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  /external/tensorflow/tensorflow/core/kernels/
softplus_op_gpu.cu.cc 33 template struct functor::Softplus<GPUDevice, T>; \
softplus_op.cc 43 functor::Softplus<Device, T> functor;
86 Name("Softplus").Device(DEVICE_CPU).TypeConstraint<type>("T"), \
100 void Softplus<GPUDevice, T>::operator()( \
103 extern template struct Softplus<GPUDevice, T>; \
118 Name("Softplus").Device(DEVICE_GPU).TypeConstraint<type>("T"), \
softplus_op.h 29 struct Softplus {
30 // Computes Softplus activation.
38 // true softplus(x). Offset of 2 from machine epsilon checked
40 // softplus implemented with numpy's log1p and numpy's logaddexp.
43 // Value above which exp(x) may overflow, but softplus(x) == x
46 // Value below which exp(x) may underflow, but softplus(x) == exp(x)
51 features, // softplus(x) ~= x for x large
52 too_small.select(features_exp, // softplus(x) ~= exp(x) for x small
62 // gradients: gradients backpropagated to the Softplus op.
63 // features: inputs that where passed to the Softplus op
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  /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/bijectors/
softplus_test.py 23 from tensorflow.contrib.distributions.python.ops.bijectors.softplus import Softplus
46 bijector = Softplus(event_ndims=0, hinge_softness=0., validate_args=True)
52 bijector = Softplus(event_ndims=0)
53 self.assertEqual("softplus", bijector.name)
62 bijector = Softplus(event_ndims=0, hinge_softness=1.5)
71 bijector = Softplus(event_ndims=0)
80 bijector = Softplus(event_ndims=1)
81 self.assertEqual("softplus", bijector.name)
90 bijector = Softplus(event_ndims=1
    [all...]
chain_test.py 26 from tensorflow.contrib.distributions.python.ops.bijectors.softplus import Softplus
37 chain = Chain((Exp(event_ndims=1), Softplus(event_ndims=1)))
62 bijector = Chain((Exp(), Softplus()))
cholesky_outer_product_test.py 40 bijectors.Softplus(event_ndims=1),
invert_test.py 40 bijectors.Softplus(event_ndims=1),
  /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/
softplus.py 15 """Softplus bijector."""
31 "Softplus",
35 class Softplus(bijector.Bijector):
38 The softplus `Bijector` has the following two useful properties:
41 * `softplus(x) approx x`, for large `x`, so it does not overflow as easily as
50 so the behavior for large `x` is the same as the standard softplus.
63 # Create the Y=g(X)=softplus(X) transform which works only on Tensors with 1
65 softplus = Softplus(event_ndims=2)
70 log(1 + exp(x)) == softplus.forward(x
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  /external/tensorflow/tensorflow/compiler/tf2xla/kernels/
unary_ops.cc 163 static xla::ComputationDataHandle Softplus(
169 // Value above which exp(x) may overflow, but softplus(x) == x
172 // Value below which exp(x) may underflow, but softplus(x) == exp(x)
182 XLAJIT_MAKE_UNARY(Softplus, Softplus(b, input_type(0), x));
  /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/
vector_laplace_diag_test.py 64 bijector=bijectors.Softplus(event_ndims=1))
mvn_diag_test.py 71 bijector=bijectors.Softplus(event_ndims=1))
236 cov_mat = array_ops.matrix_diag(nn_ops.softplus(diag)).eval()**2
  /external/tensorflow/tensorflow/compiler/tests/
randomized_tests.cc     [all...]
  /external/tensorflow/tensorflow/go/op/
wrappers.go     [all...]

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