| /external/tensorflow/tensorflow/python/training/ |
| momentum.py | 16 """Momentum for TensorFlow."""
30 """Optimizer that implements the Momentum algorithm.
35 accumulation = momentum * accumulation + gradient
46 def __init__(self, learning_rate, momentum,
47 use_locking=False, name="Momentum", use_nesterov=False):
48 """Construct a new Momentum optimizer.
52 momentum: A `Tensor` or a floating point value. The momentum.
55 gradients. Defaults to "Momentum".
56 use_nesterov: If `True` use Nesterov Momentum
[all...] |
| momentum_test.py | 15 """Tests for Momentum."""
35 from tensorflow.python.training import momentum as momentum_lib
40 def _update_nesterov_momentum_numpy(self, var, accum, g, lr, momentum):
41 var = var + accum * lr * momentum
42 accum = accum * momentum + g
44 var = var - accum * lr * momentum
60 momentum = lambda: 0.9
63 momentum = momentum()
65 learning_rate=learning_rate, momentum=momentum
[all...] |
| rmsprop_test.py | 40 # learning_rate, decay, momentum, epsilon, centered, use_resource
59 def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, decay, momentum,
68 mom_t = momentum * mom + lr * g / np.sqrt(denom_t, dtype=denom_t.dtype)
73 lr, decay, momentum, epsilon, centered):
86 mom_t[gindex] = momentum * mom[gindex] + lr * gvalue / np.sqrt(denom_t)
92 for (dtype, learning_rate, decay, momentum,
112 momentum=momentum,
127 mom0 = opt.get_slot(var0, "momentum")
129 mom1 = opt.get_slot(var1, "momentum")
[all...] |
| slot_creator.py | 27 accumulators = {var : create_zeros_slot(var, "momentum") for var in vs}
|
| rmsprop.py | 25 mom = momentum * mom{t-1} + learning_rate * g_t / sqrt(mean_square + epsilon)
28 This implementation of RMSProp uses plain momentum, not Nesterov momentum.
35 mom = momentum * mom{t-1} + learning_rate * g_t /
64 momentum=0.0,
72 corresponding accumulators (momentum, gradient moving average, square
74 (i.e. accumulators will decay, momentum will be applied). The sparse
86 momentum: A scalar tensor.
99 self._momentum = momentum
103 # Tensors for learning rate and momentum. Created in _prepare
[all...] |
| /external/tensorflow/tensorflow/compiler/tests/ |
| momentum_test.py | 15 """Tests for Momentum."""
30 from tensorflow.python.training import momentum as momentum_lib
35 def _update_nesterov_momentum_numpy(self, var, accum, g, lr, momentum):
36 var += accum * lr * momentum
37 accum = accum * momentum + g
39 var -= accum * lr * momentum
50 learning_rate=2.0, momentum=0.9)
55 self.assertEqual(["momentum"], mom_opt.get_slot_names())
56 slot0 = mom_opt.get_slot(var0, "momentum")
59 slot1 = mom_opt.get_slot(var1, "momentum")
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/layers/ |
| normalization.py | 45 momentum: Momentum for the moving average.
78 momentum=0.99,
94 momentum=momentum,
121 'momentum': self.momentum,
|
| normalization_test.py | 35 'momentum': 0.9,
84 norm = keras.layers.BatchNormalization(input_shape=(10,), momentum=0.8)
103 axis=1, input_shape=(3, 4, 4), momentum=0.8)
|
| /external/tensorflow/tensorflow/python/layers/ |
| normalization_test.py | 316 axis=1, epsilon=epsilon, momentum=0.9)
358 axis=2, epsilon=epsilon, momentum=0.9)
399 axis=1, epsilon=epsilon, momentum=0.9)
439 axis=2, epsilon=epsilon, momentum=0.9)
479 axis=3, epsilon=epsilon, momentum=0.9)
519 axis=3, epsilon=epsilon, momentum=0.9, fused=True)
560 axis=1, epsilon=epsilon, momentum=0.9, fused=True)
600 axis=-1, epsilon=epsilon, momentum=0.9)
641 axis=-1, epsilon=epsilon, momentum=0.9)
685 momentum=0.9
[all...] |
| normalization.py | 63 momentum: Momentum for the moving average.
92 renorm_momentum: Momentum used to update the moving means and standard
93 deviations with renorm. Unlike `momentum`, this affects training
95 (which would give stale estimates). Note that `momentum` is still applied
124 momentum=0.99,
151 self.momentum = momentum
322 self._one_minus_decay = 1.0 - self.momentum
603 var, value, self.momentum, zero_debias=False
[all...] |
| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| multitask_optimizer_wrapper_test.py | 29 from tensorflow.python.training import momentum
43 mom_opt_impl = momentum.MomentumOptimizer(learning_rate=2.0, momentum=0.9)
56 self.assertEqual(["momentum"], mom_opt.get_slot_names())
57 slot0 = mom_opt.get_slot(var0, "momentum")
59 slot1 = mom_opt.get_slot(var1, "momentum")
62 # Step 1: normal momentum update.
64 # Check that the momentum accumulators have been updated.
76 # Step 2: momentum update that changes only slot1 but not slot0.
