diff --git a/mindspore/nn/probability/distribution/__init__.py b/mindspore/nn/probability/distribution/__init__.py
index db8dad5c3cf..4842a0cb68a 100644
--- a/mindspore/nn/probability/distribution/__init__.py
+++ b/mindspore/nn/probability/distribution/__init__.py
@@ -25,6 +25,7 @@ from .uniform import Uniform
 from .geometric import Geometric
 from .categorical import Categorical
 from .log_normal import LogNormal
+from .logistic import Logistic
 
 __all__ = ['Distribution',
            'TransformedDistribution',
@@ -35,4 +36,5 @@ __all__ = ['Distribution',
            'Categorical',
            'Geometric',
            'LogNormal',
+           'Logistic',
            ]
diff --git a/mindspore/nn/probability/distribution/logistic.py b/mindspore/nn/probability/distribution/logistic.py
new file mode 100644
index 00000000000..0a8ff8fdb54
--- /dev/null
+++ b/mindspore/nn/probability/distribution/logistic.py
@@ -0,0 +1,327 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""Logistic Distribution"""
+import numpy as np
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+from mindspore.common import dtype as mstype
+from .distribution import Distribution
+from ._utils.utils import check_greater_zero, check_type
+from ._utils.custom_ops import exp_generic, expm1_generic, log_generic, log1p_generic
+
+
+class Logistic(Distribution):
+    """
+    Logistic distribution.
+
+    Args:
+        loc (int, float, list, numpy.ndarray, Tensor, Parameter): The location of the Logistic distribution.
+        scale (int, float, list, numpy.ndarray, Tensor, Parameter): The scale of the Logistic distribution.
+        seed (int): The seed used in sampling. The global seed is used if it is None. Default: None.
+        dtype (mindspore.dtype): The type of the event samples. Default: mstype.float32.
+        name (str): The name of the distribution. Default: 'Logistic'.
+
+    Note:
+        `scale` must be greater than zero.
+        `dist_spec_args` are `loc` and `scale`.
+        `dtype` must be a float type because Logistic distributions are continuous.
+
+    Examples:
+        >>> # To initialize a Logistic distribution of loc 3.0 and scale 4.0.
+        >>> import mindspore.nn.probability.distribution as msd
+        >>> n = msd.Logistic(3.0, 4.0, dtype=mstype.float32)
+        >>>
+        >>> # The following creates two independent Logistic distributions.
+        >>> n = msd.Logistic([3.0, 3.0], [4.0, 4.0], dtype=mstype.float32)
+        >>>
+        >>> # A Logistic distribution can be initilize without arguments.
+        >>> # In this case, `loc` and `scale` must be passed in through arguments.
+        >>> n = msd.Logistic(dtype=mstype.float32)
+        >>>
+        >>> # To use a Normal distribution in a network.
+        >>> class net(Cell):
+        >>>     def __init__(self):
+        >>>         super(net, self).__init__():
+        >>>         self.l1 = msd.Logistic(0.0, 1.0, dtype=mstype.float32)
+        >>>         self.l2 = msd.Logistic(dtype=mstype.float32)
+        >>>
+        >>>     # The following calls are valid in construct.
+        >>>     def construct(self, value, loc_b, scale_b, loc_a, scale_a):
+        >>>
+        >>>         # Private interfaces of probability functions corresponding to public interfaces, including
+        >>>         # `prob`, `log_prob`, `cdf`, `log_cdf`, `survival_function`, and `log_survival`, have the same arguments as follows.
+        >>>         # Args:
+        >>>         #     value (Tensor): the value to be evaluated.
+        >>>         #     loc (Tensor): the location of the distribution. Default: self.loc.
+        >>>         #     scale (Tensor): the scale of the distribution. Default: self.scale.
+        >>>
+        >>>         # Examples of `prob`.
+        >>>         # Similar calls can be made to other probability functions
+        >>>         # by replacing 'prob' by the name of the function
+        >>>         ans = self.l1.prob(value)
+        >>>         # Evaluate with respect to distribution b.
+        >>>         ans = self.l1.prob(value, loc_b, scale_b)
+        >>>         # `loc` and `scale` must be passed in during function calls
+        >>>         ans = self.l2.prob(value, loc_a, scale_a)
+        >>>
+        >>>         # Functions `mean`, `mode`, `sd`, `var`, and `entropy` have the same arguments.
