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/dports/science/py-dlib/dlib-19.22/dlib/dnn/
H A Dsolvers.h39 const float learning_rate, in operator()
64 const float learning_rate, in operator()
83 const float learning_rate, in operator()
102 const float learning_rate, in operator()
113 const float learning_rate, in operator()
151 const float learning_rate, in update_considering_bias() argument
226 const float learning_rate, in operator()
255 const float learning_rate, in operator()
274 const float learning_rate, in operator()
293 const float learning_rate, in operator()
[all …]
/dports/science/dlib-cpp/dlib-19.22/dlib/dnn/
H A Dsolvers.h39 const float learning_rate, in operator()
64 const float learning_rate, in operator()
83 const float learning_rate, in operator()
102 const float learning_rate, in operator()
113 const float learning_rate, in operator()
151 const float learning_rate, in update_considering_bias() argument
226 const float learning_rate, in operator()
255 const float learning_rate, in operator()
274 const float learning_rate, in operator()
293 const float learning_rate, in operator()
[all …]
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/linear_model/
H A D_stochastic_gradient.py106 self.learning_rate = learning_rate
545 learning_rate=learning_rate,
615 learning_rate=learning_rate,
625 learning_rate=learning_rate,
847 learning_rate=self.learning_rate,
889 learning_rate=self.learning_rate,
1186 learning_rate=learning_rate,
1368 learning_rate=learning_rate,
1923 learning_rate=learning_rate,
2116 learning_rate=learning_rate,
[all …]
/dports/science/py-scikit-learn/scikit-learn-1.0.2/sklearn/neural_network/
H A D_stochastic_optimizers.py27 self.learning_rate = float(learning_rate_init)
149 self.learning_rate = (
159 if self.learning_rate <= 1e-6:
164 self.learning_rate /= 5.0
166 print(msg + " Setting learning rate to %f" % self.learning_rate)
184 self.momentum * velocity - self.learning_rate * grad
191 self.momentum * velocity - self.learning_rate * grad
280 self.learning_rate = (
286 -self.learning_rate * m / (np.sqrt(v) + self.epsilon)
/dports/math/py-flax/flax-0.3.3/flax/optim/
H A Dlamb.py26 learning_rate: np.ndarray
45 def __init__(self, learning_rate=None, beta1=0.9, beta2=0.999, weight_decay=0, argument
60 learning_rate, beta1, beta2, weight_decay, eps)
67 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
71 learning_rate = hyper_params.learning_rate
91 new_param = param - trust_ratio * learning_rate * update
H A Dsgd.py24 learning_rate: np.ndarray
30 def __init__(self, learning_rate=None): argument
36 hyper_params = _GradientDescentHyperParams(learning_rate)
44 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
45 new_param = param - hyper_params.learning_rate * grad
H A Dadadelta.py25 learning_rate: float
46 learning_rate: float = None,
58 hyper_params = _AdadeltaHyperParams(learning_rate, rho, eps, weight_decay)
67 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
76 new_param = param - hyper_params.learning_rate * delta
77 new_param -= hyper_params.learning_rate * weight_decay * param
H A Dadagrad.py25 learning_rate: float
38 def __init__(self, learning_rate: float = None, eps=1e-8):
44 hyper_params = _AdagradHyperParams(learning_rate, eps)
54 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
56 new_param = param - hyper_params.learning_rate * grad / (jnp.sqrt(new_G) +
H A Dadam.py27 learning_rate: np.ndarray
66 learning_rate=None, argument
84 hyper_params = _AdamHyperParams(learning_rate, beta1, beta2, eps,
92 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
106 new_param = param - hyper_params.learning_rate * grad_ema_corr / denom
107 new_param -= hyper_params.learning_rate * weight_decay * param
H A Dmomentum.py26 learning_rate: np.ndarray
40 def __init__(self, learning_rate=None, beta=0.9, weight_decay=0, argument
53 learning_rate, beta, weight_decay, nesterov)
61 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
70 new_param = param - hyper_params.learning_rate * d_p
H A Drmsprop.py25 learning_rate: float
41 def __init__(self, learning_rate: float = None, beta2=0.9, eps=1e-8,
56 hyper_params = _RMSPropHyperParams(learning_rate, beta2, eps, centered)
67 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
76 new_param = param - hyper_params.learning_rate * grad / (
H A Dlars.py26 learning_rate: np.ndarray
45 def __init__(self, learning_rate=None, beta=0.9, weight_decay=0, argument
61 learning_rate, beta, weight_decay, trust_coefficient, eps, nesterov)
69 assert hyper_params.learning_rate is not None, 'no learning rate provided.'