78 # Check that only the relevant momentum accumulator has been updated
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/ |
| optimizers_test.py | 80 momentum=0.9,
119 momentum=0.9,
125 momentum=0.9,
|
| /external/tensorflow/tensorflow/core/kernels/ |
| training_ops_gpu.cu.cc | 87 typename TTypes<T>::ConstScalar momentum, bool use_nesterov) {
91 accum.device(d) = accum * momentum.reshape(single).broadcast(bcast) + grad;
94 accum * momentum.reshape(single).broadcast(bcast) *
151 typename TTypes<T>::ConstScalar momentum,
162 mom * momentum.reshape(single).broadcast(bcast) +
176 typename TTypes<T>::ConstScalar momentum,
188 mom.device(d) = mom * momentum.reshape(single).broadcast(bcast) +
|
| training_ops.h | 126 typename TTypes<T>::ConstScalar momentum, bool use_nesterov);
148 typename TTypes<T>::ConstScalar momentum,
160 typename TTypes<T>::ConstScalar momentum,
|
| training_ops.cc | 273 typename TTypes<T>::ConstScalar momentum, bool use_nesterov) {
274 accum.device(d) = accum * momentum() + grad;
276 var.device(d) -= grad * lr() + accum * momentum() * lr();
337 typename TTypes<T>::ConstScalar momentum,
342 mom * momentum() + (grad * lr()) / ((ms + epsilon()).sqrt());
354 typename TTypes<T>::ConstScalar momentum,
360 mom.device(d) = mom * momentum() + (grad * lr()) / denom.sqrt();
2305 const Tensor& momentum = ctx->input(4); variable
2417 const Tensor& momentum = ctx->input(5); variable
2776 const Tensor& momentum = ctx->input(5); variable
2865 const Tensor& momentum = ctx->input(6); variable
3016 const Tensor& momentum = ctx->input(5); variable
3148 const Tensor& momentum = ctx->input(6); variable
[all...] |
| /external/tensorflow/tensorflow/python/keras/_impl/keras/applications/ |
| nasnet.py | 235 axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')(
497 momentum=0.9997,
512 momentum=0.9997,
582 momentum=0.9997,
600 momentum=0.9997,
635 momentum=0.9997,
718 momentum=0.9997,
|
| /external/tensorflow/tensorflow/contrib/eager/python/ |
| saver_test.py | 35 from tensorflow.python.training import momentum
229 optimizer = momentum.MomentumOptimizer(
231 momentum=0.5)
|
| /external/tensorflow/tensorflow/contrib/kfac/python/kernel_tests/ |
| optimizer_test.py | 75 momentum=0.5,
129 0.1, 0.2, 0.3, layers, momentum=0.5, momentum_type='regular')
187 momentum=0.5,
|
| /external/tensorflow/tensorflow/contrib/kfac/python/ops/ |
| optimizer.py | 44 momentum=0.9,
73 momentum: The momentum decay constant to use. Only applies when
75 momentum_type: The type of momentum to use in this optimizer, one of
79 specified value. May only be used with momentum type 'regular'.
97 ValueError: If the momentum type is unsupported.
98 ValueError: If clipping is used with momentum type other than 'regular'.
100 ValueError: If momentum is non-zero and momentum_type is not 'regular'
122 raise ValueError("Unsupported momentum type {}. Must be one of {}."
125 raise ValueError("Update clipping is only supported with momentum"
[all...] |
| /external/tensorflow/tensorflow/contrib/kfac/examples/ |
| mlp.py | 130 momentum=0.99)
299 momentum=0.99)
|
| convnet.py | 206 momentum=0.9)
288 momentum=0.9)
|
| /external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
| estimators_test.py | 36 from tensorflow.python.training import momentum as momentum_lib
175 return momentum_lib.MomentumOptimizer(learning_rate=0.01, momentum=0.9)
|
| dynamic_rnn_estimator.py | 34 from tensorflow.python.training import momentum as momentum_opt
555 momentum=None,
617 'Ftrl', 'Momentum', 'RMSProp' or 'SGD. See `layers.optimize_loss` for
624 momentum: Momentum value. Only used if `optimizer_type` is 'Momentum'.
670 if optimizer == 'Momentum':
671 optimizer = momentum_opt.MomentumOptimizer(learning_rate, momentum)
|
| state_saving_rnn_estimator.py | 36 from tensorflow.python.training import momentum as momentum_opt
544 momentum=None,
577 one of 'Adagrad', 'Adam', 'Ftrl', Momentum', 'RMSProp', or 'SGD'.
583 momentum: Momentum value. Only used if `optimizer_type` is 'Momentum'.
627 if optimizer_type == 'Momentum':
628 optimizer_type = momentum_opt.MomentumOptimizer(learning_rate, momentum)
|
| /external/tensorflow/tensorflow/compiler/tf2xla/kernels/ |
| training_ops.cc | 93 errors::InvalidArgument("momentum is not a scalar: ",
98 xla::ComputationDataHandle momentum = ctx->Input(4); variable
100 accum = b->Add(b->Mul(accum, momentum), grad);
105 var, b->Add(b->Mul(grad, lr), b->Mul(b->Mul(accum, momentum), lr)));
278 errors::InvalidArgument("momentum is not a scalar: ",
302 xla::ComputationDataHandle momentum = ctx->Input(5); variable
307 // mom <- momentum * mom_{t-1} + lr * grad / sqrt(ms + epsilon)
328 b->Add(b->Mul(mom, momentum),
|
