Bernoulli and Normalpull/2605/head
Xun Deng
5 years ago
parent
9ba937b1af
commit
0aa26c1815
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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Distribution.
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The high-level components(Distributions) used to construct the probabilistic network.
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"""
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from .distribution import Distribution
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from .normal import Normal
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from .bernoulli import Bernoulli
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__all__ = ['Distribution',
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'Normal',
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'Bernoulli',]
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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Distribution operation utility functions.
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"""
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from .utils import *
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__all__ = ['check_scalar', 'convert_to_batch', 'cast_to_tensor',
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'calc_batch_size', 'check_greater',
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'check_greater_equal_zero',
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'calc_broadcast_shape_from_param',
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'check_scalar_from_param', 'check_prob']
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Utitly functions to help distribution class."""
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import numpy as np
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from mindspore.ops import operations as P
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from mindspore.ops import _utils as utils
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from ....common.tensor import Tensor
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from ....common import dtype as mstype
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def check_scalar(value):
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"""
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Check if input value is a scalar.
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"""
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return np.isscalar(value)
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def cast_to_tensor(t, dtype=mstype.float32):
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"""
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Cast an user input value into a Tensor of dtype.
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Args:
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t (int/float/list/numpy.ndarray/Tensor).
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dtype (mindspore.dtype).
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Raises:
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RuntimeError: if t cannot be cast to Tensor.
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Outputs:
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Tensor.
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"""
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if isinstance(t, Tensor):
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#check if the Tensor in shape of Tensor(4)
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if t.dim() == 0:
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value = t.asnumpy()
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return Tensor([t], dtype=dtype)
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#convert the type of tensor to dtype
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t.set_dtype(dtype)
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return t
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if isinstance(t, (list, np.ndarray)):
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return Tensor(t, dtype=dtype)
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if check_scalar(t):
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return Tensor([t], dtype=dtype)
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raise RuntimeError("Input type is not supported.")
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def calc_batch_size(batch_shape):
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"""
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Calculate the size of a given batch_shape.
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Args:
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batch_shape (tuple)
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Outputs:
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int.
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"""
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return int(np.prod(batch_shape))
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def convert_to_batch(t, batch_shape, dtype):
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"""
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Convert a Tensor to a given batch shape.
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Args:
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t (Tensor)
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batch_shape (tuple)
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dtype (mindspore.dtype)
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Raises:
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RuntimeError: if the converison cannot be done.
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Outputs:
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Tensor, with shape of batch_shape.
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"""
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t = cast_to_tensor(t, dtype)
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reshape = P.Reshape()
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if t.shape != batch_shape:
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mul = calc_batch_size(batch_shape) // t.size()
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if (calc_batch_size(batch_shape) % t.size()) != 0:
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raise RuntimeError("Cannot cast the tensor to the given batch shape.")
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temp = list(t.asnumpy()) * mul
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return reshape(Tensor(temp), batch_shape)
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return t
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def check_scalar_from_param(params):
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"""
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Check if params are all scalars.
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Args:
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params (dict): parameters used to initialized distribution.
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Notes: String parameters are excluded.
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"""
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for value in params.values():
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if isinstance(value, (str, type(params['dtype']))):
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continue
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elif check_scalar(value):
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continue
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else:
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return False
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return True
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def calc_broadcast_shape_from_param(params):
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"""
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Calculate the broadcast shape from params.
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Args:
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params (dict): parameters used to initialized distribution.
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Outputs:
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tuple.
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"""
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broadcast_shape = []
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for value in params.values():
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if isinstance(value, (str, type(params['dtype']))):
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continue
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if value is None:
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return None
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value_t = cast_to_tensor(value, params['dtype'])
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broadcast_shape = utils.get_broadcast_shape(broadcast_shape, list(value_t.shape), params['name'])
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return tuple(broadcast_shape)
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def check_greater_equal_zero(value, name):
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"""
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Check if the given Tensor is greater zero.
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Args:
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value (Tensor)
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name (str) : name of the value.
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Raises:
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ValueError: if the input value is less than zero.
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"""
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less = P.Less()
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zeros = Tensor([0.0], dtype=value.dtype)
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value = less(value, zeros)
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if value.asnumpy().any():
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raise ValueError('{} should be greater than zero.'.format(name))
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def check_greater(a, b, name_a, name_b):
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"""
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Check if Tensor b is strictly greater than Tensor a.
