Python RandomStreams.updates方法代码示例

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_shuffle_row_elements(self):
        """Test that RandomStreams.shuffle_row_elements generates the right results"""
        # Check over two calls to see if the random state is correctly updated.

        # On matrices, for each row, the elements of that row should be shuffled.
        # Note that this differs from numpy.random.shuffle, where all the elements
        # of the matrix are shuffled.
        random = RandomStreams(utt.fetch_seed())
        m_input = tensor.dmatrix()
        f = function([m_input], random.shuffle_row_elements(m_input), updates=random.updates())

        # Generate the elements to be shuffled
        val_rng = numpy.random.RandomState(utt.fetch_seed()+42)
        in_mval = val_rng.uniform(-2, 2, size=(20, 5))
        fn_mval0 = f(in_mval)
        fn_mval1 = f(in_mval)
        print(in_mval[0])
        print(fn_mval0[0])
        print(fn_mval1[0])
        assert not numpy.all(in_mval == fn_mval0)
        assert not numpy.all(in_mval == fn_mval1)
        assert not numpy.all(fn_mval0 == fn_mval1)

        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
        rng = numpy.random.RandomState(int(rng_seed))
        numpy_mval0 = in_mval.copy()
        numpy_mval1 = in_mval.copy()
        for row in numpy_mval0:
            rng.shuffle(row)
        for row in numpy_mval1:
            rng.shuffle(row)

        assert numpy.all(numpy_mval0 == fn_mval0)
        assert numpy.all(numpy_mval1 == fn_mval1)

        # On vectors, the behaviour is the same as numpy.random.shuffle,
        # except that it does not work in place, but returns a shuffled vector.
        random1 = RandomStreams(utt.fetch_seed())
        v_input = tensor.dvector()
        f1 = function([v_input], random1.shuffle_row_elements(v_input))

        in_vval = val_rng.uniform(-3, 3, size=(12,))
        fn_vval = f1(in_vval)
        numpy_vval = in_vval.copy()
        vrng = numpy.random.RandomState(int(rng_seed))
        vrng.shuffle(numpy_vval)
        print(in_vval)
        print(fn_vval)
        print(numpy_vval)
        assert numpy.all(numpy_vval == fn_vval)

        # Trying to shuffle a vector with function that should shuffle
        # matrices, or vice versa, raises a TypeError
        self.assertRaises(TypeError, f1, in_mval)
        self.assertRaises(TypeError, f, in_vval)

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_basics(self):
        random = RandomStreams(utt.fetch_seed())
        fn = function([], random.uniform((2, 2)), updates=random.updates())
        gn = function([], random.normal((2, 2)), updates=random.updates())

        fn_val0 = fn()
        fn_val1 = fn()

        gn_val0 = gn()

        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit

        # print fn_val0
        numpy_val0 = rng.uniform(size=(2, 2))
        numpy_val1 = rng.uniform(size=(2, 2))
        # print numpy_val0

        assert numpy.allclose(fn_val0, numpy_val0)
        print(fn_val0)
        print(numpy_val0)
        print(fn_val1)
        print(numpy_val1)
        assert numpy.allclose(fn_val1, numpy_val1)

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_multinomial(self):
        """Test that RandomStreams.multinomial generates the same results as numpy"""
        # Check over two calls to see if the random state is correctly updated.
        random = RandomStreams(utt.fetch_seed())
        fn = function([], random.multinomial((4, 4), 1, [0.1]*10), updates=random.updates())

        fn_val0 = fn()
        fn_val1 = fn()

        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit
        numpy_val0 = rng.multinomial(1, [0.1]*10, size=(4, 4))
        numpy_val1 = rng.multinomial(1, [0.1]*10, size=(4, 4))

        assert numpy.all(fn_val0 == numpy_val0)
        assert numpy.all(fn_val1 == numpy_val1)

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_default_updates(self):
        # Basic case: default_updates
        random_a = RandomStreams(utt.fetch_seed())
        out_a = random_a.uniform((2, 2))
        fn_a = function([], out_a)
        fn_a_val0 = fn_a()
        fn_a_val1 = fn_a()
        assert not numpy.all(fn_a_val0 == fn_a_val1)

        nearly_zeros = function([], out_a + out_a - 2 * out_a)
        assert numpy.all(abs(nearly_zeros()) < 1e-5)

