| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | legacy_learning_rate_decay.py | 32 def exponential_decay(learning_rate, argument
99 learning_rate, decay_steps, decay_rate, staircase=staircase, name=name)
186 def polynomial_decay(learning_rate, argument
272 learning_rate,
287 def natural_exp_decay(learning_rate, argument
361 learning_rate,
375 def inverse_time_decay(learning_rate, argument
448 learning_rate, decay_steps, decay_rate, staircase=staircase, name=name)
458 def cosine_decay(learning_rate, global_step, decay_steps, alpha=0.0, name=None): argument
511 learning_rate, decay_steps, alpha=alpha, name=name)
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| D | adagrad_test.py | 85 learning_rate = lambda: 3.0 function
87 learning_rate = learning_rate()
89 ada_opt = adagrad.Adagrad(learning_rate)
136 learning_rate = 3.0
139 ada_opt = adagrad.Adagrad(learning_rate, decay=decay)
160 lr_np = learning_rate / (1 + decay * t)
178 learning_rate = 3.0
180 ada_opt = adagrad.Adagrad(learning_rate, epsilon=1.0)
218 learning_rate = 3.0
221 learning_rate, decay_steps=1.0, decay_rate=decay)
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| D | rmsprop_test.py | 106 for (dtype, learning_rate, rho, momentum, epsilon, centered) in _TESTPARAMS:
119 learning_rate=learning_rate,
163 var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate, rho,
166 var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate, rho,
193 learning_rate = 0.01
200 learning_rate=learning_rate,
236 lr = learning_rate / (1 + decay * t)
265 learning_rate = 0.01
272 learning_rate, decay_steps=1.0, decay_rate=decay)
274 learning_rate=lr_schedule,
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| D | gradient_descent_test.py | 94 learning_rate = 3.0
96 sgd = gradient_descent.SGD(learning_rate=learning_rate, decay=decay)
102 learning_rate = learning_rate_schedule.InverseTimeDecay(
104 sgd = gradient_descent.SGD(learning_rate=learning_rate)
110 learning_rate = learning_rate_schedule.InverseTimeDecay(
112 sgd = gradient_descent.SGD(learning_rate=learning_rate)
288 opt_2 = gradient_descent.SGD(learning_rate=0.1, lr=1.0)
289 opt_3 = gradient_descent.SGD(learning_rate=0.1)
314 learning_rate = 2.0
317 learning_rate=learning_rate, momentum=momentum)
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| D | adam_test.py | 219 learning_rate = lambda: 0.001 function
224 learning_rate = learning_rate()
229 opt = adam.Adam(learning_rate=learning_rate)
369 learning_rate = 0.001
376 learning_rate=learning_rate,
387 lr_np = learning_rate / (1 + decay * t)
414 learning_rate = 0.001
417 learning_rate, decay_steps=1.0, decay_rate=decay)
423 learning_rate=lr_schedule,
434 lr_np = learning_rate / (1 + decay * t)
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| D | optimizer_v2_test.py | 120 sgd.learning_rate = 0.5
131 sgd.learning_rate = learning_rate_schedule.InverseTimeDecay(
289 opt = gradient_descent.SGD(learning_rate=1.0)
342 opt = gradient_descent.SGD(learning_rate=1.0, clipvalue=1.0)
353 opt = gradient_descent.SGD(learning_rate=1.0, clipnorm=1.0)
366 opt = gradient_descent.SGD(learning_rate=1.0, global_clipnorm=2.0)
378 gradient_descent.SGD(learning_rate=1.0, clipnorm=-1.0)
385 opt = gradient_descent.SGD(learning_rate=1.0, **{clip_type: 2.0})
393 gradient_descent.SGD(learning_rate=1.0, invalidkwargs=1.0)
398 opt1 = adam.Adam(learning_rate=1.0)
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| D | nadam.py | 68 learning_rate=0.001, argument
76 learning_rate = kwargs.get('lr', learning_rate)
77 if isinstance(learning_rate, learning_rate_schedule.LearningRateSchedule):
83 self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
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| /external/tensorflow/tensorflow/python/keras/benchmarks/keras_examples_benchmarks/ |
| D | mnist_irnn_benchmark_test.py | 32 self.learning_rate = 1e-6