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Args:
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a (Tensor)
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b (Tensor)
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name_a (str): name of Tensor_a.
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name_b (str): name of Tensor_b.
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Raises:
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ValueError: if b is less than or equal to a
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"""
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less = P.Less()
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value = less(a, b)
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if not value.asnumpy().all():
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raise ValueError('{} should be less than {}'.format(name_a, name_b))
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def check_prob(p):
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"""
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Check if p is a proper probability, i.e. 0 <= p <=1.
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Args:
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p (Tensor): value to check.
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Raises:
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ValueError: if p is not a proper probability.
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"""
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less = P.Less()
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greater = P.Greater()
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zeros = Tensor([0.0], dtype=p.dtype)
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ones = Tensor([1.0], dtype=p.dtype)
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comp = less(p, zeros)
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if comp.asnumpy().any():
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raise ValueError('Probabilities should be greater than or equal to zero')
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comp = greater(p, ones)
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if comp.asnumpy().any():
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raise ValueError('Probabilities should be less than or equal to one')
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Bernoulli Distribution"""
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from mindspore.ops import operations as P
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from .distribution import Distribution
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from ._utils.utils import cast_to_tensor, check_prob
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from ...common import dtype as mstype
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class Bernoulli(Distribution):
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"""
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Example class: Bernoulli Distribution.
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Args:
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probs (int/float/list/numpy.ndarray/Tensor): probability of 1 as outcome.
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dtype (mindspore.dtype): type of the distribution, default to int32.
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Note:
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probs should be proper probabilities (0 <= p <= 1).
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Examples:
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>>> # To initialize a Bernoulli distribution which has equal probability of getting 1 and 0
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>>> b = nn.Bernoulli(0.5, dtype = dtype.int32)
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>>> # The following create two independent Bernoulli distributions
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>>> b = nn.Bernoulli([0.7, 0.2], dtype = dtype.int32)
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"""
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def __init__(self,
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probs=None,
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dtype=mstype.int32,
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name="Bernoulli"):
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"""
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Constructor of Bernoulli distribution.
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"""
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param = dict(locals())
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super(Bernoulli, self).__init__(dtype, name, param)
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if probs is not None:
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self._probs = cast_to_tensor(probs)
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# check if the input probability is valid
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check_prob(self._probs)
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else:
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self._probs = probs
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# ops needed for the class
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self.log = P.Log()
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self.add = P.TensorAdd()
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self.mul = P.Mul()
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self.sqrt = P.Sqrt()
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self.realdiv = P.RealDiv()
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def probs(self):
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"""
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Returns the probability for the outcome is 1.
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"""
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return self._probs
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def _mean(self):
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r"""
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.. math::
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MEAN(B) = probs1
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"""
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return self._probs
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def _var(self):
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r"""
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.. math::
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VAR(B) = probs1 * probs0
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"""
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probs0 = self.add(1, -1 * self._probs)
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return self.mul(probs0, self._probs)
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def _prob(self, name, value, probs=None):
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r"""
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pmf of Bernoulli distribution.
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Args:
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name (str): name of the function. Should be "prob" when passed in from construct.
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value (Tensor): a Tensor composed of only zeros and ones.
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probs (Tensor): probability of outcome is 1. Default to self._probs.
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.. math::
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pmf(k) = probs1 if k = 1;
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pmf(k) = probs0 if k = 0;
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"""
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probs1 = self._probs if probs is None else probs
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probs0 = self.add(1, -1 * probs1)
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return self.add(self.mul(probs1, value),
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self.mul(probs0, self.add(1, -1 * value)))
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def _kl_loss(self, name, dist, probs1_b):
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r"""
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Evaluate bernoulli-bernoulli kl divergence, i.e. KL(a||b).
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Args:
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name (str): name of the funtion. Should always be "kl_loss" when passed in from construct.
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dist (str): type of the distributions. Should be "Bernoulli" in this case.
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probs1_b (Tensor): probs1 of distribution b.
