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未验证 提交 40d193ed 编写于 作者: H hong19860320 提交者： GitHub
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Add the ReLU6, Tanhshrink, SELU, Softplus, Softshrink and Softsign for the api 2.0 (#26376)

上级 6e13e86a
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Showing with 993 addition and 116 deletion
paddle/fluid/operators/activation_op.cc
浏览文件 @ 40d193ed
......@@ -317,13 +317,6 @@ $$out = x^2$$
)DOC";
UNUSED constexpr char SoftplusDoc[] = R"DOC(
Softplus Activation Operator.
$$out = \ln(1 + e^{x})$$
)DOC";
UNUSED constexpr char SoftsignDoc[] = R"DOC(
Softsign Activation Operator.
......@@ -396,6 +389,36 @@ $$out = \max(x, \alpha * x)$$
}
};
class SoftplusOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("X",
"Input of Softplus operator, an N-D Tensor, with data type "
"float32, float64 or float16.");
AddOutput(
"Out",
"Output of Softplus operator, a Tensor with shape same as input.");
AddAttr<float>("beta", "The value of beta for Softplus.").SetDefault(1.0f);
AddAttr<float>("threshold", "The value of threshold for Softplus.")
.SetDefault(20.0f);
AddAttr<bool>("use_mkldnn",
"(bool, default false) Only used in mkldnn kernel.")
.SetDefault(false);
AddAttr<bool>(
"use_cudnn",
"(bool, default false) Only used in cudnn kernel, need install cudnn.")
.SetDefault(false);
AddComment(R"DOC(
:strong:`Softplus Activation Operator`
.. math::
out = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) \\
\text{For numerical stability, the implementation reverts to the linear function when :}\,x \times \beta > threshold.
)DOC");
}
};
class SoftShrinkOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
......@@ -672,7 +695,6 @@ REGISTER_ACTIVATION_OP_MAKER(Reciprocal, ReciprocalDoc);
REGISTER_ACTIVATION_OP_MAKER(Log, LogDoc);
REGISTER_ACTIVATION_OP_MAKER(Log1p, Log1pDoc);
REGISTER_ACTIVATION_OP_MAKER(Square, SquareDoc);
REGISTER_ACTIVATION_OP_MAKER(Softplus, SoftplusDoc);
REGISTER_ACTIVATION_OP_MAKER(Softsign, SoftsignDoc);
template <ActBwdOpFwdDeps kDepValue>
......
paddle/fluid/operators/activation_op.h
浏览文件 @ 40d193ed
......@@ -975,32 +975,46 @@ struct HardSwishGradFunctor : public BaseActivationFunctor<T> {
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
};
// softplus(x) = log(1 + exp(x))
// When x is a very large positive number, exp(x) may explode to inf,
// Using trick below for numerical stability
// https://hips.seas.harvard.edu/blog/2013/01/09/computing-log-sum-exp/
// Then: softplus(x) = max(x, 0) + log(exp(-max(x, 0)) + exp(x - max(x, 0)))
// For numerical stability, using the following formula instead of softplus(x) =
// log(1 + exp(x))
// softplus(x) = log(1 + exp(beta * x)) / beta when beta * x <= threshold(beta =
// 1, threshold = 20 by default), otherwise x
template <typename T>
struct SoftplusFunctor : public BaseActivationFunctor<T> {
float beta;
float threshold;
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"beta", &beta}, {"threshold", &threshold}};
}
template <typename Device, typename X, typename Out>
void operator()(Device d, X x, Out out) {
auto temp = x.cwiseMax(static_cast<T>(0)); // temp = max(x, 0)
out.device(d) = temp + (((-temp).exp() + (x - temp).exp()).log());
auto x_beta = static_cast<T>(beta) * x;
out.device(d) = (x_beta > static_cast<T>(threshold))
.select(x, (static_cast<T>(1) + x_beta.exp()).log() /
static_cast<T>(beta));
}
};
// d(softplus(x))/dx = exp(x) / (1 + exp(x))
// For numerical stability:
// d(softplus(x))/dx = exp(x - max(x, 0)) / (exp(-max(x, 0)) +
// exp(x - max(x, 0)))
// For numerical stability, using the following formula instead of
// d(softplus(x))/dx = 1 / (1 + exp(-x))
// d(softplus(x))/dx = 1 / (1 + exp(-beta * x)) when beta * x <= threshold(beta
// = 1, threshold = 20 by default), otherwise x
template <typename T>
struct SoftplusGradFunctor : public BaseActivationFunctor<T> {
float beta;
float threshold;
typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
return {{"beta", &beta}, {"threshold", &threshold}};
}
template <typename Device, typename X, typename Out, typename dOut,
typename dX>
void operator()(Device d, X x, Out out, dOut dout, dX dx) {
auto temp = x.cwiseMax(static_cast<T>(0)); // temp = max(x, 0)
auto x_beta = static_cast<T>(beta) * x;
dx.device(d) =
dout * ((x - temp).exp() / ((-temp).exp() + (x - temp).exp()));
(x_beta > static_cast<T>(threshold))
.select(dout, dout / (static_cast<T>(1) + (-x_beta).exp()));
}
static constexpr ActBwdOpFwdDeps FwdDeps() { return kDepX; }
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 40d193ed
......@@ -8643,11 +8643,9 @@ def relu(x, name=None):
return out
@deprecated(since="2.0.0", update_to="paddle.nn.functional.selu")
def selu(x, scale=None, alpha=None, name=None):
"""
:alias_main: paddle.nn.functional.selu
:alias: paddle.nn.functional.selu,paddle.nn.functional.activation.selu
:old_api: paddle.fluid.layers.selu
Selu Operator.