+        >>>         # Args:
+        >>>         #     loc (Tensor): the location of the distribution. Default: self.loc.
+        >>>         #     scale (Tensor): the scale of the distribution. Default: self.scale.
+        >>>
+        >>>         # Example of `mean`. `mode`, `sd`, `var`, and `entropy` are similar.
+        >>>         ans = self.l1.mean() # return 0.0
+        >>>         ans = self.l1.mean(loc_b, scale_b) # return loc_b
+        >>>         # `loc` and `scale` must be passed in during function calls.
+        >>>         ans = self.l2.mean(loc_a, scale_a)
+        >>>
+        >>>         # Examples of `sample`.
+        >>>         # Args:
+        >>>         #     shape (tuple): the shape of the sample. Default: ()
+        >>>         #     loc (Tensor): the location of the distribution. Default: self.loc.
+        >>>         #     scale (Tensor): the scale of the distribution. Default: self.scale.
+        >>>         ans = self.l1.sample()
+        >>>         ans = self.l1.sample((2,3))
+        >>>         ans = self.l1.sample((2,3), scale_b, scale_b)
+        >>>         ans = self.l2.sample((2,3), scale_a, scale_a)
+    """
+
+    def __init__(self,
+                 loc=None,
+                 scale=None,
+                 seed=None,
+                 dtype=mstype.float32,
+                 name="Logistic"):
+        """
+        Constructor of Logistic.
+        """
+        param = dict(locals())
+        param['param_dict'] = {'loc': loc, 'scale': scale}
+        valid_dtype = mstype.float_type
+        check_type(dtype, valid_dtype, type(self).__name__)
+        super(Logistic, self).__init__(seed, dtype, name, param)
+
+        self._loc = self._add_parameter(loc, 'loc')
+        self._scale = self._add_parameter(scale, 'scale')
+        if self._scale is not None:
+            check_greater_zero(self._scale, "scale")
+
+        # ops needed for the class
+        self.cast = P.Cast()
+        self.const = P.ScalarToArray()
+        self.dtypeop = P.DType()
+        self.exp = exp_generic
+        self.expm1 = expm1_generic
+        self.fill = P.Fill()
+        self.less = P.Less()
+        self.log = log_generic
+        self.log1p = log1p_generic
+        self.logicalor = P.LogicalOr()
+        self.erf = P.Erf()
+        self.greater = P.Greater()
+        self.sigmoid = P.Sigmoid()
+        self.squeeze = P.Squeeze(0)
+        self.select = P.Select()
+        self.shape = P.Shape()
+        self.softplus = self._softplus
+        self.sqrt = P.Sqrt()
+        self.uniform = C.uniform
+
+        self.threshold = np.log(np.finfo(np.float32).eps) + 1.
+        self.tiny = np.finfo(np.float).tiny
+
+    def _softplus(self, x):
+        too_small = self.less(x, self.threshold)
+        too_large = self.greater(x, -self.threshold)
+        too_small_value = self.exp(x)
+        too_large_value = x
+        ones = self.fill(self.dtypeop(x), self.shape(x), 1.0)
+        too_small_or_too_large = self.logicalor(too_small, too_large)
+        x = self.select(too_small_or_too_large, ones, x)
+        y = self.log(self.exp(x) + 1.0)
+        return self.select(too_small, too_small_value, self.select(too_large, too_large_value, y))
+
+    def extend_repr(self):
+        if self.is_scalar_batch:
+            str_info = f'location = {self._loc}, scale = {self._scale}'
+        else:
+            str_info = f'batch_shape = {self._broadcast_shape}'
+        return str_info
+
+    @property
+    def loc(self):
+        """
+        Return the location of the distribution.
+        """
+        return self._loc
+
+    @property
+    def scale(self):
+        """
+        Return the scale of the distribution.
+        """
+        return self._scale
+
+    def _mean(self, loc=None, scale=None):
+        """
+        The mean of the distribution.
+        """
+        loc, scale = self._check_param_type(loc, scale)
+        return loc
+
+    def _mode(self, loc=None, scale=None):
+        """
+        The mode of the distribution.
+        """
+        loc, scale = self._check_param_type(loc, scale)
+        return loc
+
+    def _sd(self, loc=None, scale=None):
+        """
+        The standard deviation of the distribution.