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.. math::
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KL(a||b) = probs1_a * \log(\fract{probs1_a}{probs1_b}) +
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probs0_a * \log(\fract{probs0_a}{probs0_b})
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"""
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if dist == 'Bernoulli':
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probs1_a = self._probs
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probs0_a = self.add(1, -1 * probs1_a)
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probs0_b = self.add(1, -1 * probs1_b)
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return self.add(probs1_a * self.log(self.realdiv(probs1_a, probs1_b)),
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probs0_a * self.log(self.realdiv(probs0_a, probs0_b)))
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return None
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def extend_repr(self):
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str_info = 'probs={}'.format(self._probs)
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return str_info
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@ -0,0 +1,232 @@
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""basic"""
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from ..cell import Cell
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from ._utils.utils import calc_broadcast_shape_from_param
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class Distribution(Cell):
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"""
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Base class for all mathematical distributions.
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Note:
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Derived class should override operations such as ,_mean, _prob,
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and _log_prob. Functions should be called through construct when
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used inside a network in the form of function name followed by
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arguments.
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Examples:
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>>> class MyNormalDistribution(Distribution):
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>>> def __init__(self):
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>>> super(MyDistribution, self).__init__()
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>>> self._mean_value = Tensor([2.0,3.0])
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>>> self._sd_value = Tensor([2.0,3.0])
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>>>
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>>> def _mean(self):
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>>> return self._mean_value
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"""
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def __init__(self,
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dtype,
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name,
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param):
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"""
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Constructor of distribution class.
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"""
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super(Distribution, self).__init__()
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self._name = name
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self._dtype = dtype
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self._parameters = {}
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# parsing parameters
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for k in param.keys():
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if not(k == 'self' or k.startswith('_')):
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self._parameters[k] = param[k]
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# some attributes
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self._broadcast_shape = calc_broadcast_shape_from_param(
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self._parameters)
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# set the function to call according to the derived class's attributes
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self._set_prob()
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self._set_log_prob()
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self._set_sd()
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def _set_prob(self):
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"""
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Set probability funtion based on the availability of _prob and _log_likehood.
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"""
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if hasattr(self, '_prob'):
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self._call_prob = self._prob
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elif hasattr(self, '_log_likelihood'):
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self._call_prob = self._calc_prob_from_log_likelihood
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def _set_sd(self):
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"""
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Set standard deviation based on the availability of _sd and _var.
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"""
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if hasattr(self, '_sd'):
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self._call_sd = self._sd
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elif hasattr(self, '_var'):
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self._call_sd = self._calc_sd_from_var
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def _set_log_prob(self):
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"""
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Set log probability based on the availability of _prob and _log_likelihood.
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"""
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if hasattr(self, '_log_likelihood'):
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self._call_log_prob = self._log_likelihood
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if hasattr(self, '_prob'):
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self._call_log_prob = self._calc_log_prob_from_prob
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def log_likelihood(self, *args):
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"""
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Evaluate the log probability at the given value.
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Note:
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value is casted to Tensor for further calculation.
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Args:
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name (str): name of the calling function.
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value (Tensor): values to be evaluated.
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mean (Tensor): mean of the distirbution. Default: self.mean.
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sd (Tensor): standard deviation of the distribution. Default: self.sd.
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Outputs:
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Tensor, shape: broadcast_shape of the distribution.
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"""
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return self._call_log_prob(*args)
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def _calc_prob_from_log_likelihood(self, *args):
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r"""
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Evaluate prob from log probability.
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.. math::
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probability(x) = \exp(log_likehood(x))
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Args:
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name (str): name of the calling function.
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value (Tensor): values to be evaluated.
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mean (Tensor): mean of the distribution. Default: self.mean.
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sd (Tensor): standard deviation of the distritbuion. Default: self.sd.
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"""
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return self.exp(self._log_likelihood(*args))
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def _call_prob(self, *args):
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"""
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Raises:
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NotImplementedError when derived class didn't override _prob or _log_likelihood.