......@@ -9304,12 +9302,9 @@ def elu(x, alpha=1.0, name=None):
return out
@templatedoc()
@deprecated(since="2.0.0", update_to="paddle.nn.functional.relu6")
def relu6(x, threshold=6.0, name=None):
"""
:alias_main: paddle.nn.functional.relu6
:alias: paddle.nn.functional.relu6,paddle.nn.functional.activation.relu6
:old_api: paddle.fluid.layers.relu6
${comment}
......
python/paddle/fluid/layers/ops.py
浏览文件 @ 40d193ed
......@@ -20,6 +20,8 @@ from ..framework import convert_np_dtype_to_dtype_, Variable
from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
from paddle.utils import deprecated
__deprecated_func_name__ = {'tanh_shrink': 'tanhshrink', }
__activations_noattr__ = [
'sigmoid',
'logsigmoid',
......@@ -64,14 +66,20 @@ __all__ += __activations_noattr__
__all__ += __unary_func__
for _OP in set(__activations_noattr__):
_new_OP = _OP
if _OP in __deprecated_func_name__:
_new_OP = __deprecated_func_name__[_OP]
func = generate_activation_fn(_OP)
func = deprecated(
since="2.0.0", update_to="paddle.nn.functional.%s" % (_OP))(func)
since="2.0.0", update_to="paddle.nn.functional.%s" % (_new_OP))(func)
globals()[_OP] = func
for _OP in set(__unary_func__):
_new_OP = _OP
if _OP in __deprecated_func_name__:
_new_OP = __deprecated_func_name__[_OP]
func = generate_activation_fn(_OP)
func = deprecated(since="2.0.0", update_to="paddle.%s" % (_OP))(func)
func = deprecated(since="2.0.0", update_to="paddle.%s" % (_new_OP))(func)
globals()[_OP] = func
add_sample_code(globals()["sigmoid"], r"""
......@@ -160,16 +168,14 @@ add_sample_code(globals()["tanh_shrink"], r"""
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x_data = np.array([-0.4, -0.2, 0.1, 0.3])
x = paddle.to_variable(x_data)
out = F.tanh_shrink(x)
print(out.numpy())
# [-0.02005104 -0.00262468 0.00033201 0.00868739]
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
out = F.tanhshrink(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
""")
......@@ -401,16 +407,14 @@ add_sample_code(globals()["softplus"], r"""
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x_data = np.array([-0.4, -0.2, 0.1, 0.3])
x = paddle.to_variable(x_data)
out = F.softplus(x)
print(out.numpy())
# [0.51301525 0.59813887 0.74439666 0.85435524]
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
out = F.softplus(x) # [0.513015, 0.598139, 0.744397, 0.854355]
""")
......@@ -418,16 +422,14 @@ add_sample_code(globals()["softsign"], r"""
Examples:
.. code-block:: python
import numpy as np
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x_data = np.array([-0.4, -0.2, 0.1, 0.3])
x = paddle.to_variable(x_data)
out = F.softsign(x)
print(out.numpy())
# [-0.28571429 -0.16666667 0.09090909 0.23076923]
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
out = F.softsign(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
""")
......
