leras: added AdaBelief optimizer

This commit is contained in:
iperov 2020-12-11 15:47:47 +04:00
parent 254a7cf5cf
commit e7d36b4287
4 changed files with 101 additions and 17 deletions

View file

@ -76,25 +76,28 @@ class Saveable():
if self.name is None:
raise Exception("name must be defined.")
tuples = []
for w in weights:
w_name_split = w.name.split('/')
if self.name != w_name_split[0]:
raise Exception("weight first name != Saveable.name")
try:
tuples = []
for w in weights:
w_name_split = w.name.split('/')
if self.name != w_name_split[0]:
raise Exception("weight first name != Saveable.name")
sub_w_name = "/".join(w_name_split[1:])
sub_w_name = "/".join(w_name_split[1:])
w_val = d.get(sub_w_name, None)
w_val = d.get(sub_w_name, None)
if w_val is None:
#io.log_err(f"Weight {w.name} was not loaded from file {filename}")
tuples.append ( (w, w.initializer) )
else:
w_val = np.reshape( w_val, w.shape.as_list() )
tuples.append ( (w, w_val) )
nn.batch_set_value(tuples)
if w_val is None:
#io.log_err(f"Weight {w.name} was not loaded from file {filename}")
tuples.append ( (w, w.initializer) )
else:
w_val = np.reshape( w_val, w.shape.as_list() )
tuples.append ( (w, w_val) )
nn.batch_set_value(tuples)
except:
return False
return True
def init_weights(self):

View file

@ -0,0 +1,80 @@
from tensorflow.python.ops import control_flow_ops, state_ops
from core.leras import nn
tf = nn.tf
class AdaBelief(nn.OptimizerBase):
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, lr_dropout=1.0, lr_cos=0, epsilon=1e-7, clipnorm=0.0, name=None, **kwargs):
super().__init__(name=name)
if name is None:
raise ValueError('name must be defined.')
self.lr = lr
self.beta_1 = beta_1
self.beta_2 = beta_2
self.lr_dropout = lr_dropout
self.lr_cos = lr_cos
self.clipnorm = clipnorm
self.epsilon = epsilon
with tf.device('/CPU:0') :
with tf.variable_scope(self.name):
self.iterations = tf.Variable(0, dtype=tf.int64, name='iters')
self.ms_dict = {}
self.vs_dict = {}
self.lr_rnds_dict = {}
def get_weights(self):
return [self.iterations] + list(self.ms_dict.values()) + list(self.vs_dict.values())
def initialize_variables(self, trainable_weights, vars_on_cpu=True, lr_dropout_on_cpu=False):
# Initialize here all trainable variables used in training
e = tf.device('/CPU:0') if vars_on_cpu else None
if e: e.__enter__()
with tf.variable_scope(self.name):
ms = { v.name : tf.get_variable ( f'ms_{v.name}'.replace(':','_'), v.shape, dtype=v.dtype, initializer=tf.initializers.constant(0.0), trainable=False) for v in trainable_weights }
vs = { v.name : tf.get_variable ( f'vs_{v.name}'.replace(':','_'), v.shape, dtype=v.dtype, initializer=tf.initializers.constant(0.0), trainable=False) for v in trainable_weights }
self.ms_dict.update (ms)
self.vs_dict.update (vs)
if self.lr_dropout != 1.0:
e = tf.device('/CPU:0') if lr_dropout_on_cpu else None
if e: e.__enter__()
lr_rnds = [ nn.random_binomial( v.shape, p=self.lr_dropout, dtype=v.dtype) for v in trainable_weights ]
if e: e.__exit__(None, None, None)
self.lr_rnds_dict.update ( { v.name : rnd for v,rnd in zip(trainable_weights,lr_rnds) } )
if e: e.__exit__(None, None, None)
def get_update_op(self, grads_vars):
updates = []
if self.clipnorm > 0.0:
norm = tf.sqrt( sum([tf.reduce_sum(tf.square(g)) for g,v in grads_vars]))
updates += [ state_ops.assign_add( self.iterations, 1) ]
for i, (g,v) in enumerate(grads_vars):
if self.clipnorm > 0.0:
g = self.tf_clip_norm(g, self.clipnorm, norm)
ms = self.ms_dict[ v.name ]
vs = self.vs_dict[ v.name ]
m_t = self.beta_1*ms + (1.0-self.beta_1) * g
v_t = self.beta_2*vs + (1.0-self.beta_2) * tf.square(g-m_t)
lr = tf.constant(self.lr, g.dtype)
if self.lr_cos != 0:
lr *= (tf.cos( tf.cast(self.iterations, g.dtype) * (2*3.1415926535/ float(self.lr_cos) ) ) + 1.0) / 2.0
v_diff = - lr * m_t / (tf.sqrt(v_t) + self.epsilon)
if self.lr_dropout != 1.0:
lr_rnd = self.lr_rnds_dict[v.name]
v_diff *= lr_rnd
new_v = v + v_diff
updates.append (state_ops.assign(ms, m_t))
updates.append (state_ops.assign(vs, v_t))
updates.append (state_ops.assign(v, new_v))
return control_flow_ops.group ( *updates, name=self.name+'_updates')
nn.AdaBelief = AdaBelief

View file

@ -3,7 +3,7 @@ from core.leras import nn
tf = nn.tf
class RMSprop(nn.OptimizerBase):
def __init__(self, lr=0.001, rho=0.9, lr_dropout=1.0, epsilon=1e-7, clipnorm=0.0, name=None):
def __init__(self, lr=0.001, rho=0.9, lr_dropout=1.0, epsilon=1e-7, clipnorm=0.0, name=None, **kwargs):
super().__init__(name=name)
if name is None:

View file

@ -1,2 +1,3 @@
from .OptimizerBase import *
from .RMSprop import *
from .RMSprop import *
from .AdaBelief import *