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"""
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raise NotImplementedError('pdf/pmf is not implemented: {}'.format(type(self).__name__))
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def _call_log_prob(self, *args):
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"""
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Raises:
|
||||
NotImplementedError when derived class didn't override _prob or _log_likelihood.
|
||||
"""
|
||||
raise NotImplementedError('log_probability is not implemented: {}'.format(type(self).__name__))
|
||||
|
||||
def _call_sd(self):
|
||||
"""
|
||||
Raises:
|
||||
NotImplementedError when derived class didn't override _sd or _var.
|
||||
"""
|
||||
raise NotImplementedError('standard deviation is not implemented: {}'.format(type(self).__name__))
|
||||
|
||||
def prob(self, *args):
|
||||
"""
|
||||
Evaluate the prob (pdf or pmf) at given value.
|
||||
|
||||
Note:
|
||||
value is casted to Tensor for further calculation.
|
||||
|
||||
Args:
|
||||
name (str): name of the calling function.
|
||||
value (Tensor): values to be evaluated.
|
||||
mean (Tensor): mean of the distribution.
|
||||
sd (Tensor): standard deviation of the distritbuion.
|
||||
|
||||
Outputs:
|
||||
Tensor, shape: broadcast_shape of the distribution.
|
||||
"""
|
||||
return self._call_prob(*args)
|
||||
|
||||
def _calc_log_prob_from_prob(self, *args):
|
||||
r"""
|
||||
Evaluate log probability from probability.
|
||||
|
||||
.. math::
|
||||
log_prob(x) = \log(prob(x))
|
||||
"""
|
||||
return self.log(self._prob(*args))
|
||||
|
||||
def kl_loss(self, **kwargs):
|
||||
"""
|
||||
Evaluate the KL divergence. Parameters of the second distribution should be
|
||||
passed in through **kwargs.
|
||||
|
||||
Outputs:
|
||||
Tensor, shape: broadcast_shape of the distribution and input distribution.
|
||||
"""
|
||||
return self._kl_loss(**kwargs)
|
||||
|
||||
def mean(self, **kwargs):
|
||||
"""
|
||||
Evaluate the mean.
|
||||
|
||||
Outputs:
|
||||
Tensor, shape: broadcast_shape of the distribution.
|
||||
"""
|
||||
return self._mean(**kwargs)
|
||||
|
||||
def sd(self, **kwargs):
|
||||
"""
|
||||
Evaluate the standard deviation.
|
||||
|
||||
Outputs:
|
||||
Tensor, with shape of broadcast_shape of the distribution.
|
||||
"""
|
||||
return self._call_sd(**kwargs)
|
||||
|
||||
def _calc_sd_from_var(self, **kwargs):
|
||||
r"""
|
||||
Evaluate log probability from probability.
|
||||
|
||||
.. math::
|
||||
STD(x) = \sqrt(VAR(x))
|
||||
"""
|
||||
return self.sqrt(self._var(**kwargs))
|
||||
|
||||
def construct(self, *inputs):
|
||||
"""
|
||||
Override construct in Cell.
|
||||
|
||||
Args:
|
||||
*inputs: inputs[0] is always the name of the function.
|
||||
|
||||
Notes:
|
||||
Always raise RuntimeError as Distribution should not be called directly.
|
||||
"""
|
||||
|
||||
if inputs[0] == 'log_prob':
|
||||
return self._call_log_prob(*inputs)
|
||||
if inputs[0] == 'prob':
|
||||
return self._call_prob(*inputs)
|
||||
if inputs[0] == 'kl_loss':
|
||||
return self._kl_loss(*inputs)
|
||||
if inputs[0] == 'mean':
|
||||
return self._mean()
|
||||
if inputs[0] == 'sd':
|
||||
return self._call_sd()
|
||||
return None
|
||||
@ -0,0 +1,124 @@
|
||||
# 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.
|
||||
# ============================================================================
|
||||
"""Normal Distribution"""
|
||||
import numpy as np
|
||||
from mindspore.ops import operations as P
|
||||
from .distribution import Distribution
|
||||
from ._utils.utils import convert_to_batch, check_greater_equal_zero
|
||||
from ...common import dtype as mstype
|
||||
from ...context import get_context
|
||||
|
||||
class Normal(Distribution):
|
||||
"""
|
||||
Example class: Normal distribution.
|
||||
|
||||
Args:
|
||||
mean (int/float/list/numpy.ndarray/Tensor): mean of the Gaussian distribution
|
||||
standard deviation (int/float/list/numpy.ndarray/Tensor): vairance of the Gaussian distribution
|
||||
dtype (mindspore.dtype): type of the distribution
|
||||
|
||||
Note:
|
||||
Standard deviation should be greater than zero.