python/paddle/fluid/tests/unittests/test_activation_op.py
浏览文件 @ 40d193ed
......@@ -369,15 +369,20 @@ class TestCoshOpError(unittest.TestCase):
fluid.layers.cosh(x_fp16)
class TestTanhShrink(TestActivation):
def ref_tanhshrink(x):
out = x - np.tanh(x)
return out
class TestTanhshrink(TestActivation):
def setUp(self):
self.op_type = "tanh_shrink"
self.init_dtype()
x = np.random.uniform(0.1, 1, [10, 17]).astype(self.dtype)
out = x - np.tanh(x)
x = np.random.uniform(10, 20, [10, 17]).astype(self.dtype)
out = ref_tanhshrink(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.inputs = {'X': x}
self.outputs = {'Out': out}
def test_check_grad(self):
......@@ -386,6 +391,57 @@ class TestTanhShrink(TestActivation):
self.check_grad(['X'], 'Out')
class TestTanhshrinkAPI(unittest.TestCase):
# test paddle.nn.Tanhshrink, paddle.nn.functional.tanhshrink
def setUp(self):
self.x_np = np.random.uniform(10, 20, [10, 17]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.tanhshrink(x)
tanhshrink = paddle.nn.Tanhshrink()
out2 = tanhshrink(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_tanhshrink(self.x_np)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.tanhshrink(x)
tanhshrink = paddle.nn.Tanhshrink()
out2 = tanhshrink(x)
out_ref = ref_tanhshrink(self.x_np)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_fluid_api(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.tanh_shrink(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_tanhshrink(self.x_np)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.tanhshrink, 1)
# The input dtype must be float16, float32, float64.
x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.tanhshrink, x_int32)
# support the input dtype is float16
x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
F.tanhshrink(x_fp16)
def ref_hardshrink(x, threshold):
out = np.copy(x)
out[(out >= -threshold) & (out <= threshold)] = 0
......@@ -469,19 +525,24 @@ class TestHardShrinkAPI(unittest.TestCase):
F.hardshrink(x_fp16)
class TestSoftShrink(TestActivation):
def ref_softshrink(x, threshold=0.5):
out = np.copy(x)
out = (out < -threshold) * (out + threshold) + (out > threshold) * (
out - threshold)
return out
class TestSoftshrink(TestActivation):
def setUp(self):
self.op_type = "softshrink"
self.init_dtype()
lambda_val = 0.1
x = np.random.uniform(0.25, 10, [10, 12]).astype(self.dtype)
out = np.copy(x)
out = (out < -lambda_val) * (out + lambda_val) + (out > lambda_val) * (
out - lambda_val)
threshold = 0.8
self.attrs = {'lambda': lambda_val}
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
x = np.random.uniform(0.25, 10, [10, 12]).astype(self.dtype)
out = ref_softshrink(x, threshold)
self.inputs = {'X': x}
self.attrs = {"lambda": threshold}
self.outputs = {'Out': out}
def test_check_grad(self):
......@@ -490,17 +551,56 @@ class TestSoftShrink(TestActivation):
self.check_grad(['X'], 'Out')
class TestSoftShrinkOpError(unittest.TestCase):
class TestSoftshrinkAPI(unittest.TestCase):
# test paddle.nn.Softshrink, paddle.nn.functional.softshrink
def setUp(self):
self.threshold = 0.8
self.x_np = np.random.uniform(0.25, 10, [10, 12]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.softshrink(x, self.threshold)
softshrink = paddle.nn.Softshrink(self.threshold)
out2 = softshrink(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_softshrink(self.x_np, self.threshold)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.softshrink(x, self.threshold)
softshrink = paddle.nn.Softshrink(self.threshold)
out2 = softshrink(x)
out_ref = ref_softshrink(self.x_np, self.threshold)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_fluid_api(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softshrink(x, self.threshold)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_softshrink(self.x_np, self.threshold)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
with program_guard(Program()):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.softshrink, 1)
self.assertRaises(TypeError, F.softshrink, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.softshrink, x_int32)
x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.softshrink, x_int32)
# support the input dtype is float16
x_fp16 = fluid.data(name='x_fp16', shape=[12, 10], dtype='float16')
fluid.layers.softshrink(x_fp16)
x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
F.softshrink(x_fp16)
class TestSqrt(TestActivation, TestParameter):
......@@ -903,20 +1003,24 @@ class TestBReluOpError(unittest.TestCase):
fluid.layers.brelu(x_fp16)
def ref_relu6(x, threshold=6.0):
out = np.copy(x)
out[np.abs(x - threshold) < 0.005] = threshold + 0.02
out = np.minimum(np.maximum(x, 0), threshold)
return out
class TestRelu6(TestActivation):
def setUp(self):
self.op_type = "relu6"
self.init_dtype()
x = np.random.uniform(-1, 10, [10, 12]).astype(self.dtype)
threshold = 6.0
# The same with TestAbs
x[np.abs(x) < 0.005] = 0.02
x[np.abs(x - threshold) < 0.005] = threshold + 0.02
out = np.minimum(np.maximum(x, 0), threshold)