72 optimizer=tf.keras.optimizers.RMSprop(learning_rate=self.learning_rate),
88 optimizer=tf.keras.optimizers.RMSprop(learning_rate=self.learning_rate),
104 optimizer=tf.keras.optimizers.RMSprop(learning_rate=self.learning_rate),
125 optimizer=tf.keras.optimizers.RMSprop(learning_rate=self.learning_rate),
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| D | cifar10_cnn_benchmark_test.py | 81 optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, decay=1e-6),
98 optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, decay=1e-6),
115 optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, decay=1e-6),
137 optimizer=tf.keras.optimizers.RMSprop(learning_rate=0.0001, decay=1e-6),
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| /external/tensorflow/tensorflow/python/training/ |
| D | momentum.py | 46 def __init__(self, learning_rate, momentum, argument
81 self._learning_rate = learning_rate
90 learning_rate = self._learning_rate
91 if callable(learning_rate):
92 learning_rate = learning_rate()
93 self._learning_rate_tensor = ops.convert_to_tensor(learning_rate,
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| D | gradient_descent.py | 34 def __init__(self, learning_rate, use_locking=False, name="GradientDescent"): argument
52 self._learning_rate = learning_rate
80 learning_rate = self._call_if_callable(self._learning_rate)
82 learning_rate, name="learning_rate")
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| D | rmsprop_test.py | 95 for (dtype, learning_rate, decay, momentum,
113 learning_rate=learning_rate,
151 var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate,
154 var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate,
177 learning_rate=1.0,
201 learning_rate=1.0,
219 for (dtype, learning_rate, decay,
239 learning_rate=learning_rate,
277 learning_rate, decay, momentum, epsilon, centered)
280 learning_rate, decay, momentum, epsilon, centered)
[all …]
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| D | adagrad.py | 41 def __init__(self, learning_rate, initial_accumulator_value=0.1, argument
67 self._learning_rate = learning_rate
93 learning_rate = self._call_if_callable(self._learning_rate)
95 learning_rate, name="learning_rate")
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| D | momentum_test.py | 61 learning_rate = lambda: 2.0 function
64 learning_rate = learning_rate()
67 learning_rate=learning_rate, momentum=momentum)
181 learning_rate=2.0, momentum=0.9, use_nesterov=True)
215 learning_rate=2.0, momentum=0.9, use_nesterov=True)
255 opt = momentum_lib.MomentumOptimizer(learning_rate=1.0, momentum=0.0)
280 opt = momentum_lib.MomentumOptimizer(learning_rate=1.0, momentum=0.0)
295 learning_rate=constant_op.constant(2.0),
454 mom_opt = momentum_lib.MomentumOptimizer(learning_rate=0.1, momentum=0.1)
474 learning_rate=2.0, momentum=0.9)
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_embedding.py | 72 learning_rate=None, argument
133 if learning_rate is not None and learning_rate_fn is not None:
136 learning_rate, learning_rate_fn))
147 combiner, hot_id_replication, learning_rate,
372 learning_rate: float,
381 self.learning_rate = learning_rate
419 learning_rate: float,
449 learning_rate=learning_rate,
475 learning_rate: float,
511 learning_rate=learning_rate,
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| D | tpu_embedding_v2_utils.py | 55 learning_rate: Union[float, Callable[[], float]],
63 self.learning_rate = learning_rate
216 learning_rate: Union[float, Callable[[], float]] = 0.01,
247 learning_rate, use_gradient_accumulation, clip_weight_min,
318 learning_rate: float = 0.001,
354 learning_rate, use_gradient_accumulation, clip_weight_min,
433 learning_rate: Union[float, Callable[[], float]] = 0.001,
486 learning_rate, use_gradient_accumulation, clip_weight_min,
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| /external/webrtc/rtc_base/ |