|
||||
|
||||
Examples:
|
||||
>>> # To initialize a normal distribution of mean 3.0 and standard deviation 4.0
|
||||
>>> n = nn.Normal(3.0, 4.0, dtype=dtype.float32)
|
||||
>>> # The following create two independent normal distributions
|
||||
>>> n = nn.Normal([3.0, 3.0], [4.0, 4.0], dtype=dtype.float32)
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
mean=None,
|
||||
sd=None,
|
||||
dtype=mstype.float32,
|
||||
name="Normal"):
|
||||
"""
|
||||
Constructor of normal distribution.
|
||||
"""
|
||||
param = dict(locals())
|
||||
super(Normal, self).__init__(dtype, name, param)
|
||||
if mean is not None and sd is not None:
|
||||
self._mean_value = convert_to_batch(mean, self._broadcast_shape, dtype)
|
||||
self._sd_value = convert_to_batch(sd, self._broadcast_shape, dtype)
|
||||
#check validity of standard deviation
|
||||
check_greater_equal_zero(self._sd_value, "Standard deviation")
|
||||
else:
|
||||
self._mean_value = mean
|
||||
self._sd_value = sd
|
||||
|
||||
#ops needed for the class
|
||||
self.exp = P.Exp()
|
||||
self.add = P.TensorAdd()
|
||||
self.sq = P.Square()
|
||||
self.log = P.Log()
|
||||
self.sqrt = P.Sqrt()
|
||||
self.realdiv = P.RealDiv()
|
||||
self.expm1 = P.Expm1() if get_context('device_target') == 'Ascend' else self._expm1_by_step
|
||||
|
||||
def _expm1_by_step(self, x):
|
||||
"""
|
||||
Expm1 ops under GPU context.
|
||||
"""
|
||||
return self.add(self.exp(x), -1)
|
||||
|
||||
def _mean(self):
|
||||
"""
|
||||
Mean of the distribution.
|
||||
"""
|
||||
return self._mean_value
|
||||
|
||||
def _sd(self):
|
||||
"""
|
||||
Standard deviation of the distribution.
|
||||
"""
|
||||
return self._sd_value
|
||||
|
||||
def _log_likelihood(self, name, value, mean=None, sd=None):
|
||||
r"""
|
||||
Evaluate log probability.
|
||||
|
||||
.. math::
|
||||
L(x) = -1* \fract{(x - \mu)^2}{2. * \sigma^2} - \log(\sqrt(2* \pi * \sigma^2))
|
||||
"""
|
||||
mean = self._mean_value if mean is None else mean
|
||||
sd = self._sd_value if sd is None else sd
|
||||
unnormalized_log_prob = -1. * self.realdiv(self.sq(self.add(value, -1. * mean)),
|
||||
2. * self.sq(sd))
|
||||
neg_normalization = -1. * self.log(self.sqrt(2. * np.pi * self.sq(sd)))
|
||||
return self.add(unnormalized_log_prob, neg_normalization)
|
||||
|
||||
def _kl_loss(self, name, dist, mean, sd):
|
||||
r"""
|
||||
Evaluate Normal-Normal kl divergence, i.e. KL(a||b).
|
||||
|
||||
Args:
|
||||
name (str): name of the funtion passed in from construct. Should always be "kl_loss".
|
||||
dist (str): type of the distributions. Should be "Normal" in this case.
|
||||
mean (Tensor): mean of distribution b.
|
||||
sd (Tensor): standard deviation distribution b.
