Python tensor.iscalar方法代码示例

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def __getitem__(self, args):
        if not isinstance(args, tuple):
            args = args,

        if len(args) == 2:
            scalar_arg_1 = (numpy.isscalar(args[0]) or
                            getattr(args[0], 'type', None) == tensor.iscalar)
            scalar_arg_2 = (numpy.isscalar(args[1]) or
                            getattr(args[1], 'type', None) == tensor.iscalar)
            if scalar_arg_1 and scalar_arg_2:
                ret = get_item_scalar(self, args)
            elif isinstance(args[0], list):
                ret = get_item_2lists(self, args[0], args[1])
            else:
                ret = get_item_2d(self, args)
        elif isinstance(args[0], list):
            ret = get_item_list(self, args[0])
        else:
            ret = get_item_2d(self, args)
        return ret 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_perform(self):
        x = tensor.matrix()
        y = tensor.scalar()
        z = tensor.iscalar()

        f = function([x, y, z], fill_diagonal_offset(x, y, z))
        for test_offset in (-5, -4, -1, 0, 1, 4, 5):
            for shp in [(8, 8), (5, 8), (8, 5), (5, 5)]:
                a = numpy.random.rand(*shp).astype(config.floatX)
                val = numpy.cast[config.floatX](numpy.random.rand())
                out = f(a, val, test_offset)
                # We can't use numpy.fill_diagonal as it is bugged.
                assert numpy.allclose(numpy.diag(out, test_offset), val)
                if test_offset >= 0:
                   assert (out == val).sum() == min( min(a.shape),
                                            a.shape[1]-test_offset )
                else:
                    assert (out == val).sum() == min( min(a.shape),
                                            a.shape[0]+test_offset ) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_infer_shape(self):
        a = tensor.dvector()
        self._compile_and_check([a], [self.op(a, 16, 0)],
                                [numpy.random.rand(12)],
                               self.op_class)
        a = tensor.dmatrix()
        for var in [self.op(a, 16, 1), self.op(a, None, 1),
                     self.op(a, 16, None), self.op(a, None, None)]:
            self._compile_and_check([a], [var],
                                    [numpy.random.rand(12, 4)],
                                    self.op_class)
        b = tensor.iscalar()
        for var in [self.op(a, 16, b), self.op(a, None, b)]:
            self._compile_and_check([a, b], [var],
                                    [numpy.random.rand(12, 4), 0],
                                    self.op_class) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_shape_i_scalar(self):
        # Each axis is treated independently by shape_i/shape operators

        mode_opt = self.mode.including("fast_run")

        v_data = numpy.array(numpy.arange(5), dtype=self.dtype)
        t_data = self.shared(v_data)
        start = tensor.iscalar('b')
        stop = tensor.iscalar('e')
        step = tensor.iscalar('s')
        f = self.function([start, stop, step],
                          t_data[start:stop:step].shape,
                          mode=mode_opt,
                          op=self.ops,
                          N=0)
        assert tensor.Subtensor not in [x.op for x in f.maker.
            fgraph.toposort()]
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for stop in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
                for step in [-3, -1, 2, 5]:
                    assert numpy.all(f(start, stop, step) ==
                                     v_data[start:stop:step].shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_slice_canonical_form_0(self):
        start = tensor.iscalar('b')
        stop = tensor.iscalar('e')
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, stop, step), length)
        f = self.function([start, stop, step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
                for step in [-6, -3, -1, 2, 5]:
                    out = f(start, stop, step, length)
                    t_out = a[out[0]:out[1]:out[2]][::out[3]]
                    v_out = a[start:stop:step]
                    assert numpy.all(t_out == v_out)
                    assert numpy.all(t_out.shape == v_out.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_slice_canonical_form_1(self):
        stop = tensor.iscalar('e')
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(None, stop, step), length)
        f = self.function([stop, step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for step in [-6, -3, -1, 2, 5]:
                out = f(stop, step, length)
                t_out = a[out[0]:out[1]:out[2]][::out[3]]
                v_out = a[:stop:step]
                assert numpy.all(t_out == v_out)
                assert numpy.all(t_out.shape == v_out.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_slice_canonical_form_3(self):
        start = tensor.iscalar('b')
        stop = tensor.iscalar('e')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, stop, None), length)
        f = self.function([start, stop, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
                out = f(start, stop, length)
                t_out = a[out[0]:out[1]:out[2]][::out[3]]
                v_out = a[start:stop:None]
                assert numpy.all(t_out == v_out)
                assert numpy.all(t_out.shape == v_out.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_slice_canonical_form_4(self):
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(None, None, step), length)
        f = self.function([step, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for step in [-6, -3, -1, 2, 5]:
            out = f(step, length)
            t_out = a[out[0]:out[1]:out[2]][::out[3]]
            v_out = a[None:None:step]
            assert numpy.all(t_out == v_out)
            assert numpy.all(t_out.shape == v_out.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_slice_canonical_form_5(self):
        start = tensor.iscalar('b')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, None, None), length)
        f = self.function([start, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for start in [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            out = f(start, length)
            t_out = a[out[0]:out[1]:out[2]][::out[3]]
            v_out = a[start:None:None]
            assert numpy.all(t_out == v_out)
            assert numpy.all(t_out.shape == v_out.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_slice_canonical_form_6(self):
        stop = tensor.iscalar('e')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(None, stop, None), length)
        f = self.function([stop, length], [
            tensor.as_tensor_variable(cnf[0].start),
            tensor.as_tensor_variable(cnf[0].stop),
            tensor.as_tensor_variable(cnf[0].step),
            tensor.as_tensor_variable(cnf[1])], N=0, op=self.ops)

