def to_bigdl_optim_method(koptim_method):
# koptim_method is always an object
import tensorflow.keras.backend as K
import tensorflow.keras.optimizers as koptimizers
import bigdl.optim.optimizer as boptimizer
lr = float(K.eval(koptim_method.lr))
decay = float(K.eval(koptim_method.decay))
if isinstance(koptim_method, koptimizers.Adagrad):
warnings.warn("For Adagrad, we don't support epsilon for now")
return boptimizer.Adagrad(learningrate=lr,
learningrate_decay=decay)
elif isinstance(koptim_method, koptimizers.SGD):
momentum = float(K.eval(koptim_method.momentum))
return boptimizer.SGD(learningrate=lr,
learningrate_decay=decay,
momentum=momentum,
nesterov=koptim_method.nesterov)
elif isinstance(koptim_method, koptimizers.Adam):
beta1 = float(K.eval(koptim_method.beta_1))
beta2 = float(K.eval(koptim_method.beta_2))
return boptimizer.Adam(learningrate=lr,
learningrate_decay=decay,
beta1=beta1,
beta2=beta2,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.RMSprop):
rho = float(K.eval(koptim_method.rho))
return boptimizer.RMSprop(learningrate=lr,
learningrate_decay=decay,
decayrate=rho,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.Adadelta):
warnings.warn(
"For Adadelta, we don't support learning rate and learning rate decay for now"
)
return boptimizer.Adadelta(decayrate=koptim_method.rho,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.Adamax):
beta1 = float(K.eval(koptim_method.beta_1))
beta2 = float(K.eval(koptim_method.beta_2))
warnings.warn(
"For Adamax, we don't support learning rate decay for now")
return boptimizer.Adamax(learningrate=lr,
beta1=beta1,
beta2=beta2,
epsilon=koptim_method.epsilon)
else:
raise Exception("We don't support %s for now" % koptim_method)
def to_bigdl_optim_method(koptim_method):
# koptim_method is always an object
import tensorflow.keras.backend as K
import tensorflow.keras.optimizers as koptimizers
import bigdl.optim.optimizer as boptimizer
import tensorflow.train as tftrain
import tensorflow as tf
from tensorflow.python.keras.optimizers import TFOptimizer
if isinstance(koptim_method, TFOptimizer):
koptim_method = koptim_method.optimizer
if isinstance(koptim_method, boptimizer.OptimMethod):
return koptim_method
elif isinstance(koptim_method, koptimizers.Optimizer):
lr = float(K.eval(koptim_method.lr))
decay = float(K.eval(koptim_method.decay))
if isinstance(koptim_method, koptimizers.Adagrad):
warnings.warn("For Adagrad, we don't support epsilon for now")
return boptimizer.Adagrad(learningrate=lr,
learningrate_decay=decay)
elif isinstance(koptim_method, koptimizers.SGD):
momentum = float(K.eval(koptim_method.momentum))
return boptimizer.SGD(learningrate=lr,
learningrate_decay=decay,
momentum=momentum,
nesterov=koptim_method.nesterov)
elif isinstance(koptim_method, koptimizers.Adam):
beta1 = float(K.eval(koptim_method.beta_1))
beta2 = float(K.eval(koptim_method.beta_2))
return boptimizer.Adam(learningrate=lr,
learningrate_decay=decay,
beta1=beta1,
beta2=beta2,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.RMSprop):
rho = float(K.eval(koptim_method.rho))
return boptimizer.RMSprop(learningrate=lr,
learningrate_decay=decay,
decayrate=rho,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.Adadelta):
warnings.warn(
"For Adadelta, we don't support learning rate and learning rate decay for now")
return boptimizer.Adadelta(decayrate=koptim_method.rho,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.Adamax):
beta1 = float(K.eval(koptim_method.beta_1))
beta2 = float(K.eval(koptim_method.beta_2))
warnings.warn("For Adamax, we don't support learning rate decay for now")
return boptimizer.Adamax(learningrate=lr,
beta1=beta1,
beta2=beta2,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, tftrain.Optimizer):
def get_value(v):
if isinstance(v, (tf.Tensor, tf.SparseTensor, tf.Variable)):
return float(K.eval(v))
else:
return float(v)