|
||||
|
||||
.. math::
|
||||
KL(a||b) = 0.5 * (\fract{MEAN(a)}{STD(b)} - \fract{MEAN(b)}{STD(b)}) ^ 2 +
|
||||
0.5 * EXPM1(2 * (\log(STD(a)) - \log(STD(b))) - (\log(STD(a)) - \log(STD(b)))
|
||||
"""
|
||||
if dist == 'Normal':
|
||||
diff_log_scale = self.add(self.log(self._sd_value), - self.log(sd))
|
||||
squared_diff = self.sq(self.add(self.realdiv(self._mean_value, sd), - self.realdiv(mean, sd)))
|
||||
return self.add(self.add(0.5 * squared_diff, 0.5 * self.expm1(2 * diff_log_scale)), - diff_log_scale)
|
||||
return None
|
||||
|
||||
def extend_repr(self):
|
||||
str_info = 'mean={}, standard deviation={}'.format(self._mean_value, self._sd_value)
|
||||
return str_info
|
||||
@ -0,0 +1,128 @@
|
||||
# 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 bernoulli distribution"""
|
||||
import numpy as np
|
||||
from scipy import stats
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common.api import ms_function
|
||||
from mindspore import dtype
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
|
||||
class Net(nn.Cell):
|
||||
"""
|
||||
Test class: probability of bernoulli distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
|
||||
|
||||
@ms_function
|
||||
def construct(self, x_):
|
||||
return self.b('prob', x_)
|
||||
|
||||
class Net1(nn.Cell):
|
||||
"""
|
||||
Test class: log probability of bernoulli distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net1, self).__init__()
|
||||
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
|
||||
|
||||
@ms_function
|
||||
def construct(self, x_):
|
||||
return self.b('log_prob', x_)
|
||||
|
||||
class Net2(nn.Cell):
|
||||
"""
|
||||
Test class: kl_loss between bernoulli distributions.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net2, self).__init__()
|
||||
self.b = nn.Bernoulli(0.7, dtype=dtype.int32)
|
||||
|
||||
@ms_function
|
||||
def construct(self, x_):
|
||||
return self.b('kl_loss', 'Bernoulli', x_)
|
||||
|
||||
class Net3(nn.Cell):
|
||||
"""
|
||||
Test class: mean/sd of bernoulli distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net3, self).__init__()
|
||||
self.b = nn.Bernoulli([0.7, 0.5], dtype=dtype.int32)
|
||||
|
||||
@ms_function
|
||||
def construct(self):
|
||||
return self.b('mean'), self.b('sd')
|
||||
|
||||
def test_pmf():
|
||||
"""
|
||||
Test pmf.
|
||||
"""
|
||||
bernoulli_benchmark = stats.bernoulli(0.7)
|
||||
expect_pmf = bernoulli_benchmark.pmf([0, 1, 0, 1, 1]).astype(np.float32)
|
||||
pdf = Net()
|
||||
x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
|
||||
output = pdf(x_)
|
||||
print("expected_pmf: ", expect_pmf)
|
||||
print("ans: ", output.asnumpy())
|
||||
tol = 1e-6
|
||||
assert (output.asnumpy() - expect_pmf < tol).all()
|
||||
|
||||
def test_log_likelihood():
|
||||
"""
|
||||
Test log_pmf.
|
||||
"""
|
||||
bernoulli_benchmark = stats.bernoulli(0.7)
|
||||
expect_logpmf = bernoulli_benchmark.logpmf([0, 1, 0, 1, 1]).astype(np.float32)
|
||||
logprob = Net1()
|
||||
x_ = Tensor(np.array([0, 1, 0, 1, 1]).astype(np.int32), dtype=dtype.float32)
|
||||
output = logprob(x_)
|
||||
print("expected_log_probability: ", expect_logpmf)
|
||||
print("ans: ", output.asnumpy())
|
||||
tol = 1e-6
|
||||
assert (output.asnumpy() - expect_logpmf < tol).all()
|
||||
|
||||
def test_kl_loss():
|
||||
"""
|
||||
Test kl_loss.
|
||||
"""
|
||||
probs1_a = 0.7
|
||||
probs1_b = 0.5
|
||||
probs0_a = 1 - probs1_a
|
||||
probs0_b = 1 - probs1_b
|
||||
expect_kl_loss = probs1_a * np.log(probs1_a / probs1_b) + probs0_a * np.log(probs0_a / probs0_b)
|
||||
kl_loss = Net2()
|
||||
output = kl_loss(Tensor([probs1_b], dtype=dtype.float32))
|
||||
print("expected_kl_loss: ", expect_kl_loss)
|
||||
print("ans: ", output.asnumpy())
|
||||
tol = 1e-6
|
||||
assert (output.asnumpy() - expect_kl_loss < tol).all()
|
||||
|
||||
def test_basics():
|
||||
"""
|
||||
Test mean/standard deviation and probs.
