Using multi-task optimizer")
return opt
def get_optimizer(self):
"""Get the optimizer."""
optconf = self.config['solver']['optimizer']
method = optconf['name']
learning_rate = self.get_learning_rate()
if method == 'adadelta':
opt = tf.train.AdadeltaOptimizer(learning_rate)
elif method == 'adam':
opt = tf.train.AdamOptimizer(learning_rate)
elif method == 'adagrad':
opt = tf.train.AdagradOptimizer(learning_rate)
elif method == 'momentum':
opt = tf.train.MomentumOptimizer(learning_rate, momentum=0.9)
elif method == 'rmsprop':
opt = tf.train.RMSPropOptimizer(learning_rate)
elif method == 'gradientdecent':
opt = tf.train.GradientDescentOptimizer(learning_rate)
elif method == 'lazyadam':
opt = tfa.optimizers.LazyAdam(learning_rate)
elif method == 'weightedadam':
weight_decay = self.config['solver']['optimizer']['weight_decay']
opt = tfa.optimizers.AdamW(
weight_decay=weight_decay, learning_rate=learning_rate)
elif method == 'yellowfin':
opt = optimizer.YFOptimizer(learning_rate)
else:
raise ValueError("Not support optimizer: {}".format(method))
return opt