out = ref_relu6(x)
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
self.attrs = {'threshold': threshold}
self.inputs = {'X': x}
self.attrs = {'threshold': 6.0}
self.outputs = {'Out': out}
def test_check_grad(self):
......@@ -925,17 +1029,56 @@ class TestRelu6(TestActivation):
self.check_grad(['X'], 'Out')
class TestRelu6OpError(unittest.TestCase):
class TestRelu6API(unittest.TestCase):
# test paddle.nn.ReLU6, paddle.nn.functional.relu6
def setUp(self):
self.x_np = np.random.uniform(-1, 10, [10, 12]).astype(np.float64)
self.x_np[np.abs(self.x_np) < 0.005] = 0.02
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.relu6(x)
relu6 = paddle.nn.ReLU6()
out2 = relu6(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_relu6(self.x_np)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.relu6(x)
relu6 = paddle.nn.ReLU6()
out2 = relu6(x)
out_ref = ref_relu6(self.x_np)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_fluid_api(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.relu6(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_relu6(self.x_np)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
with program_guard(Program()):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.relu6, 1)
self.assertRaises(TypeError, F.relu6, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.relu6, x_int32)
x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.relu6, x_int32)
# support the input dtype is float16
x_fp16 = fluid.data(name='x_fp16', shape=[12, 10], dtype='float16')
fluid.layers.relu6(x_fp16)
x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
F.relu6(x_fp16)
class TestHardSwish(TestActivation):
......@@ -1318,16 +1461,25 @@ class TestSTanhOpError(unittest.TestCase):
fluid.layers.stanh(x_fp16)
def ref_softplus(x, beta=1, threshold=20):
x_beta = beta * x
out = np.select([x_beta <= threshold, x_beta > threshold],
[np.log(1 + np.exp(x_beta)) / beta, x])
return out
class TestSoftplus(TestActivation):
def setUp(self):
self.op_type = "softplus"
self.init_dtype()
self.dtype = np.float64
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = np.log(1 + np.exp(x))
beta = 2
threshold = 15
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = ref_softplus(x, beta, threshold)
self.inputs = {'X': x}
self.attrs = {'beta': beta, "threshold": threshold}
self.outputs = {'Out': out}
def test_check_grad(self):
......@@ -1336,15 +1488,72 @@ class TestSoftplus(TestActivation):
self.check_grad(['X'], 'Out')
class TestSoftplusAPI(unittest.TestCase):
# test paddle.nn.Softplus, paddle.nn.functional.softplus
def setUp(self):
self.beta = 2
self.threshold = 15
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.softplus(x, self.beta, self.threshold)
softplus = paddle.nn.Softplus(self.beta, self.threshold)
out2 = softplus(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_softplus(self.x_np, self.beta, self.threshold)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.softplus(x, self.beta, self.threshold)
softplus = paddle.nn.Softplus(self.beta, self.threshold)
out2 = softplus(x)
out_ref = ref_softplus(self.x_np, self.beta, self.threshold)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_fluid_api(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softplus(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_softplus(self.x_np)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.softplus, 1)
# The input dtype must be float16, float32, float64.
x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.softplus, x_int32)
# support the input dtype is float16
x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
F.softplus(x_fp16)
def ref_softsign(x):
out = np.divide(x, 1 + np.abs(x))
return out
class TestSoftsign(TestActivation):
def setUp(self):
self.op_type = "softsign"
self.init_dtype()
x = np.random.uniform(-1, 1, [11, 17]).astype(self.dtype)
out = np.divide(x, 1 + np.abs(x))
self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
x = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
out = ref_softsign(x)
self.inputs = {'X': x}
self.outputs = {'Out': out}
def test_check_grad(self):
......@@ -1353,6 +1562,57 @@ class TestSoftsign(TestActivation):
self.check_grad(['X'], 'Out')
class TestSoftsignAPI(unittest.TestCase):
# test paddle.nn.Softsign, paddle.nn.functional.softsign
def setUp(self):
self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(np.float64)
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.softsign(x)
softsign = paddle.nn.Softsign()
out2 = softsign(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_softsign(self.x_np)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.softsign(x)
softsign = paddle.nn.Softsign()
out2 = softsign(x)
out_ref = ref_softsign(self.x_np)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_fluid_api(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.softsign(x)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_softsign(self.x_np)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, F.softsign, 1)