|
||||
"""
|
||||
basics = Net3()
|
||||
mean, sd = basics()
|
||||
print("mean : ", mean)
|
||||
print("sd : ", sd)
|
||||
b = nn.Bernoulli([0.7, 0.5], dtype=dtype.int32)
|
||||
probs = b.probs()
|
||||
print("probs is ", probs)
|
||||
@ -0,0 +1,130 @@
|
||||
# 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 normal distribution"""
|
||||
import numpy as np
|
||||
from scipy import stats
|
||||
import mindspore.context as context
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore.common.api import ms_function
|
||||
from mindspore import dtype
|
||||
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
|
||||
class Net(nn.Cell):
|
||||
"""
|
||||
Test class: probability of normal distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net, self).__init__()
|
||||
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
@ms_function
|
||||
def construct(self, x_):
|
||||
return self.n('prob', x_)
|
||||
|
||||
class Net1(nn.Cell):
|
||||
"""
|
||||
Test class: log probability of normal distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net1, self).__init__()
|
||||
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
@ms_function
|
||||
def construct(self, x_):
|
||||
return self.n('log_prob', x_)
|
||||
|
||||
class Net2(nn.Cell):
|
||||
"""
|
||||
Test class: kl_loss of normal distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net2, self).__init__()
|
||||
self.n = nn.Normal(np.array([3.0]), np.array([4.0]), dtype=dtype.float32)
|
||||
|
||||
@ms_function
|
||||
def construct(self, x_, y_):
|
||||
return self.n('kl_loss', 'Normal', x_, y_)
|
||||
|
||||
class Net3(nn.Cell):
|
||||
"""
|
||||
Test class: mean/sd of normal distribution.
|
||||
"""
|
||||
def __init__(self):
|
||||
super(Net3, self).__init__()
|
||||
self.n = nn.Normal(np.array([3.0]), np.array([[2.0], [4.0]]), dtype=dtype.float32)
|
||||
|
||||
@ms_function
|
||||
def construct(self):
|
||||
return self.n('mean'), self.n('sd')
|
||||
|
||||
def test_pdf():
|
||||
"""
|
||||
Test pdf.
|
||||
"""
|
||||
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_pdf = norm_benchmark.pdf([1.0, 2.0]).astype(np.float32)
|
||||
pdf = Net()
|
||||
output = pdf(Tensor([1.0, 2.0], dtype=dtype.float32))
|
||||
print("expected_pdf: ", expect_pdf)
|
||||
print("ans: ", output.asnumpy())
|
||||
tol = 1e-6
|
||||
assert (output.asnumpy() - expect_pdf < tol).all()
|
||||
|
||||
def test_log_likelihood():
|
||||
"""
|
||||
Test log_pdf.
|
||||
"""
|
||||
norm_benchmark = stats.norm(np.array([3.0]), np.array([[2.0], [4.0]]))
|
||||
expect_logpdf = norm_benchmark.logpdf([1.0, 2.0]).astype(np.float32)
|
||||
logprob = Net1()
|
||||
output = logprob(Tensor([1.0, 2.0], dtype=dtype.float32))
|
||||
print("expected_log_probability: ", expect_logpdf)
|
||||
print("ans: ", output.asnumpy())
|
||||
tol = 1e-6
|
||||
assert (output.asnumpy() - expect_logpdf < tol).all()
|
||||
|
||||
def test_kl_loss():
|
||||
"""
|
||||
Test kl_loss.
|
||||
"""
|
||||
mean_a = np.array([3.0]).astype(np.float32)
|
||||
sd_a = np.array([4.0]).astype(np.float32)
|
||||
|
||||
mean_b = np.array([1.0]).astype(np.float32)
|
||||
sd_b = np.array([1.0]).astype(np.float32)
|
||||
|
||||
diff_log_scale = np.log(sd_a) - np.log(sd_b)
|
||||
squared_diff = np.square(mean_a / sd_b - mean_b / sd_b)
|
||||
expect_kl_loss = 0.5 * squared_diff + 0.5 * np.expm1(2 * diff_log_scale) - diff_log_scale
|
||||
|
||||
kl_loss = Net2()
|
||||
mean = Tensor(mean_b, dtype=dtype.float32)
|
||||
sd = Tensor(sd_b, dtype=dtype.float32)
|
||||
output = kl_loss(mean, sd)
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print("expected_kl_loss: ", expect_kl_loss)
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print("ans: ", output.asnumpy())
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tol = 1e-6
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assert (output.asnumpy() - expect_kl_loss < tol).all()
|
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|
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def test_basics():
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"""
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||||
Test mean/standard deviation.
|
||||
"""
|
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basics = Net3()
|
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mean, sd = basics()
|
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print("mean is ", mean)
|
||||
print("sd is ", sd)
|
||||
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