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100 lines
3.1 KiB
100 lines
3.1 KiB
5 years ago
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# Copyright 2019 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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"""test cases for scalar affine"""
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.bijector as msb
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from mindspore import Tensor
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from mindspore import dtype
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5 years ago
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
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5 years ago
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class Net(nn.Cell):
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"""
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Test class: forward pass of bijector.
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"""
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def __init__(self):
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super(Net, self).__init__()
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self.bijector = msb.Softplus(sharpness=2.0)
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def construct(self, x_):
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return self.bijector.forward(x_)
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def test_forward():
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forward = Net()
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x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
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ans = forward(Tensor(x, dtype=dtype.float32))
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expected = np.log(1 + np.exp(2 * x)) * 0.5
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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class Net1(nn.Cell):
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"""
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Test class: backward pass of bijector.
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"""
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def __init__(self):
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super(Net1, self).__init__()
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self.bijector = msb.Softplus(sharpness=2.0)
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def construct(self, x_):
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return self.bijector.inverse(x_)
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def test_backward():
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backward = Net1()
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x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
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ans = backward(Tensor(x, dtype=dtype.float32))
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expected = np.log(np.exp(2 * x) - 1) * 0.5
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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class Net2(nn.Cell):
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"""
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Test class: Forward Jacobian.
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"""
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def __init__(self):
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super(Net2, self).__init__()
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self.bijector = msb.Softplus(sharpness=2.0)
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def construct(self, x_):
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return self.bijector.forward_log_jacobian(x_)
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def test_forward_jacobian():
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forward_jacobian = Net2()
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x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
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ans = forward_jacobian(Tensor(x, dtype=dtype.float32))
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expected = np.log(np.exp(2 * x) / (1 + np.exp(2.0 * x)))
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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class Net3(nn.Cell):
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"""
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Test class: Backward Jacobian.
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"""
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def __init__(self):
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super(Net3, self).__init__()
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self.bijector = msb.Softplus(sharpness=2.0)
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def construct(self, x_):
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return self.bijector.inverse_log_jacobian(x_)
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def test_backward_jacobian():
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backward_jacobian = Net3()
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x = np.array([2.0, 3.0, 4.0, 5.0]).astype(np.float32)
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ans = backward_jacobian(Tensor(x, dtype=dtype.float32))
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expected = np.log(np.exp(2.0 * x) / np.expm1(2.0 * x))
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tol = 1e-6
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assert (np.abs(ans.asnumpy() - expected) < tol).all()
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