# The input dtype must be float16, float32, float64.
x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.softsign, x_int32)
# support the input dtype is float16
x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
F.softsign(x_fp16)
class TestThresholdedRelu(TestActivation):
def setUp(self):
self.op_type = "thresholded_relu"
......@@ -1548,9 +1808,9 @@ create_test_act_fp16_class(TestActivation)
create_test_act_fp16_class(TestSigmoid)
create_test_act_fp16_class(TestLogSigmoid)
create_test_act_fp16_class(TestTanh)
create_test_act_fp16_class(TestTanhShrink)
create_test_act_fp16_class(TestTanhshrink)
create_test_act_fp16_class(TestHardShrink)
create_test_act_fp16_class(TestSoftShrink)
create_test_act_fp16_class(TestSoftshrink)
create_test_act_fp16_class(TestSqrt)
create_test_act_fp16_class(TestAbs)
create_test_act_fp16_class(TestCeil, grad_check=False)
......
python/paddle/fluid/tests/unittests/test_selu_op.py
浏览文件 @ 40d193ed
......@@ -17,9 +17,26 @@ from __future__ import print_function
import unittest
import numpy as np
import six
import paddle.fluid.core as core
from op_test import OpTest
import paddle
import paddle.fluid as fluid
from paddle.fluid import Program, program_guard
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.fluid import compiler, Program, program_guard
def ref_selu(x,
scale=1.0507009873554804934193349852946,
alpha=1.6732632423543772848170429916717):
out = np.copy(x)
out_flat = out.flatten()
for i in range(out_flat.size):
if out_flat[i] < 0:
out_flat[i] = alpha * np.exp(out_flat[i]) - alpha
out_flat[i] = scale * out_flat[i]
out = out_flat.reshape(x.shape)
return out
class SeluTest(OpTest):
......@@ -39,17 +56,10 @@ class SeluTest(OpTest):
# zero.
x[np.abs(x) < 0.005] = 0.02
x_flat = x.flatten()
for i in range(x_flat.size):
if x_flat[i] < 0:
x_flat[i] = alpha * np.exp(x_flat[i]) - alpha
x_flat[i] = scale * x_flat[i]
out_np = x_flat.reshape(self.x_shape)
out = ref_selu(x, scale, alpha)
self.inputs = {'X': x}
self.outputs = {'Out': out_np}
self.outputs = {'Out': out}
self.attrs = {
'alpha': alpha,
......@@ -69,17 +79,60 @@ class SeluTest(OpTest):
self.check_grad(['X'], 'Out')
class TestSeluOpError(unittest.TestCase):
class TestSeluAPI(unittest.TestCase):
# test paddle.nn.SELU, paddle.nn.functional.selu
def setUp(self):
self.scale = 1.5
self.alpha = 2.0
self.x_np = np.random.normal(size=[3, 5, 5, 10]).astype(np.float64)
# Since zero point in selu is not differentiable, avoid randomize
# zero.
self.x_np[np.abs(self.x_np) < 0.005] = 0.02
self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
else paddle.CPUPlace()
def test_static_api(self):
with paddle.static.program_guard(paddle.static.Program()):
x = paddle.data('X', self.x_np.shape, self.x_np.dtype)
out1 = F.selu(x, self.scale, self.alpha)
selu = paddle.nn.SELU(self.scale, self.alpha)
out2 = selu(x)
exe = paddle.static.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out1, out2])
out_ref = ref_selu(self.x_np, self.scale, self.alpha)
for r in res:
self.assertEqual(np.allclose(out_ref, r), True)
def test_dygraph_api(self):
paddle.disable_static(self.place)
x = paddle.to_tensor(self.x_np)
out1 = F.selu(x, self.scale, self.alpha)
selu = paddle.nn.SELU(self.scale, self.alpha)
out2 = selu(x)
out_ref = ref_selu(self.x_np, self.scale, self.alpha)
for r in [out1, out2]:
self.assertEqual(np.allclose(out_ref, r.numpy()), True)
paddle.enable_static()
def test_fluid_api(self):
with fluid.program_guard(fluid.Program()):
x = fluid.data('X', self.x_np.shape, self.x_np.dtype)
out = fluid.layers.selu(x, self.scale, self.alpha)
exe = fluid.Executor(self.place)
res = exe.run(feed={'X': self.x_np}, fetch_list=[out])
out_ref = ref_selu(self.x_np, self.scale, self.alpha)
self.assertEqual(np.allclose(out_ref, res[0]), True)
def test_errors(self):
with program_guard(Program()):
with paddle.static.program_guard(paddle.static.Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.selu, 1)
self.assertRaises(TypeError, F.selu, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.selu, x_int32)
# support the input dtype is float32
x_fp32 = fluid.data(name='x_fp32', shape=[12, 10], dtype='float32')
fluid.layers.selu(x_fp32)
x_int32 = paddle.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, F.selu, x_int32)
# support the input dtype is float16
x_fp16 = paddle.data(name='x_fp16', shape=[12, 10], dtype='float16')
F.selu(x_fp16)
if __name__ == "__main__":
......