+        """
+        loc, scale = self._check_param_type(loc, scale)
+        return scale * self.const(np.pi) / self.sqrt(self.const(3.0))
+
+    def _entropy(self, loc=None, scale=None):
+        r"""
+        Evaluate entropy.
+
+        .. math::
+            H(X) = \log(scale) + 2.
+        """
+        loc, scale = self._check_param_type(loc, scale)
+        return self.log(scale) + 2.
+
+    def _log_prob(self, value, loc=None, scale=None):
+        r"""
+        Evaluate log probability.
+
+        Args:
+            value (Tensor): The value to be evaluated.
+            loc (Tensor): The location of the distribution. Default: self.loc.
+            scale (Tensor): The scale of the distribution. Default: self.scale.
+
+        .. math::
+            z = (x - \mu) / \sigma
+            L(x) = -z * -2. * softplus(-z) - \log(\sigma)
+        """
+        value = self._check_value(value, 'value')
+        value = self.cast(value, self.dtype)
+        loc, scale = self._check_param_type(loc, scale)
+        z = (value - loc) / scale
+        return -z - 2. * self.softplus(-z) - self.log(scale)
+
+    def _cdf(self, value, loc=None, scale=None):
+        r"""
+        Evaluate the cumulative distribution function on the given value.
+
+        Args:
+            value (Tensor): The value to be evaluated.
+            loc (Tensor): The location of the distribution. Default: self.loc.
+            scale (Tensor): The scale the distribution. Default: self.scale.
+
+        .. math::
+            cdf(x) = sigmoid((x - loc) / scale)
+        """
+        value = self._check_value(value, 'value')
+        value = self.cast(value, self.dtype)
+        loc, scale = self._check_param_type(loc, scale)
+        z = (value - loc) / scale
+        return self.sigmoid(z)
+
+    def _log_cdf(self, value, loc=None, scale=None):
+        r"""
+        Evaluate the log cumulative distribution function on the given value.
+
+        Args:
+            value (Tensor): The value to be evaluated.
+            loc (Tensor): The location of the distribution. Default: self.loc.
+            scale (Tensor): The scale the distribution. Default: self.scale.
+
+        .. math::
+            log_cdf(x) = -softplus(-(x - loc) / scale)
+        """
+        value = self._check_value(value, 'value')
+        value = self.cast(value, self.dtype)
+        loc, scale = self._check_param_type(loc, scale)
+        z = (value - loc) / scale
+        return -self.softplus(-z)
+
+    def _survival_function(self, value, loc=None, scale=None):
+        r"""
+        Evaluate the survival function on the given value.
+
+        Args:
+            value (Tensor): The value to be evaluated.
+            loc (Tensor): The location of the distribution. Default: self.loc.
+            scale (Tensor): The scale the distribution. Default: self.scale.
+
+        .. math::
+            survival(x) = sigmoid(-(x - loc) / scale)
+        """
+        value = self._check_value(value, 'value')
+        value = self.cast(value, self.dtype)
+        loc, scale = self._check_param_type(loc, scale)
+        z = (value - loc) / scale
+        return self.sigmoid(-z)
+
+    def _log_survival(self, value, loc=None, scale=None):
+        r"""
+        Evaluate the log survival function on the given value.
+
+        Args:
+            value (Tensor): The value to be evaluated.
+            loc (Tensor): The location of the distribution. Default: self.loc.
+            scale (Tensor): The scale the distribution. Default: self.scale.
+
+        .. math::
+            survival(x) = -softplus((x - loc) / scale)
+        """
+        value = self._check_value(value, 'value')
+        value = self.cast(value, self.dtype)
+        loc, scale = self._check_param_type(loc, scale)
+        z = (value - loc) / scale
+        return -self.softplus(z)
+
+    def _sample(self, shape=(), loc=None, scale=None):
+        """
+        Sampling.
+
+        Args:
+            shape (tuple): The shape of the sample. Default: ().
+            loc (Tensor): The location of the samples. Default: self.loc.
+            scale (Tensor): The scale of the samples. Default: self.scale.
+
+        Returns:
+            Tensor, with the shape being shape + batch_shape.