        # Explicit updates #1
        random_b = RandomStreams(utt.fetch_seed())
        out_b = random_b.uniform((2, 2))
        fn_b = function([], out_b, updates=random_b.updates())
        fn_b_val0 = fn_b()
        fn_b_val1 = fn_b()
        assert numpy.all(fn_b_val0 == fn_a_val0)
        assert numpy.all(fn_b_val1 == fn_a_val1)

        # Explicit updates #2
        random_c = RandomStreams(utt.fetch_seed())
        out_c = random_c.uniform((2, 2))
        fn_c = function([], out_c, updates=[out_c.update])
        fn_c_val0 = fn_c()
        fn_c_val1 = fn_c()
        assert numpy.all(fn_c_val0 == fn_a_val0)
        assert numpy.all(fn_c_val1 == fn_a_val1)

        # No updates at all
        random_d = RandomStreams(utt.fetch_seed())
        out_d = random_d.uniform((2, 2))
        fn_d = function([], out_d, no_default_updates=True)
        fn_d_val0 = fn_d()
        fn_d_val1 = fn_d()
        assert numpy.all(fn_d_val0 == fn_a_val0)
        assert numpy.all(fn_d_val1 == fn_d_val0)

        # No updates for out
        random_e = RandomStreams(utt.fetch_seed())
        out_e = random_e.uniform((2, 2))
        fn_e = function([], out_e, no_default_updates=[out_e.rng])
        fn_e_val0 = fn_e()
        fn_e_val1 = fn_e()
        assert numpy.all(fn_e_val0 == fn_a_val0)
        assert numpy.all(fn_e_val1 == fn_e_val0)

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_setitem(self):

        random = RandomStreams(234)
        out = random.uniform((2, 2))
        fn = function([], out, updates=random.updates())

        random.seed(888)

        rng = numpy.random.RandomState(utt.fetch_seed())
        random[out.rng] = numpy.random.RandomState(utt.fetch_seed())

        fn_val0 = fn()
        fn_val1 = fn()
        numpy_val0 = rng.uniform(size=(2, 2))
        numpy_val1 = rng.uniform(size=(2, 2))
        assert numpy.allclose(fn_val0, numpy_val0)
        assert numpy.allclose(fn_val1, numpy_val1)

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_permutation(self):
        """Test that RandomStreams.permutation generates the same results as numpy"""
        # Check over two calls to see if the random state is correctly updated.
        random = RandomStreams(utt.fetch_seed())
        fn = function([], random.permutation((20,), 10), updates=random.updates())

        fn_val0 = fn()
        fn_val1 = fn()

        rng_seed = numpy.random.RandomState(utt.fetch_seed()).randint(2**30)
        rng = numpy.random.RandomState(int(rng_seed))  # int() is for 32bit

        # rng.permutation outputs one vector at a time, so we iterate.
        numpy_val0 = numpy.asarray([rng.permutation(10) for i in range(20)])
        numpy_val1 = numpy.asarray([rng.permutation(10) for i in range(20)])

        assert numpy.all(fn_val0 == numpy_val0)
        assert numpy.all(fn_val1 == numpy_val1)

# 需要导入模块: from theano.tensor.shared_randomstreams import RandomStreams [as 别名]
# 或者: from theano.tensor.shared_randomstreams.RandomStreams import updates [as 别名]
    def test_seed_fn(self):
        random = RandomStreams(234)
        fn = function([], random.uniform((2, 2)), updates=random.updates())

        random.seed(utt.fetch_seed())

        fn_val0 = fn()
        fn_val1 = fn()

        rng_seed = np.random.RandomState(utt.fetch_seed()).randint(2**30)
        rng = np.random.RandomState(int(rng_seed))  #int() is for 32bit

        # print fn_val0
        numpy_val0 = rng.uniform(size=(2, 2))
        numpy_val1 = rng.uniform(size=(2, 2))
        # print numpy_val0

        assert np.allclose(fn_val0, numpy_val0)
        assert np.allclose(fn_val1, numpy_val1)