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SAE: added option "simple optimizer" allows to train bigger networks on same VRAM

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nnlib: added DFLOptimizer is my own optimizer
This commit is contained in:
iperov 2019-03-12 09:32:35 +04:00
parent 3bad8dd8ec
commit fd3b9add2f
2 changed files with 30 additions and 20 deletions
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29
nnlib/nnlib.py
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@ -72,7 +72,7 @@ RandomNormal = keras.initializers.RandomNormal
Model = keras.models.Model
Adam = keras.optimizers.Adam
FastAdam = nnlib.FastAdam
DFLOptimizer = nnlib.DFLOptimizer
modelify = nnlib.modelify
gaussian_blur = nnlib.gaussian_blur
@ -434,16 +434,14 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
return dict(list(base_config.items()) + list(config.items()))
nnlib.Scale = Scale
class FastAdam(keras.optimizers.Optimizer):
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, iterations=0, **kwargs):
super(FastAdam, self).__init__(**kwargs)
class DFLOptimizer(keras.optimizers.Optimizer):
def __init__(self, lr=0.001, **kwargs):
super(DFLOptimizer, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(iterations, dtype='int64', name='iterations')
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.lr = K.variable(lr, name='lr')
self.beta_1 = K.variable(beta_1, name='beta_1')
self.beta_2 = K.variable(beta_2, name='beta_2')
self.epsilon = K.epsilon()
self.beta_1 = K.variable(0.9, name='beta_1')
self.beta_2 = K.variable(0.998, name='beta_2')
@keras.legacy.interfaces.legacy_get_updates_support
def get_updates(self, loss, params):
@ -451,16 +449,16 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
self.updates = [K.update_add(self.iterations, 1)]
lr = self.lr
t = K.cast(self.iterations, K.floatx()) + 1
t = ( K.cast(self.iterations, K.floatx()) ) % 1000 + 1
lr_t = lr * (K.sqrt(1. - K.pow(self.beta_2, t)) /
(1. - K.pow(self.beta_1, t)))
self.weights = [self.iterations]
self.weights = []
for p, g in zip(params, grads):
m_t = (1. - self.beta_1) * g
v_t = (1. - self.beta_2) * K.square(g)
p_t = p - lr_t * m_t / (K.sqrt(v_t) + self.epsilon)
p_t = p - lr_t * m_t / (K.sqrt(v_t) + K.epsilon() )
new_p = p_t
# Apply constraints.
@ -471,15 +469,14 @@ NLayerDiscriminator = nnlib.NLayerDiscriminator
return self.updates
def get_config(self):
config = {'iterations': int(K.get_value(self.iterations)),
'lr': float(K.get_value(self.lr)),
config = {'lr': float(K.get_value(self.lr)),
'beta_1': float(K.get_value(self.beta_1)),
'beta_2': float(K.get_value(self.beta_2))
}
base_config = super(FastAdam, self).get_config()
base_config = super(DFLOptimizer, self).get_config()
return dict(list(base_config.items()) + list(config.items()))
nnlib.FastAdam = FastAdam
nnlib.DFLOptimizer = DFLOptimizer
'''
not implemented in plaidML