| /external/tensorflow/tensorflow/python/keras/optimizer_v2/ |
| D | adam_test.py | 45 beta2=0.999, argument
47 lr_t = lr * np.sqrt(1 - beta2**(t + 1)) / (1 - beta1**(t + 1))
50 v_t = beta2 * v + (1 - beta2) * g_t * g_t
64 beta2=0.999, argument
66 lr_t = lr * np.sqrt(1 - beta2**(t + 1)) / (1 - beta1**(t + 1))
69 v_t = beta2 * v + (1 - beta2) * g_t * g_t
85 beta2=0.999, argument
89 lr_t = lr * np.sqrt(1 - beta2**(t + 1)) / (1 - beta1**(t + 1))
91 v_t_slice = beta2 * v[indices] + (1 - beta2) * g_t * g_t
222 beta2 = lambda: 0.999 function
[all …]
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| D | nadam_test.py | 57 beta2=0.999, argument
65 v_t = beta2 * v + (1 - beta2) * g_t * g_t
68 v_prime_t = v_t / (1 - beta2**(t + 1))
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| D | adamax_test.py | 43 beta2=0.999, argument
46 v_t = np.maximum(beta2 * v, np.abs(g_t))
59 beta2=0.999, argument
63 v_t_slice = np.maximum(beta2 * v[indices], np.abs(g_t))
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| /external/tensorflow/tensorflow/python/training/ |
| D | training_ops_test.py | 277 beta2 = np.array(0.999, dtype=var.dtype)
279 beta2_power = beta2**t
283 beta2_t = constant_op.constant(beta2, self._toType(var.dtype), [])
292 beta2, epsilon)
300 def _adamUpdateNumpy(self, param, g_t, t, m, v, alpha, beta1, beta2, epsilon): argument
301 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
304 v_t = beta2 * v + (1 - beta2) * g_t * g_t
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| D | adam_test.py | 44 beta2=0.999, argument
46 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
49 v_t = beta2 * v + (1 - beta2) * g_t * g_t
184 beta2 = lambda: 0.999 function
189 beta2 = beta2()
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| D | adam.py | 42 beta2=0.999, argument
100 self._beta2 = beta2
137 beta2 = self._call_if_callable(self._beta2)
142 self._beta2_t = ops.convert_to_tensor(beta2, name="beta2")
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| /external/tensorflow/tensorflow/contrib/opt/python/training/ |
| D | nadam_optimizer_test.py | 39 beta2=0.999, argument
41 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
44 v_t = beta2 * v + (1 - beta2) * g_t * g_t
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| D | weight_decay_optimizers_test.py | 38 beta2=0.999, epsilon=1e-8): argument
39 lr_t = lr * np.sqrt(1 - beta2**t) / (1 - beta1**t)
42 v_t = beta2 * v + (1 - beta2) * g_t * g_t
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| D | lazy_adam_optimizer_test.py | 45 beta2=0.999, argument
47 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
50 v_t = beta2 * v + (1 - beta2) * g_t * g_t
188 beta2 = lambda: 0.999 function
193 beta2 = beta2()
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| D | adam_gs_optimizer.py | 44 beta2=0.999, argument
108 self._beta2 = beta2
132 beta2 = self._call_if_callable(self._beta2)
137 self._beta2_t = ops.convert_to_tensor(beta2, name="beta2")
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| D | adam_gs_optimizer_test.py | 45 beta2=0.999, argument
47 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
50 v_t = beta2 * v + (1 - beta2) * g_t * g_t
193 beta2 = lambda: 0.999 function
198 beta2 = beta2()
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| D | lazy_adam_gs_optimizer_test.py | 45 beta2=0.999, argument
47 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
50 v_t = beta2 * v + (1 - beta2) * g_t * g_t
212 beta2 = lambda: 0.999 function
217 beta2 = beta2()
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| D | weight_decay_optimizers.py | 359 def __init__(self, weight_decay, learning_rate=0.001, beta1=0.9, beta2=0.999, argument
380 weight_decay, learning_rate=learning_rate, beta1=beta1, beta2=beta2,
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| D | adamax_test.py | 44 beta2=0.999, argument
47 v_t = np.maximum(beta2 * v, np.abs(g_t))
60 beta2=0.999, argument
64 v_t_slice = np.maximum(beta2 * v[indices], np.abs(g_t))
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| D | adamax.py | 40 def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, argument
86 super(AdaMaxOptimizer, self).__init__(learning_rate, beta1, beta2,
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| /external/tensorflow/tensorflow/core/kernels/ |
| D | training_ops_gpu.cu.cc | 133 typename TTypes<T>::ConstScalar beta2, in operator ()()
144 v + (beta2.constant(one) - beta2).reshape(single).broadcast(bcast) * in operator ()()
177 typename TTypes<T>::ConstScalar beta2, in operator ()()
188 v + (beta2.constant(one) - beta2).reshape(single).broadcast(bcast) * in operator ()()
208 typename TTypes<T>::ConstScalar beta2, in operator ()()
219 (beta2.reshape(single).broadcast(bcast) * v).cwiseMax(grad.abs()); in operator ()()
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| D | training_ops.h | 146 typename TTypes<T>::ConstScalar beta2,
160 typename TTypes<T>::ConstScalar beta2,
172 typename TTypes<T>::ConstScalar beta2,
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| /external/tensorflow/tensorflow/compiler/tests/ |
| D | adam_test.py | 41 beta2=0.999, argument
43 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
46 v_t = beta2 * v + (1 - beta2) * g_t * g_t
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| D | adamax_test.py | 40 beta2=0.999, argument
43 v_t = np.maximum(beta2 * v, np.abs(g_t))
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| /external/speex/libspeexdsp/ |
| D | scal.c | 156 float beta, beta2; in speex_decorrelate() local
186 beta2 = beta; in speex_decorrelate()
205 if (max_alpha > .98/(1.+beta2)) in speex_decorrelate()
206 max_alpha = .98/(1.+beta2); in speex_decorrelate()
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| /external/tensorflow/tensorflow/python/tpu/ |
| D | tpu_embedding.py | 154 beta2=0.999, argument
181 if beta2 < 0. or beta2 >= 1.:
182 raise ValueError('beta2 must be between 0. and 1; got {}.'.format(beta2))
190 self.beta2 = beta2
882 table_descriptor.optimization_parameters.adam.beta2 = (
883 self._optimization_parameters.beta2)
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_ApplyAdaMax.pbtxt | 41 name: "beta2"
75 v_t <- max(beta2 * v_{t-1}, abs(g))
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| D | api_def_ResourceApplyAdaMax.pbtxt | 41 name: "beta2"
69 v_t <- max(beta2 * v_{t-1}, abs(g))
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| /external/tensorflow/tensorflow/contrib/optimizer_v2/ |
| D | adam_test.py | 44 beta2=0.999, argument
46 alpha_t = alpha * np.sqrt(1 - beta2**t) / (1 - beta1**t)
49 v_t = beta2 * v + (1 - beta2) * g_t * g_t
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| D | adam.py | 37 def __init__(self, learning_rate=0.001, beta1=0.9, beta2=0.999, epsilon=1e-8, argument
94 self._set_hyper("beta2", beta2)
|