+        """
+        shape = self.checktuple(shape, 'shape')
+        loc, scale = self._check_param_type(loc, scale)
+        batch_shape = self.shape(loc + scale)
+        origin_shape = shape + batch_shape
+        if origin_shape == ():
+            sample_shape = (1,)
+        else:
+            sample_shape = origin_shape
+        l_zero = self.const(self.tiny)
+        h_one = self.const(1.0)
+        sample_uniform = self.uniform(sample_shape, l_zero, h_one, self.seed)
+        sample = self.log(sample_uniform) - self.log1p(sample_uniform)
+        sample = sample * scale + loc
+        value = self.cast(sample, self.dtype)
+        if origin_shape == ():
+            value = self.squeeze(value)
+        return value
diff --git a/tests/st/probability/distribution/test_logistic.py b/tests/st/probability/distribution/test_logistic.py
new file mode 100644
index 00000000000..2292f37ab56
--- /dev/null
+++ b/tests/st/probability/distribution/test_logistic.py
@@ -0,0 +1,227 @@
+# Copyright 2019 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""test cases for Logistic distribution"""
+import numpy as np
+from scipy import stats
+import mindspore.context as context
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import Tensor
+from mindspore import dtype
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+class Prob(nn.Cell):
+    """
+    Test class: probability of Logistic distribution.
+    """
+    def __init__(self):
+        super(Prob, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.l.prob(x_)
+
+def test_pdf():
+    """
+    Test pdf.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_pdf = logistic_benchmark.pdf([1.0, 2.0]).astype(np.float32)
+    pdf = Prob()
+    output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect_pdf) < tol).all()
+
+class LogProb(nn.Cell):
+    """
+    Test class: log probability of Logistic distribution.
+    """
+    def __init__(self):
+        super(LogProb, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.l.log_prob(x_)
+
+def test_log_likelihood():
+    """
+    Test log_pdf.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_logpdf = logistic_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
+    logprob = LogProb()
+    output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect_logpdf) < tol).all()
+
+class Basics(nn.Cell):
+    """
+    Test class: mean/sd/mode of Logistic distribution.
+    """
+    def __init__(self):
+        super(Basics, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([2.0, 4.0]), dtype=dtype.float32)
+
+    def construct(self):
+        return self.l.mean(), self.l.sd(), self.l.mode()
+
+def test_basics():
+    """
+    Test mean/standard deviation/mode.
+    """
+    basics = Basics()
+    mean, sd, mode = basics()
+    expect_mean = [3.0, 3.0]
+    expect_sd = np.pi * np.array([2.0, 4.0]) / np.sqrt(np.array([3.0]))
+    tol = 1e-6
+    assert (np.abs(mean.asnumpy() - expect_mean) < tol).all()
+    assert (np.abs(mode.asnumpy() - expect_mean) < tol).all()
+    assert (np.abs(sd.asnumpy() - expect_sd) < tol).all()
+
+class Sampling(nn.Cell):
+    """
+    Test class: sample of Logistic distribution.
+    """
+    def __init__(self, shape, seed=0):
+        super(Sampling, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), seed=seed, dtype=dtype.float32)
+        self.shape = shape
+
+    def construct(self, mean=None, sd=None):
+        return self.l.sample(self.shape, mean, sd)
+
+def test_sample():
+    """
+    Test sample.
+    """
+    shape = (2, 3)
+    seed = 10
+    mean = Tensor([2.0], dtype=dtype.float32)
+    sd = Tensor([2.0, 2.0, 2.0], dtype=dtype.float32)
+    sample = Sampling(shape, seed=seed)
+    output = sample(mean, sd)
+    assert output.shape == (2, 3, 3)
+
+class CDF(nn.Cell):
+    """
+    Test class: cdf of Logistic distribution.
+    """
+    def __init__(self):
+        super(CDF, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.l.cdf(x_)
+
+
+def test_cdf():
+    """
+    Test cdf.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_cdf = logistic_benchmark.cdf([1.0, 2.0]).astype(np.float32)
+    cdf = CDF()
+    output = cdf(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 2e-5
+    assert (np.abs(output.asnumpy() - expect_cdf) < tol).all()
+
+class LogCDF(nn.Cell):
+    """
+    Test class: log_cdf of Logistic distribution.