python/paddle/nn/__init__.py
浏览文件 @ 40d193ed
......@@ -57,10 +57,16 @@ from .layer.activation import GELU
from .layer.activation import Hardshrink
# from .layer.activation import PReLU #DEFINE_ALIAS
from .layer.activation import ReLU
from .layer.activation import ReLU6 #DEFINE_ALIAS
from .layer.activation import SELU #DEFINE_ALIAS
from .layer.activation import LeakyReLU #DEFINE_ALIAS
from .layer.activation import Sigmoid #DEFINE_ALIAS
from .layer.activation import LogSigmoid
# from .layer.activation import Softmax #DEFINE_ALIAS
from .layer.activation import Softplus #DEFINE_ALIAS
from .layer.activation import Softshrink #DEFINE_ALIAS
from .layer.activation import Softsign #DEFINE_ALIAS
from .layer.activation import Tanhshrink #DEFINE_ALIAS
from .layer.activation import LogSoftmax #DEFINE_ALIAS
from .layer.activation import HSigmoid #DEFINE_ALIAS
from .layer.common import BilinearTensorProduct #DEFINE_ALIAS
......
python/paddle/nn/functional/__init__.py
浏览文件 @ 40d193ed
......@@ -47,7 +47,7 @@ from .activation import softplus #DEFINE_ALIAS
from .activation import softshrink #DEFINE_ALIAS
from .activation import softsign #DEFINE_ALIAS
from .activation import swish #DEFINE_ALIAS
from .activation import tanh_shrink #DEFINE_ALIAS
from .activation import tanhshrink #DEFINE_ALIAS
from .activation import thresholded_relu #DEFINE_ALIAS
from .activation import log_softmax #DEFINE_ALIAS
from .common import dropout #DEFINE_ALIAS
......
python/paddle/nn/functional/activation.py
浏览文件 @ 40d193ed
......@@ -19,15 +19,9 @@ from ...fluid.layers import hard_sigmoid #DEFINE_ALIAS
from ...fluid.layers import hard_swish #DEFINE_ALIAS
from ...fluid.layers import leaky_relu #DEFINE_ALIAS
from ...fluid.layers import maxout #DEFINE_ALIAS
from ...fluid.layers import relu6 #DEFINE_ALIAS
from ...fluid.layers import selu #DEFINE_ALIAS
from ...fluid.layers import soft_relu #DEFINE_ALIAS
from ...fluid.layers import softplus #DEFINE_ALIAS
from ...fluid.layers import softshrink #DEFINE_ALIAS
from ...fluid.layers import softsign #DEFINE_ALIAS
from ...fluid.layers import swish #DEFINE_ALIAS
from ...fluid.layers import sigmoid #DEFINE_ALIAS
from ...fluid.layers import tanh_shrink #DEFINE_ALIAS
from ...fluid.layers import thresholded_relu #DEFINE_ALIAS
__all__ = [
......@@ -53,7 +47,7 @@ __all__ = [
'softsign',
'sigmoid',
'swish',
'tanh_shrink',
'tanhshrink',
'thresholded_relu',
'log_softmax'
]
......@@ -423,6 +417,103 @@ def logsigmoid(x, name=None):
return out
def relu6(x, name=None):
"""
relu6 activation
.. math::
\text{relu6}(x) = \min(\max(0,x), 6)
Args:
x (Tensor): The input Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
out = F.relu6(x) # [0, 0.3, 6]
"""
threshold = 6.0
if in_dygraph_mode():
return core.ops.relu6(x, 'threshold', threshold)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'relu6')
helper = LayerHelper('relu6', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='relu6',
inputs={'X': x},
outputs={'Out': out},
attrs={'threshold': threshold})
return out
def selu(x,
scale=1.0507009873554804934193349852946,
alpha=1.6732632423543772848170429916717,
name=None):
"""
selu activation
.. math::
\text{selu}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))), \\
with\,alpha=1.6732632423543772848170429916717 and \\
scale=1.0507009873554804934193349852946
Args:
x (Tensor): The input Tensor with data type float32, float64.
