Python Adam.max_grad_norm Examples

Example #1

File: _ml.py Project: cs394-s20/Aqua

def create_default_optimizer(ops, **cfg):
    learn_rate = util.env_opt("learn_rate", 0.001)
    beta1 = util.env_opt("optimizer_B1", 0.9)
    beta2 = util.env_opt("optimizer_B2", 0.999)
    eps = util.env_opt("optimizer_eps", 1e-8)
    L2 = util.env_opt("L2_penalty", 1e-6)
    max_grad_norm = util.env_opt("grad_norm_clip", 1.0)
    optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1, beta2=beta2, eps=eps)
    optimizer.max_grad_norm = max_grad_norm
    optimizer.device = ops.device
    return optimizer

Example #2

File: _ml.py Project: spacy-io/spaCy

def create_default_optimizer(ops, **cfg):
    learn_rate = util.env_opt("learn_rate", 0.001)
    beta1 = util.env_opt("optimizer_B1", 0.9)
    beta2 = util.env_opt("optimizer_B2", 0.999)
    eps = util.env_opt("optimizer_eps", 1e-8)
    L2 = util.env_opt("L2_penalty", 1e-6)
    max_grad_norm = util.env_opt("grad_norm_clip", 1.0)
    optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1, beta2=beta2, eps=eps)
    optimizer.max_grad_norm = max_grad_norm
    optimizer.device = ops.device
    return optimizer

Example #3

def create_default_optimizer(ops, **cfg):
    learn_rate = util.env_opt('learn_rate', 0.001)
    beta1 = util.env_opt('optimizer_B1', 0.9)
    beta2 = util.env_opt('optimizer_B2', 0.999)
    eps = util.env_opt('optimizer_eps', 1e-08)
    L2 = util.env_opt('L2_penalty', 1e-6)
    max_grad_norm = util.env_opt('grad_norm_clip', 1.)
    optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1, beta2=beta2, eps=eps)
    optimizer.max_grad_norm = max_grad_norm
    optimizer.device = ops.device
    return optimizer

Example #4

File: _ml.py Project: IndicoDataSolutions/spaCy

def create_default_optimizer(ops, **cfg):
    learn_rate = util.env_opt('learn_rate', 0.001)
    beta1 = util.env_opt('optimizer_B1', 0.9)
    beta2 = util.env_opt('optimizer_B2', 0.999)
    eps = util.env_opt('optimizer_eps', 1e-08)
    L2 = util.env_opt('L2_penalty', 1e-6)
    max_grad_norm = util.env_opt('grad_norm_clip', 1.)
    optimizer = Adam(ops, learn_rate, L2=L2, beta1=beta1,
                     beta2=beta2, eps=eps)
    optimizer.max_grad_norm = max_grad_norm
    optimizer.device = ops.device
    return optimizer