+    """
+    def __init__(self):
+        super(LogCDF, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.l.log_cdf(x_)
+
+def test_log_cdf():
+    """
+    Test log cdf.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_logcdf = logistic_benchmark.logcdf([1.0, 2.0]).astype(np.float32)
+    logcdf = LogCDF()
+    output = logcdf(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 5e-5
+    assert (np.abs(output.asnumpy() - expect_logcdf) < tol).all()
+
+class SF(nn.Cell):
+    """
+    Test class: survival function of Logistic distribution.
+    """
+    def __init__(self):
+        super(SF, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.l.survival_function(x_)
+
+def test_survival():
+    """
+    Test log_survival.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_survival = logistic_benchmark.sf([1.0, 2.0]).astype(np.float32)
+    survival_function = SF()
+    output = survival_function(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 2e-5
+    assert (np.abs(output.asnumpy() - expect_survival) < tol).all()
+
+class LogSF(nn.Cell):
+    """
+    Test class: log survival function of Logistic distribution.
+    """
+    def __init__(self):
+        super(LogSF, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self, x_):
+        return self.l.log_survival(x_)
+
+def test_log_survival():
+    """
+    Test log_survival.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_log_survival = logistic_benchmark.logsf([1.0, 2.0]).astype(np.float32)
+    log_survival = LogSF()
+    output = log_survival(Tensor([1.0, 2.0], dtype=dtype.float32))
+    tol = 2e-5
+    assert (np.abs(output.asnumpy() - expect_log_survival) < tol).all()
+
+class EntropyH(nn.Cell):
+    """
+    Test class: entropy of Logistic distribution.
+    """
+    def __init__(self):
+        super(EntropyH, self).__init__()
+        self.l = msd.Logistic(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
+
+    def construct(self):
+        return self.l.entropy()
+
+def test_entropy():
+    """
+    Test entropy.
+    """
+    logistic_benchmark = stats.logistic(np.array([3.0]), np.array([[2.0], [4.0]]))
+    expect_entropy = logistic_benchmark.entropy().astype(np.float32)
+    entropy = EntropyH()
+    output = entropy()
+    tol = 1e-6
+    assert (np.abs(output.asnumpy() - expect_entropy) < tol).all()
diff --git a/tests/ut/python/nn/probability/distribution/test_logistic.py b/tests/ut/python/nn/probability/distribution/test_logistic.py
new file mode 100644
index 00000000000..404cf71ee2b
--- /dev/null
+++ b/tests/ut/python/nn/probability/distribution/test_logistic.py
@@ -0,0 +1,195 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Test nn.probability.distribution.logistic.
+"""
+import pytest
+
+import mindspore.nn as nn
+import mindspore.nn.probability.distribution as msd
+from mindspore import dtype
+from mindspore import Tensor
+
+def test_logistic_shape_errpr():
+    """
+    Invalid shapes.
+    """
+    with pytest.raises(ValueError):
+        msd.Logistic([[2.], [1.]], [[2.], [3.], [4.]], dtype=dtype.float32)
+
+def test_type():
+    with pytest.raises(TypeError):
+        msd.Logistic(0., 1., dtype=dtype.int32)
+
+def test_name():
+    with pytest.raises(TypeError):
+        msd.Logistic(0., 1., name=1.0)
+
+def test_seed():
+    with pytest.raises(TypeError):
+        msd.Logistic(0., 1., seed='seed')
+
+def test_scale():
+    with pytest.raises(ValueError):
+        msd.Logistic(0., 0.)
+    with pytest.raises(ValueError):
+        msd.Logistic(0., -1.)
+
+def test_arguments():
+    """
+    args passing during initialization.
+    """
+    l = msd.Logistic()
+    assert isinstance(l, msd.Distribution)
+    l = msd.Logistic([3.0], [4.0], dtype=dtype.float32)
+    assert isinstance(l, msd.Distribution)
+
+
+class LogisticProb(nn.Cell):
+    """
+    logistic distribution: initialize with loc/scale.
+    """
+    def __init__(self):
+        super(LogisticProb, self).__init__()
+        self.logistic = msd.Logistic(3.0, 4.0, dtype=dtype.float32)
+
+    def construct(self, value):
+        prob = self.logistic.prob(value)
+        log_prob = self.logistic.log_prob(value)
+        cdf = self.logistic.cdf(value)
+        log_cdf = self.logistic.log_cdf(value)
+        sf = self.logistic.survival_function(value)
+        log_sf = self.logistic.log_survival(value)
+        return prob + log_prob + cdf + log_cdf + sf + log_sf
+
+def test_logistic_prob():
+    """
+    Test probability functions: passing value through construct.