| D | rolling_accumulator.h | 109 double ComputeWeightedMean(double learning_rate) const { in ComputeWeightedMean() argument
110 if (count() < 1 || learning_rate <= 0.0 || learning_rate >= 1.0) { in ComputeWeightedMean()
118 current_weight *= learning_rate; in ComputeWeightedMean()
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| /external/tensorflow/tensorflow/core/kernels/boosted_trees/ |
| D | training_ops.cc | 79 const auto learning_rate = learning_rate_t->scalar<float>()(); in Compute() local
84 FindBestSplitsPerNode(context, learning_rate, node_ids_list, gains_list, in Compute()
170 OpKernelContext* const context, const float learning_rate, in FindBestSplitsPerNode() argument
200 learning_rate * left_node_contribs(candidate_idx, 0)); in FindBestSplitsPerNode()
202 learning_rate * right_node_contribs(candidate_idx, 0)); in FindBestSplitsPerNode()
281 const auto learning_rate = learning_rate_t->scalar<float>()(); in Compute() local
289 FindBestSplitsPerNode(context, learning_rate, node_ids_list, gains_list, in Compute()
390 OpKernelContext* const context, const float learning_rate, in FindBestSplitsPerNode() argument
427 learning_rate * left_node_contribs(candidate_idx, i)); in FindBestSplitsPerNode()
429 learning_rate * right_node_contribs(candidate_idx, i)); in FindBestSplitsPerNode()
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| /external/tensorflow/tensorflow/python/keras/mixed_precision/ |
| D | loss_scale_optimizer_test.py | 177 learning_rate = 2.
178 expected_gradient = variables.Variable(learning_rate /
182 opt = gradient_descent.SGD(learning_rate)
202 2 * learning_rate / strategy.num_replicas_in_sync))
221 learning_rate = 2.
225 opt = gradient_descent.SGD(learning_rate, **{clip_type: 2.0})
293 learning_rate = 2.
296 opt = gradient_descent.SGD(learning_rate)
398 opt = adam.Adam(learning_rate=1.0, beta_1=0.5, beta_2=0.9)
408 self.assertIs(lso.lr, lso.learning_rate)
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | rmsprop_test.py | 75 learning_rate = 3.0
76 rms_opt = rmsprop.RMSPropOptimizer(learning_rate, centered=centered)
108 learning_rate,
116 learning_rate,
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| /external/tensorflow/tensorflow/python/keras/distribute/ |
| D | keras_premade_models_test.py | 73 opt = gradient_descent.SGD(learning_rate=0.1)
88 linear_opt = gradient_descent.SGD(learning_rate=0.05)
89 dnn_opt = adagrad.Adagrad(learning_rate=0.1)
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| D | simple_models.py | 54 optimizer = gradient_descent.SGD(learning_rate=0.001)
79 optimizer = gradient_descent.SGD(learning_rate=0.001)
109 optimizer = gradient_descent.SGD(learning_rate=0.001)
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| /external/tensorflow/tensorflow/python/kernel_tests/boosted_trees/ |
| D | training_ops_test.py | 72 learning_rate=0.1,
179 learning_rate=0.1,
288 learning_rate=0.1,
401 learning_rate=0.1,
615 learning_rate=0.1,
813 learning_rate=0.1,
1016 learning_rate=0.1,
1231 learning_rate=0.1,
1459 learning_rate=0.1,
1611 learning_rate=0.1,
[all …]
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| /external/tensorflow/tensorflow/python/keras/engine/ |
| D | training_eager_test.py | 83 optimizer = rmsprop.RMSprop(learning_rate=0.001)
155 optimizer = rmsprop.RMSprop(learning_rate=0.001)
179 model.compile(optimizer=rmsprop.RMSprop(learning_rate=0.001),
204 optimizer = rmsprop.RMSprop(learning_rate=0.001)
248 optimizer=rmsprop.RMSprop(learning_rate=0.001, **optimizer_kwargs),
269 optimizer=rmsprop.RMSprop(learning_rate=0.001, clipvalue=0.0),
291 optimizer=rmsprop.RMSprop(learning_rate=0.001),
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| /external/tensorflow/tensorflow/core/ops/compat/ops_history_v1/ |
| D | BoostedTreesUpdateEnsembleV2.pbtxt | 51 name: "learning_rate"
115 name: "learning_rate"
187 name: "learning_rate"
|