/external/tensorflow/tensorflow/core/kernels/ |
cwise_op_gpu_sigmoid.cu.cc | 23 DEFINE_UNARY3(sigmoid, Eigen::half, float, double);
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cwise_op_sigmoid.cc | 20 REGISTER5(UnaryOp, CPU, "Sigmoid", functor::sigmoid, float, Eigen::half, double, 23 REGISTER3(UnaryOp, GPU, "Sigmoid", functor::sigmoid, float, Eigen::half, 27 REGISTER(UnaryOp, SYCL, "Sigmoid", functor::sigmoid, float);
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/external/libopus/src/ |
mlp.h | 41 int sigmoid; member in struct:__anon29285
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mlp.c | 88 if (layer->sigmoid) {
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/external/tensorflow/tensorflow/contrib/model_pruning/python/layers/ |
rnn_cells.py | 139 sigmoid = math_ops.sigmoid 161 multiply(c, sigmoid(add(f, forget_bias_tensor))), 162 multiply(sigmoid(i), self._activation(j))) 163 new_h = multiply(self._activation(new_c), sigmoid(o)) 299 sigmoid = math_ops.sigmoid 321 sigmoid(f + self._forget_bias + self._w_f_diag * c_prev) * c_prev + 322 sigmoid(i + self._w_i_diag * c_prev) * self._activation(j)) 325 sigmoid(f + self._forget_bias) * c_prev [all...] |
/external/eigen/unsupported/test/ |
cxx11_tensor_math.cpp | 34 Tensor<float, 1> vec2 = vec1.sigmoid();
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/external/tensorflow/tensorflow/contrib/rnn/python/ops/ |
rnn_cell.py | 244 sigmoid = math_ops.sigmoid 294 f_act = sigmoid(f + self._forget_bias + w_f_diag * c_prev) 296 f_act = sigmoid(f + self._forget_bias) 304 m = sigmoid(o + w_o_diag * c) * self._activation(c) 306 m = sigmoid(o) * self._activation(c) 407 sigmoid = math_ops.sigmoid 450 sigmoid(f + self._forget_bias + w_f_diag * c_prev) * c_prev + 451 sigmoid(i + w_i_diag * c_prev) * tanh(j) [all...] |
/external/tensorflow/tensorflow/compiler/tests/ |
lstm.py | 72 in_gate = math_ops.sigmoid(in_gate) 73 forget_gate = math_ops.sigmoid(forget_gate) 74 out_gate = math_ops.sigmoid(out_gate)
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/external/tensorflow/tensorflow/contrib/keras/api/keras/activations/ |
__init__.py | 27 from tensorflow.python.keras.activations import sigmoid
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/external/tensorflow/tensorflow/python/keras/ |
activations.py | 180 @keras_export('keras.activations.sigmoid') 181 def sigmoid(x): function 182 """Sigmoid activation function. 188 The sigmoid activation: `(1.0 / (1.0 + exp(-x)))`. 190 return nn.sigmoid(x) 208 """Hard sigmoid activation function. 210 Faster to compute than sigmoid activation. 216 Hard sigmoid activation:
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activations_test.py | 40 'sigmoid', 'hard_sigmoid', 'linear', 123 sigmoid = np.vectorize(ref_sigmoid) 126 f = keras.backend.function([x], [keras.activations.sigmoid(x)]) 129 expected = sigmoid(test_values)
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/external/tensorflow/tensorflow/python/ops/ |
nn.py | 32 from tensorflow.python.ops.math_ops import sigmoid
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rnn_cell_impl.py | 584 value = math_ops.sigmoid(gate_inputs) 758 sigmoid = math_ops.sigmoid 779 new_c = add(multiply(c, sigmoid(add(f, forget_bias_tensor))), 780 multiply(sigmoid(i), self._activation(j))) 781 new_h = multiply(self._activation(new_c), sigmoid(o)) [all...] |
/external/tensorflow/tensorflow/contrib/boosted_trees/estimator_batch/ |
distillation_loss.py | 43 return math_ops.sigmoid(logits)
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/external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