+    """
+    net = LogisticProb()
+    value = Tensor([0.5, 1.0], dtype=dtype.float32)
+    ans = net(value)
+    assert isinstance(ans, Tensor)
+
+
+class LogisticProb1(nn.Cell):
+    """
+    logistic distribution: initialize without loc/scale.
+    """
+    def __init__(self):
+        super(LogisticProb1, self).__init__()
+        self.logistic = msd.Logistic()
+
+    def construct(self, value, mu, s):
+        prob = self.logistic.prob(value, mu, s)
+        log_prob = self.logistic.log_prob(value, mu, s)
+        cdf = self.logistic.cdf(value, mu, s)
+        log_cdf = self.logistic.log_cdf(value, mu, s)
+        sf = self.logistic.survival_function(value, mu, s)
+        log_sf = self.logistic.log_survival(value, mu, s)
+        return prob + log_prob + cdf + log_cdf + sf + log_sf
+
+def test_logistic_prob1():
+    """
+    Test probability functions: passing loc/scale, value through construct.
+    """
+    net = LogisticProb1()
+    value = Tensor([0.5, 1.0], dtype=dtype.float32)
+    mu = Tensor([0.0], dtype=dtype.float32)
+    s = Tensor([1.0], dtype=dtype.float32)
+    ans = net(value, mu, s)
+    assert isinstance(ans, Tensor)
+
+class KL(nn.Cell):
+    """
+    Test kl_loss. Should raise NotImplementedError.
+    """
+    def __init__(self):
+        super(KL, self).__init__()
+        self.logistic = msd.Logistic(3.0, 4.0)
+
+    def construct(self, mu, s):
+        kl = self.logistic.kl_loss('Logistic', mu, s)
+        return kl
+
+class Crossentropy(nn.Cell):
+    """
+    Test cross entropy. Should raise NotImplementedError.
+    """
+    def __init__(self):
+        super(Crossentropy, self).__init__()
+        self.logistic = msd.Logistic(3.0, 4.0)
+
+    def construct(self, mu, s):
+        cross_entropy = self.logistic.cross_entropy('Logistic', mu, s)
+        return cross_entropy
+
+
+class LogisticBasics(nn.Cell):
+    """
+    Test class: basic loc/scale function.
+    """
+    def __init__(self):
+        super(LogisticBasics, self).__init__()
+        self.logistic = msd.Logistic(3.0, 4.0, dtype=dtype.float32)
+
+    def construct(self):
+        mean = self.logistic.mean()
+        sd = self.logistic.sd()
+        mode = self.logistic.mode()
+        entropy = self.logistic.entropy()
+        return mean + sd + mode + entropy
+
+def test_bascis():
+    """
+    Test mean/sd/mode/entropy functionality of logistic.
+    """
+    net = LogisticBasics()
+    ans = net()
+    assert isinstance(ans, Tensor)
+    mu = Tensor(1.0, dtype=dtype.float32)
+    s = Tensor(1.0, dtype=dtype.float32)
+    with pytest.raises(NotImplementedError):
+        kl = KL()
+        ans = kl(mu, s)
+    with pytest.raises(NotImplementedError):
+        crossentropy = Crossentropy()
+        ans = crossentropy(mu, s)
+
+class LogisticConstruct(nn.Cell):
+    """
+    logistic distribution: going through construct.
+    """
+    def __init__(self):
+        super(LogisticConstruct, self).__init__()
+        self.logistic = msd.Logistic(3.0, 4.0)
+        self.logistic1 = msd.Logistic()
+
+    def construct(self, value, mu, s):
+        prob = self.logistic('prob', value)
+        prob1 = self.logistic('prob', value, mu, s)
+        prob2 = self.logistic1('prob', value, mu, s)
+        return prob + prob1 + prob2
+
+def test_logistic_construct():
+    """
+    Test probability function going through construct.
+    """
+    net = LogisticConstruct()
+    value = Tensor([0.5, 1.0], dtype=dtype.float32)
+    mu = Tensor([0.0], dtype=dtype.float32)
+    s = Tensor([1.0], dtype=dtype.float32)
+    ans = net(value, mu, s)
+    assert isinstance(ans, Tensor)