        length = 5
        a = numpy.arange(length)
        for stop in  [-8, -5, -4, -1, 0, 1, 4, 5, 8]:
            out = f(stop, length)
            t_out = a[out[0]:out[1]:out[2]][::out[3]]
            v_out = a[None:stop:None]
            assert numpy.all(t_out == v_out)
            assert numpy.all(t_out.shape == v_out.shape) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_advanced_indexing(self):
        # tests advanced indexing in Theano for 2D and 3D tensors
        rng = numpy.random.RandomState(utt.seed_rng())
        a = rng.uniform(size=(3, 3))
        b = theano.shared(a)
        i = tensor.iscalar()
        j = tensor.iscalar()
        z = b[[i, j], :]
        f1 = theano.function([i, j], z)
        cmd = f1(0, 1) == a[[0, 1], :]
        self.assertTrue(cmd.all())

        aa = rng.uniform(size=(4, 2, 3))
        bb = theano.shared(aa)
        k = tensor.iscalar()
        z = bb[[i, j, k], :, i:k]
        f2 = theano.function([i, j, k], z)
        cmd = f2(0, 1, 2) == aa[[0, 1, 2], :, 0:2]
        self.assertTrue(cmd.all()) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def setUp(self):
        super(Test_local_elemwise_alloc, self).setUp()
        self.fast_run_mode = mode_with_gpu

        # self.vec = tensor.vector('vec', dtype=dtype)
        # self.mat = tensor.matrix('mat', dtype=dtype)
        # self.tens = tensor.tensor3('tens', dtype=dtype)

        # self.alloc_wo_dep = basic_ops.gpu_alloc(self.vec, 2, 2)
        # self.alloc_w_dep = basic_ops.gpu_alloc(self.vec, *self.mat.shape)

        self.alloc_wo_dep = basic_ops.gpu_alloc(self.vec, 2, 2)
        self.alloc_w_dep = basic_ops.gpu_alloc(self.vec, *self.mat.shape)
        self.alloc_w_dep_tens = basic_ops.gpu_alloc(
            self.vec,
            self.tens.shape[0],
            self.tens.shape[1]
        )
        self.tv_wo_dep = basic_ops.gpu_alloc(self.vec, 5, 5)
        self.tm_wo_dep = basic_ops.gpu_alloc(self.mat, 5, 5, 5)
        self.s = tensor.iscalar('s')
        self.tv_w_dep = basic_ops.gpu_alloc(self.vec, self.s, self.s)
        self.tm_w_dep = basic_ops.gpu_alloc(self.mat, 5, 5, 5)
        self.row = tensor.row(dtype=self.dtype)
        self.o = basic_ops.gpu_alloc(self.row, 5, 5) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_select_distinct(self):
        """
        Tests that MultinomialWOReplacementFromUniform always selects distinct elements
        """
        p = tensor.fmatrix()
        u = tensor.fvector()
        n = tensor.iscalar()
        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)

        f = function([p, u, n], m, allow_input_downcast=True)

        n_elements = 1000
        all_indices = range(n_elements)
        numpy.random.seed(12345)
        for i in [5, 10, 50, 100, 500, n_elements]:
            uni = numpy.random.rand(i).astype(config.floatX)
            pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
            pvals /= pvals.sum(1)
            res = f(pvals, uni, i)
            res = numpy.squeeze(res)
            assert len(res) == i
            assert numpy.all(numpy.in1d(numpy.unique(res), all_indices)), res 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_fail_select_alot(self):
        """
        Tests that MultinomialWOReplacementFromUniform fails when asked to sample more
        elements than the actual number of elements
        """
        p = tensor.fmatrix()
        u = tensor.fvector()
        n = tensor.iscalar()
        m = multinomial.MultinomialWOReplacementFromUniform('auto')(p, u, n)

        f = function([p, u, n], m, allow_input_downcast=True)

        n_elements = 100
        n_selected = 200
        numpy.random.seed(12345)
        uni = numpy.random.rand(n_selected).astype(config.floatX)
        pvals = numpy.random.randint(1, 100, (1, n_elements)).astype(config.floatX)
        pvals /= pvals.sum(1)
        self.assertRaises(ValueError, f, pvals, uni, n_selected) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import iscalar [as 别名]
def test_n_samples_2():
    p = tensor.fmatrix()
    u = tensor.fvector()
    n = tensor.iscalar()
    m = multinomial.MultinomialFromUniform('auto')(p, u, n)

    f = function([p, u, n], m, allow_input_downcast=True)

    numpy.random.seed(12345)
    for i in [1, 5, 10, 100, 1000]:
        uni = numpy.random.rand(i).astype(config.floatX)
        pvals = numpy.random.randint(1, 1000, (1, 1000)).astype(config.floatX)
        pvals /= pvals.sum(1)
        res = f(pvals, uni, i)
        assert res.sum() == i

    for i in [1, 5, 10, 100, 1000]:
        uni = numpy.random.rand(i).astype(config.floatX)
        pvals = numpy.random.randint(
            1, 1000000, (1, 1000000)).astype(config.floatX)
        pvals /= pvals.sum(1)
        res = f(pvals, uni, i)
        assert res.sum() == i