scale (float, optional): The value of scale for selu. Default is 1.0507009873554804934193349852946
alpha (float, optional): The value of alpha for selu. Default is 1.6732632423543772848170429916717
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([[0, 1],[2, 3]]))
out = F.selu(x) # [[0, 1.050701],[2.101402, 3.152103]]
"""
if in_dygraph_mode():
return core.ops.selu(x, 'scale', scale, 'alpha', alpha)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'], 'selu')
helper = LayerHelper('selu', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='selu',
inputs={'X': x},
outputs={'Out': out},
attrs={'scale': scale,
'alpha': alpha})
return out
def softmax(x, axis=-1, name=None):
"""
This operator implements the softmax layer. The calculation process is as follows:
......@@ -539,6 +630,188 @@ def softmax(x, axis=-1, name=None):
return paddle.fluid.layers.softmax(input=x, axis=axis, name=name)
def softplus(x, beta=1, threshold=20, name=None):
"""
softplus activation
.. math::
\text{softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) \\
\text{For numerical stability, the implementation reverts to the linear function when :}\,x \times \beta > threshold.
Args:
x (Tensor): The input Tensor with data type float32, float64.
beta (float, optional): The value of beta for softplus. Default is 1
threshold (float, optional): The value of threshold for softplus. Default is 20
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
out = F.softplus(x) # [0.513015, 0.598139, 0.744397, 0.854355]
"""
if in_dygraph_mode():
return core.ops.softplus(x, 'beta', beta, 'threshold', threshold)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'softplus')
helper = LayerHelper('softplus', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='softplus',
inputs={'X': x},
outputs={'Out': out},
attrs={'beta': beta,
'threshold': threshold})
return out
def softshrink(x, threshold=0.5, name=None):
"""
softshrink activation
.. math::
\text{softshrink}(x) =
\begin{cases}
x - threshold, & \text{ if } x > threshold \\
x + threshold, & \text{ if } x < -threshold \\
0, & \text{ otherwise }
\end{cases}
Args:
x (Tensor): The input Tensor with data type float32, float64.
threshold (float, optional): The value of threshold(must be no less than zero) for softplus. Default is 0.5
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
out = F.softshrink(x) # [-0.4, 0, 0, 0.3]
"""
if in_dygraph_mode():
return core.ops.softshrink(x, 'lambda', threshold)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'softshrink')
helper = LayerHelper('softshrink', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(
type='softshrink',
inputs={'X': x},
outputs={'Out': out},
attrs={'lambda': threshold})
return out
def softsign(x, name=None):
"""
softsign activation
.. math::
\text{softsign}(x) = \frac{x}{1 + |x|}
Args:
x (Tensor): The input Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
out = F.softsign(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
"""
if in_dygraph_mode():
return core.ops.softsign(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'softsign')
helper = LayerHelper('softsign', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(type='softsign', inputs={'X': x}, outputs={'Out': out})
return out
def tanhshrink(x, name=None):
"""
tanhshrink activation
.. math::
\text{tanhshrink}(x) = x - \text{tanh}(x)
Args:
x (Tensor): The input Tensor with data type float32, float64.
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Returns:
A Tensor with the same data type and shape as ``x`` .
Examples:
.. code-block:: python
import paddle
import paddle.nn.functional as F
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
out = F.tanhshrink(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
"""
if in_dygraph_mode():
return core.ops.tanh_shrink(x)
check_variable_and_dtype(x, 'x', ['float16', 'float32', 'float64'],
'tanhshrink')
helper = LayerHelper('tanh_shrink', **locals())
out = helper.create_variable_for_type_inference(x.dtype)
helper.append_op(type='tanh_shrink', inputs={'X': x}, outputs={'Out': out})
return out
def log_softmax(x, axis=-1, dtype=None, name=None):
"""
This operator implements the log_softmax layer. The calculation process is
......
