本文整理汇总了Python中neon.optimizers.GradientDescentMomentum方法的典型用法代码示例。如果您正苦于以下问题：Python optimizers.GradientDescentMomentum方法的具体用法？Python optimizers.GradientDescentMomentum怎么用？Python optimizers.GradientDescentMomentum使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类neon.optimizers的用法示例。
在下文中一共展示了optimizers.GradientDescentMomentum方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_model
# 需要导入模块: from neon import optimizers [as 别名]
# 或者: from neon.optimizers import GradientDescentMomentum [as 别名]
def create_model(args, hyper_params):
# setup layers
imagenet_layers = [
Conv((11, 11, 64), init=Gaussian(scale=0.01), bias=Constant(0), activation=Rectlin(),
padding=3, strides=4),
Pooling(3, strides=2),
Conv((5, 5, 192), init=Gaussian(scale=0.01), bias=Constant(1), activation=Rectlin(),
padding=2),
Pooling(3, strides=2),
Conv((3, 3, 384), init=Gaussian(scale=0.03), bias=Constant(0), activation=Rectlin(),
padding=1),
Conv((3, 3, 256), init=Gaussian(scale=0.03), bias=Constant(1), activation=Rectlin(),
padding=1),
Conv((3, 3, 256), init=Gaussian(scale=0.03), bias=Constant(1), activation=Rectlin(),
padding=1),
Pooling(3, strides=2),
Affine(nout=4096, init=Gaussian(scale=0.01), bias=Constant(1), activation=Rectlin()),
Dropout(keep=0.5),
Affine(nout=4096, init=Gaussian(scale=0.01), bias=Constant(1), activation=Rectlin()),
# The following layers are used in Alexnet, but are not used in the new model
Dropout(keep=0.5),
# Affine(nout=1000, init=Gaussian(scale=0.01), bias=Constant(-7), activation=Softmax())
]
target_layers = imagenet_layers + [
Affine(nout=4096, init=Gaussian(scale=0.005), bias=Constant(.1), activation=Rectlin()),
Dropout(keep=0.5),
Affine(nout=21, init=Gaussian(scale=0.01), bias=Constant(0), activation=Softmax())]
# setup optimizer
opt = GradientDescentMomentum(hyper_params.learning_rate_scale,
hyper_params.momentum, wdecay=0.0005,
schedule=hyper_params.learning_rate_sched)
# setup model
if args.model_file:
model = Model(layers=args.model_file)
else:
model = Model(layers=target_layers)
return model, opt 示例2: get_function
# 需要导入模块: from neon import optimizers [as 别名]
# 或者: from neon.optimizers import GradientDescentMomentum [as 别名]
def get_function(name):
mapping = {}
# activation
mapping['relu'] = neon.transforms.activation.Rectlin
mapping['sigmoid'] = neon.transforms.activation.Logistic
mapping['tanh'] = neon.transforms.activation.Tanh
mapping['linear'] = neon.transforms.activation.Identity
# loss
mapping['mse'] = neon.transforms.cost.MeanSquared
mapping['binary_crossentropy'] = neon.transforms.cost.CrossEntropyBinary
mapping['categorical_crossentropy'] = neon.transforms.cost.CrossEntropyMulti
# optimizer
def SGD(learning_rate=0.01, momentum_coef=0.9, gradient_clip_value=5):
return GradientDescentMomentum(learning_rate, momentum_coef, gradient_clip_value)
mapping['sgd'] = SGD
mapping['rmsprop'] = neon.optimizers.optimizer.RMSProp
mapping['adam'] = neon.optimizers.optimizer.Adam
mapping['adagrad'] = neon.optimizers.optimizer.Adagrad
mapping['adadelta'] = neon.optimizers.optimizer.Adadelta
mapped = mapping.get(name)
if not mapped:
raise Exception('No neon function found for "{}"'.format(name))
return mapped