77 scaled_lr = hyper_params.learning_rate * clipped_trust_ratio
/dports/math/py-pymc3/pymc-3.11.4/pymc3/variational/
H A Dupdates.py187 def sgd(loss_or_grads=None, params=None, learning_rate=1e-3): argument
235 updates[param] = param - learning_rate * grad
289 def momentum(loss_or_grads=None, params=None, learning_rate=1e-3, momentum=0.9): argument
345 updates = sgd(loss_or_grads, params, learning_rate)
465 updates = sgd(loss_or_grads, params, learning_rate)
469 def adagrad(loss_or_grads=None, params=None, learning_rate=1.0, epsilon=1e-6): argument
543 updates[param] = param - (learning_rate * grad / tt.sqrt(accu_new + epsilon))
589 updates[param] = param - (learning_rate * grad / tt.sqrt(accu_sum + epsilon))
773 updates[param] = param - learning_rate * update
850 a_t = learning_rate * tt.sqrt(one - beta2 ** t) / (one - beta1 ** t)
[all …]
/dports/lang/nim/nim-1.6.2/tests/typerel/
H A Dt7734.nim3 learning_rate: T
6 learning_rate: T
15 proc optimizer[M; T: SomeFloat](model: M, _: typedesc[Foo], learning_rate: T): Foo[T] =
16 result.learning_rate = learning_rate
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/gluon/house_prices/
H A Dkaggle_k_fold_cross_validation.py82 def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, argument
89 {'learning_rate': learning_rate,
105 learning_rate, weight_decay, batch_size): argument
130 learning_rate, weight_decay, batch_size)
142 learning_rate = 0.3 variable
148 learning_rate, weight_decay, batch_size)
152 def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate, argument
156 _ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
163 learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/gluon/house_prices/
H A Dkaggle_k_fold_cross_validation.py82 def train(net, X_train, y_train, epochs, verbose_epoch, learning_rate, argument
89 {'learning_rate': learning_rate,
105 learning_rate, weight_decay, batch_size): argument
130 learning_rate, weight_decay, batch_size)
142 learning_rate = 0.3 variable
148 learning_rate, weight_decay, batch_size)
152 def learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate, argument
156 _ = train(net, X_train, y_train, epochs, verbose_epoch, learning_rate,
163 learn(epochs, verbose_epoch, X_train, y_train, test, learning_rate,
/dports/math/py-deap/deap-1.3.1/examples/eda/
H A Dpbil.py27 def __init__(self, ndim, learning_rate, mut_prob, mut_shift, lambda_): argument
29 self.learning_rate = learning_rate
45 self.prob_vector[i] *= 1.0 - self.learning_rate
46 self.prob_vector[i] += value * self.learning_rate
68 pbil = PBIL(ndim=50, learning_rate=0.3, mut_prob=0.1,
/dports/misc/py-gluoncv/gluon-cv-0.9.0/gluoncv/utils/
H A Dlr_scheduler.py24 self.learning_rate = 0
39 return self.learning_rate
50 self.learning_rate = lr.learning_rate
120 return self.learning_rate
145 self.learning_rate = self.base_lr * factor
147 self.learning_rate = self.target_lr + (self.base_lr - self.target_lr) * factor
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/speech_recognition/
H A Dtrain.py42 def __init__(self, learning_rate=0.001): argument
44 self.learning_rate = learning_rate
47 return self.learning_rate
71 learning_rate = args.config.getfloat('train', 'learning_rate')
115 lr_scheduler = SimpleLRScheduler(learning_rate=learning_rate)
184 lr_scheduler.learning_rate=learning_rate/learning_rate_annealing
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/speech_recognition/
H A Dtrain.py42 def __init__(self, learning_rate=0.001): argument
44 self.learning_rate = learning_rate
47 return self.learning_rate
71 learning_rate = args.config.getfloat('train', 'learning_rate')
115 lr_scheduler = SimpleLRScheduler(learning_rate=learning_rate)
184 lr_scheduler.learning_rate=learning_rate/learning_rate_annealing
/dports/misc/mxnet/incubator-mxnet-1.9.0/example/ssd/train/
H A Dtrain_net.py48 def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio, argument
75 return (learning_rate, None)
77 lr = learning_rate
82 if lr != learning_rate:
92 prefix, ctx, begin_epoch, end_epoch, frequent, learning_rate, argument
245 learning_rate, lr_scheduler = get_lr_scheduler(learning_rate, lr_refactor_step,
247 optimizer_params={'learning_rate':learning_rate,
/dports/misc/py-mxnet/incubator-mxnet-1.9.0/example/ssd/train/
H A Dtrain_net.py48 def get_lr_scheduler(learning_rate, lr_refactor_step, lr_refactor_ratio, argument
75 return (learning_rate, None)
77 lr = learning_rate
82 if lr != learning_rate:
92 prefix, ctx, begin_epoch, end_epoch, frequent, learning_rate, argument
245 learning_rate, lr_scheduler = get_lr_scheduler(learning_rate, lr_refactor_step,
247 optimizer_params={'learning_rate':learning_rate,
/dports/math/libxsmm/libxsmm-1.16.3/src/template/
H A Dlibxsmm_dnn_optimizer_sgd_st_generic.tpl.c44 __m512 vlr = _mm512_set1_ps( handle->desc.learning_rate );
54 master[i] = master[i] - (handle->desc.learning_rate*t1.f);
66 master[i] = master[i] - (handle->desc.learning_rate*t1.f);
75 __m512 vlr = _mm512_set1_ps( handle->desc.learning_rate );
80 filter[i] = filter[i] - (handle->desc.learning_rate*dfilter[i]);
85 filter[i] = filter[i] - (handle->desc.learning_rate*dfilter[i]);
/dports/misc/orange3/orange3-3.29.1/Orange/classification/
H A Dxgb.py30 learning_rate=None, argument
58 learning_rate=learning_rate,
93 learning_rate=None, argument
121 learning_rate=learning_rate,
/dports/misc/orange3/orange3-3.29.1/Orange/regression/
H A Dxgb.py29 learning_rate=None, argument
56 super().__init__(max_depth=max_depth, learning_rate=learning_rate,
81 learning_rate=None, argument
108 super().__init__(max_depth=max_depth, learning_rate=learning_rate,

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