sigmoid.py | 15 """Sigmoid bijector.""" 28 "Sigmoid", 32 class Sigmoid(bijector.Bijector): 43 def __init__(self, validate_args=False, name="sigmoid"): 44 super(Sigmoid, self).__init__( 50 return math_ops.sigmoid(x)
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/external/tensorflow/tensorflow/lite/experimental/examples/lstm/ |
rnn_cell.py | 416 sigmoid = math_ops.sigmoid 479 sigmoid(f + self._w_f_diag * c_prev) * c_prev + 480 sigmoid(i + self._w_i_diag * c_prev) * self._activation(j)) 482 c = (sigmoid(f) * c_prev + sigmoid(i) * self._activation(j)) 489 m = sigmoid(o + self._w_o_diag * c) * self._activation(c) 491 m = sigmoid(o) * self._activation(c)
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/external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ |
sigmoid_test.py | 15 """Sigmoid Tests.""" 24 from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import Sigmoid 35 self.assertEqual("sigmoid", Sigmoid().name) 39 bijector = Sigmoid() 49 assert_scalar_congruency(Sigmoid(), lower_x=-7., upper_x=7.) 57 Sigmoid(), x, y, event_ndims=0, atol=0., rtol=1e-4)
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/external/tensorflow/tensorflow/contrib/specs/python/ |
specs_ops.py | 81 Cs = Fun(layers.conv2d, activation_fn=math_ops.sigmoid) 90 Fs = Fun(layers.fully_connected, activation_fn=math_ops.sigmoid) 119 Sig = Fun(math_ops.sigmoid) 125 """Depth-wise convolution + sigmoid (used after LSTM)."""
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/external/tensorflow/tensorflow/python/ops/distributions/ |
bernoulli.py | 63 where the probability of an event is sigmoid(logits). Only one of 155 return (-self.logits * (math_ops.sigmoid(self.logits) - 1) + 186 return (math_ops.sigmoid(a.logits) * delta_probs0 187 + math_ops.sigmoid(-a.logits) * delta_probs1)
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/external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
relaxed_bernoulli.py | 22 from tensorflow.contrib.distributions.python.ops.bijectors.sigmoid import Sigmoid 57 random variable followed by a `tf.sigmoid` op, one solution is to treat 58 the Logistic as the random variable and `tf.sigmoid` as downstream. The 59 KL divergences of two Logistics, which are always followed by a `tf.sigmoid` 92 Creates three continuous distributions, whose sigmoid approximate 3 Bernoullis 100 sigmoid_samples = tf.sigmoid(samples) 159 event is sigmoid(logits). Only one of `logits` or `probs` should be 191 bijector=Sigmoid(validate_args=validate_args),
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logistic.py | 202 return math_ops.sigmoid(self._z(x)) 208 return math_ops.sigmoid(-self._z(x))
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negative_binomial.py | 170 math_ops.sigmoid(-self.logits)) 200 return self._mean() / math_ops.sigmoid(-self.logits)
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/external/tensorflow/tensorflow/contrib/boosted_trees/python/utils/ |
losses.py | 185 modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive 186 class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in 205 modified_predictions are 1 if sigmoid is >= 0.5+eps (eg we predict positive 206 class), -1 if sigmoid < 0.5-eps (e.g. we predict negative class) and ax+b in 246 probs = math_ops.sigmoid(logits)
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/external/tensorflow/tensorflow/contrib/labeled_tensor/ |
__init__.py | 76 sigmoid = _core.sigmoid variable
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/external/tensorflow/tensorflow/contrib/learn/python/learn/estimators/ |
dnn.py | 68 "sigmoid": nn.sigmoid 107 "sigmoid". 340 name of the op may also be provided, e.g., "relu", "tanh", or "sigmoid". 624 the op may also be provided, e.g., "relu", "tanh", or "sigmoid". [all...] |