python/paddle/nn/layer/activation.py
浏览文件 @ 40d193ed
......@@ -20,9 +20,15 @@ __all__ = [
'Hardshrink',
# 'PReLU',
'ReLU',
'ReLU6',
'SELU',
'LeakyReLU',
'Sigmoid',
# 'Softmax',
'Softplus',
'Softshrink',
'Softsign',
'Tanhshrink',
'LogSigmoid',
'LogSoftmax',
'HSigmoid'
......@@ -351,6 +357,91 @@ class ReLU(layers.Layer):
return F.relu(x, self._name)
class ReLU6(layers.Layer):
"""
ReLU6 Activation
.. math::
\text{ReLU6}(x) = \min(\max(0,x), 6)
Parameters:
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-1, 0.3, 6.5]))
m = paddle.nn.ReLU6()
out = m(x) # [0, 0.3, 6]
"""
def __init__(self, name=None):
super(ReLU6, self).__init__()
self._name = name
def forward(self, x):
return F.relu6(x, self._name)
class SELU(layers.Layer):
"""
SELU Activation
.. math::
\text{SELU}(x) = scale * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1))), \\
with\,alpha=1.6732632423543772848170429916717 and \\
scale=1.0507009873554804934193349852946
Parameters:
scale (float, optional): The value of scale for SELU. Default is 1.0507009873554804934193349852946
alpha (float, optional): The value of alpha for SELU. Default is 1.6732632423543772848170429916717
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([[0, 1],[2, 3]]))
m = paddle.nn.SELU()
out = m(x) # [[0, 1.050701],[2.101402, 3.152103]]
"""
def __init__(self,
scale=1.0507009873554804934193349852946,
alpha=1.6732632423543772848170429916717,
name=None):
super(SELU, self).__init__()
self._scale = scale
self._alpha = alpha
self._name = name
def forward(self, x):
return F.selu(x, self._scale, self._alpha, self._name)
class LeakyReLU(layers.Layer):
"""
Leaky ReLU Activation.
......@@ -431,6 +522,167 @@ class Sigmoid(layers.Layer):
return F.sigmoid(x, self.name)
class Softplus(layers.Layer):
"""
Softplus Activation
.. math::
\text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) \\
\text{For numerical stability, the implementation reverts to the linear function when :}\,x \times \beta > threshold.
Parameters:
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
m = paddle.nn.Softplus()
out = m(x) # [0.513015, 0.598139, 0.744397, 0.854355]
"""
def __init__(self, beta=1, threshold=20, name=None):
super(Softplus, self).__init__()
self._beta = beta
self._threshold = threshold
self._name = name
def forward(self, x):
return F.softplus(x, self._beta, self._threshold, self._name)
class Softshrink(layers.Layer):
"""
Softshrink Activation
.. math::
\text{Softshrink}(x) =
\begin{cases}
x - threshold, & \text{ if } x > threshold \\
x + threshold, & \text{ if } x < -threshold \\
0, & \text{ otherwise }
\end{cases}
Parameters:
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.9, -0.2, 0.1, 0.8]))
m = paddle.nn.Softshrink()
out = m(x) # [-0.4, 0, 0, 0.3]
"""
def __init__(self, threshold=0.5, name=None):
super(Softshrink, self).__init__()
self._threshold = threshold
self._name = name
def forward(self, x):
return F.softshrink(x, self._threshold, self._name)
class Softsign(layers.Layer):
"""
Softsign Activation
.. math::
\text{Softsign}(x) = \frac{x}{1 + |x|}
Parameters:
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
m = paddle.nn.Softsign()
out = m(x) # [-0.285714, -0.166667, 0.0909091, 0.230769]
"""
def __init__(self, name=None):
super(Softsign, self).__init__()
self._name = name
def forward(self, x):
return F.softsign(x, self._name)
class Tanhshrink(layers.Layer):
"""
Tanhshrink Activation
.. math::
\text{Tanhshrink}(x) = x - \text{Tanh}(x)
Parameters:
name (str, optional): Name for the operation (optional, default is None).
For more information, please refer to :ref:`api_guide_Name`.
Shape:
- input: Tensor with any shape.
- output: Tensor with the same shape as input.
Examples:
.. code-block:: python
import paddle
import numpy as np
paddle.disable_static()
x = paddle.to_tensor(np.array([-0.4, -0.2, 0.1, 0.3]))
m = paddle.nn.Tanhshrink()
out = m(x) # [-0.020051, -0.00262468, 0.000332005, 0.00868739]
"""
def __init__(self, name=None):
super(Tanhshrink, self).__init__()
self._name = name
def forward(self, x):
return F.tanhshrink(x, self._name)
class LogSigmoid(layers.Layer):
"""
LogSigmoid Activation.
......
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