| /external/tensorflow/tensorflow/python/kernel_tests/ |
| D | softplus_op_test.py | 42 softplus = nn_ops.softplus(np_features)
43 tf_softplus = self.evaluate(softplus)
46 self.assertShapeEqual(np_softplus, softplus)
81 y = nn_ops.softplus(x, name="softplus")
98 y = nn_ops.softplus(x, name="softplus")
116 y = nn_ops.softplus(x, name="softplus")
134 nn_ops.softplus(constant_op.constant(42)).eval()
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| /external/tensorflow/tensorflow/core/api_def/base_api/ |
| D | api_def_SoftplusGrad.pbtxt | 7 The backpropagated gradients to the corresponding softplus operation.
13 The features passed as input to the corresponding softplus operation.
22 summary: "Computes softplus gradients for a softplus operation."
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| D | api_def_Softplus.pbtxt | 3 summary: "Computes softplus: `log(exp(features) + 1)`."
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| /external/tensorflow/tensorflow/python/ops/distributions/ |
| D | bernoulli.py | 156 nn.softplus(-self.logits))
184 delta_probs0 = nn.softplus(-b.logits) - nn.softplus(-a.logits)
185 delta_probs1 = nn.softplus(b.logits) - nn.softplus(a.logits)
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| D | gamma.py | 305 concentration=nn.softplus(concentration,
307 rate=nn.softplus(rate, name="softplus_rate"),
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| D | beta.py | 368 concentration1=nn.softplus(concentration1,
370 concentration0=nn.softplus(concentration0,
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| D | exponential.py | 162 rate=nn.softplus(rate, name="softplus_rate"),
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| /external/tensorflow/tensorflow/contrib/distributions/python/kernel_tests/ |
| D | estimator_test.py | 51 def softplus(x): function
66 return softplus(logits[..., 1] + scale_bias)
71 scale=nn_ops.softplus(logits[..., 1] + scale_bias))
104 expected_stddev = softplus(logits[..., 1] + scale_bias)
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| /external/tensorflow/tensorflow/core/api_def/python_api/ |
| D | api_def_Softplus.pbtxt | 4 name: "math.softplus"
7 name: "nn.softplus"
|
| /external/tensorflow/tensorflow/contrib/distributions/python/ops/bijectors/ |
| D | softplus.py | 118 return nn_ops.softplus(x)
120 return hinge_softness * nn_ops.softplus(x / hinge_softness)
146 return -nn_ops.softplus(-x)
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| D | sigmoid.py | 59 return -nn_ops.softplus(-x) - nn_ops.softplus(x)
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| D | scale_tril.py | 24 from tensorflow.contrib.distributions.python.ops.bijectors import softplus
116 diag_bijector = softplus.Softplus(validate_args=validate_args)
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| D | softmax_centered.py | 170 log_normalization = nn_ops.softplus(
|
| D | __init__.py | 88 from tensorflow.contrib.distributions.python.ops.bijectors.softplus import *
|
| /external/tensorflow/tensorflow/contrib/labeled_tensor/python/ops/ |
| D | nn.py | 29 softplus = core.define_unary_op('softplus', nn.softplus) variable
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| D | nn_test.py | 41 ('softplus', nn_ops.softplus, nn.softplus),
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| /external/tensorflow/tensorflow/contrib/distributions/python/ops/ |
| D | logistic.py | 199 return -nn_ops.softplus(-self._z(x))
205 return -nn_ops.softplus(self._z(x))
212 return - z - 2. * nn_ops.softplus(-z)
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| D | inverse_gamma.py | 305 concentration=nn.softplus(concentration,
307 rate=nn.softplus(rate, name="softplus_rate"),
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| D | mvn_diag.py | 249 scale_diag=nn.softplus(scale_diag),
|
| /external/tensorflow/tensorflow/python/keras/ |
| D | activations.py | 118 def softplus(x): function
127 return nn.softplus(x)
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| D | activations_test.py | 95 def softplus(x): function
99 f = keras.backend.function([x], [keras.activations.softplus(x)])
102 expected = softplus(test_values)
|
| /external/tensorflow/tensorflow/contrib/keras/api/keras/activations/ |
| D | __init__.py | 29 from tensorflow.python.keras.activations import softplus
|
| /external/tensorflow/tensorflow/tools/api/golden/v2/ |
| D | tensorflow.keras.activations.pbtxt | 48 name: "softplus"
|
| /external/tensorflow/tensorflow/tools/api/golden/v1/ |
| D | tensorflow.keras.activations.pbtxt | 48 name: "softplus"
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| /external/tensorflow/tensorflow/contrib/nn/python/ops/ |
| D | scaled_softplus.py | 66 y = alpha * nn.softplus(x / alpha)
|