if isinstance(koptim_method, tftrain.GradientDescentOptimizer):
lr = get_value(koptim_method._learning_rate)
return boptimizer.SGD(learningrate=lr)
elif isinstance(koptim_method, tftrain.MomentumOptimizer):
lr = get_value(koptim_method._learning_rate)
momentum = get_value(koptim_method._momentum)
use_nesterov = koptim_method._use_nesterov
return boptimizer.SGD(learningrate=lr, momentum=momentum, nesterov=use_nesterov)
elif isinstance(koptim_method, tftrain.AdagradOptimizer):
lr = get_value(koptim_method._learning_rate)
return boptimizer.Adagrad(learningrate=lr)
elif isinstance(koptim_method, tftrain.AdamOptimizer):
lr = get_value(koptim_method._lr)
beta1 = get_value(koptim_method._beta1)
beta2 = get_value(koptim_method._beta2)
epsilon = get_value(koptim_method._epsilon)
return boptimizer.Adam(learningrate=lr, beta1=beta1, beta2=beta2, epsilon=epsilon)
elif isinstance(koptim_method, tftrain.RMSPropOptimizer):
lr = get_value(koptim_method._learning_rate)
decay = get_value(koptim_method._decay)
momentum = get_value(koptim_method._momentum)
epsilon = get_value(koptim_method._epsilon)
centered = get_value(koptim_method._centered)
if momentum != 0.0 or centered:
warnings.warn(
"For RMSPropOptimizer, we don't support momentum and centered for now")
return boptimizer.RMSprop(learningrate=lr,
learningrate_decay=decay,
epsilon=epsilon)
elif isinstance(koptim_method, tftrain.AdadeltaOptimizer):
lr = get_value(koptim_method._lr)
rho = get_value(koptim_method._rho)
epsilon = get_value(koptim_method._epsilon)
warnings.warn(
"For Adadelta, we don't support learning rate for now")
return boptimizer.Adadelta(decayrate=rho, epsilon=epsilon)
raise ValueError("We don't support %s for now" % koptim_method)
def to_bigdl_optim_method(koptim_method):
if isinstance(koptim_method, six.string_types):
koptim_method = koptim_method.lower()
if koptim_method == "adagrad":
return boptimizer.Adagrad(learningrate=0.01)
elif koptim_method == "sgd":
return boptimizer.SGD(learningrate=0.01)
elif koptim_method == "adam":
return boptimizer.Adam()
elif koptim_method == "rmsprop":
return boptimizer.RMSprop(learningrate=0.001, decayrate=0.9)
elif koptim_method == "adadelta":
return boptimizer.Adadelta(decayrate=0.95, epsilon=1e-8)
elif koptim_method == "adamax":
return boptimizer.Adamax(epsilon=1e-8)
else:
unsupport_exp(koptim_method)
else:
# koptim_method a Keras object
lr = float(K.eval(koptim_method.lr))
decay = float(K.eval(koptim_method.decay))
if isinstance(koptim_method, koptimizers.Adagrad):
warnings.warn("For Adagrad, we don't support epsilon for now")
return boptimizer.Adagrad(learningrate=lr,
learningrate_decay=decay)
elif isinstance(koptim_method, koptimizers.SGD):
momentum = float(K.eval(koptim_method.momentum))
return boptimizer.SGD(learningrate=lr,
learningrate_decay=decay,
momentum=momentum,
nesterov=koptim_method.nesterov)
elif isinstance(koptim_method, koptimizers.Adam):
beta1 = float(K.eval(koptim_method.beta_1))
beta2 = float(K.eval(koptim_method.beta_2))
return boptimizer.Adam(learningrate=lr,
learningrate_decay=decay,
beta1=beta1,
beta2=beta2,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.RMSprop):
rho = float(K.eval(koptim_method.rho))
return boptimizer.RMSprop(learningrate=lr,
learningrate_decay=decay,
decayrate=rho,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.Adadelta):
warnings.warn(
"For Adadelta, we don't support learning rate and learning rate decay for now"
)
return boptimizer.Adadelta(decayrate=koptim_method.rho,
epsilon=koptim_method.epsilon)
elif isinstance(koptim_method, koptimizers.Adamax):
beta1 = float(K.eval(koptim_method.beta_1))
beta2 = float(K.eval(koptim_method.beta_2))
warnings.warn(
"For Adamax, we don't support learning rate decay for now")
return boptimizer.Adamax(learningrate=lr,
beta1=beta1,
beta2=beta2,
epsilon=koptim_method.epsilon)
else:
unsupport_exp(koptim_method)
