Python theano.tensor.as_tensor_variable() Examples

def make_node(self, alpha, x, y, z):
        if not _is_sparse_variable(x) and not _is_sparse_variable(y):
            # If x and y are tensor, we don't want to use this class
            # We should use Dot22 and Gemm in that case.
            raise TypeError(x)

        dtype_out = scalar.upcast(alpha.type.dtype, x.type.dtype,
                                  y.type.dtype, z.type.dtype)
        alpha = tensor.as_tensor_variable(alpha)
        z = tensor.as_tensor_variable(z)

        assert z.ndim == 2
        assert alpha.type.broadcastable == (True,) * alpha.ndim
        if not _is_sparse_variable(x):
            x = tensor.as_tensor_variable(x)
            assert y.format in ["csr", "csc"]
            assert x.ndim == 2
        if not _is_sparse_variable(y):
            y = tensor.as_tensor_variable(y)
            assert x.format in ["csr", "csc"]
            assert y.ndim == 2

        return gof.Apply(self, [alpha, x, y, z],
                         [tensor.tensor(dtype=dtype_out,
                                        broadcastable=(False, False))]) 

def make_node(self, img, kern):
        # Make sure both inputs are Variables with the same Type
        if not isinstance(img, theano.Variable):
            img = as_tensor_variable(img)
        if not isinstance(kern, theano.Variable):
            kern = as_tensor_variable(kern)
        ktype = img.type.clone(dtype=kern.dtype,
                               broadcastable=kern.broadcastable)
        kern = ktype.filter_variable(kern)

        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if kern.type.ndim != 4:
            raise TypeError('kern must be 4D tensor')

        broadcastable = [img.broadcastable[0],
                         kern.broadcastable[0],
                         False, False]
        output = img.type.clone(broadcastable=broadcastable)()
        return Apply(self, [img, kern], [output]) 

def make_node(self, img, topgrad, shape):
        # Make sure both inputs are Variables with the same Type
        if not isinstance(img, theano.Variable):
            img = as_tensor_variable(img)
        if not isinstance(topgrad, theano.Variable):
            topgrad = as_tensor_variable(topgrad)
        gtype = img.type.clone(dtype=topgrad.dtype,
                               broadcastable=topgrad.broadcastable)
        topgrad = gtype.filter_variable(topgrad)

        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')

        shape = as_tensor_variable(shape)
        broadcastable = [topgrad.broadcastable[1],
                         img.broadcastable[1],
                         False, False]
        output = img.type.clone(broadcastable=broadcastable)()
        return Apply(self, [img, topgrad, shape], [output]) 

def make_node(self, x, y):
        x, y = sparse.as_sparse_variable(x), tensor.as_tensor_variable(y)
        out_dtype = scalar.upcast(x.type.dtype, y.type.dtype)
        if self.inplace:
            assert out_dtype == y.dtype

        indices, indptr, data = csm_indices(x), csm_indptr(x), csm_data(x)
        # We either use CSC or CSR depending on the format of input
        assert self.format == x.type.format
        # The magic number two here arises because L{scipy.sparse}
        # objects must be matrices (have dimension 2)
        assert y.type.ndim == 2
        out = tensor.TensorType(dtype=out_dtype,
                                broadcastable=y.type.broadcastable)()
        return gof.Apply(self,
                         [data, indices, indptr, y],
                         [out]) 

def make_node(self, x, y, ilist):
        x_ = T.as_tensor_variable(x)
        y_ = T.as_tensor_variable(y)
        ilist_ = T.as_tensor_variable(ilist)

        #if ilist_.type.dtype[:3] not in ('int', 'uin'):
        #    raise TypeError('index must be integers')
        if ilist_.type.ndim != 1:
            raise TypeError('index must be vector')
        if x_.type.ndim == 0:
            raise TypeError('cannot index into a scalar')
        if y_.type.ndim > x_.type.ndim:
            if self.set_instead_of_inc:
                opname = 'set'
            else:
                opname = 'increment'
            raise TypeError(
                'cannot %s x subtensor with ndim=%s'
                ' by y with ndim=%s' % (
                    opname, x_.type.ndim, y_.type.ndim))

        return theano.gof.Apply(self, [x_, y_, ilist_], [x_.type()]) 

def make_node(self, x, ilist):
        x_ = as_cuda_ndarray_variable(x)
        ilist_ = gpu_contiguous(T.cast(ilist, dtype=config.floatX)) # T.as_tensor_variable(ilist)
        #if ilist_.type.dtype[:3] not in ('int', 'uin'):
        #    raise TypeError('index must be integers')
        if ilist_.type.ndim != 1:
            raise TypeError('index must be vector')
        if x_.type.ndim == 0:
            raise TypeError('cannot index into a scalar')

        # # c code suppose it is int64
        # if x.ndim in [1, 2, 3] and ilist_.dtype in [
        #     'int8', 'int16', 'int32', 'uint8', 'uint16', 'uint32']:
        #     ilist_ = tensor.cast(ilist_, 'int64')

        bcast = (ilist_.broadcastable[0],) + x_.broadcastable[1:]
        return theano.gof.Apply(self, [x_, ilist_],
                                [CudaNdarrayType(dtype=x.dtype,
                                                 broadcastable=bcast)()]) 

def test_shape_Constant_tensor(self):
        """
        Tests correlation where the {image,filter}_shape is a Constant tensor.
        """
        as_t = T.as_tensor_variable
        border_modes = ['valid', 'full', 'half', (1, 1), (2, 1), (1, 2), (3, 3), 1]

        for border_mode in border_modes:
            self.validate((as_t(3), as_t(2), as_t(7), as_t(5)),
                          (5, 2, 2, 3), border_mode)
            self.validate(as_t([3, 2, 7, 5]), (5, 2, 2, 3), border_mode)
            self.validate(as_t((3, 2, 7, 5)), (5, 2, 2, 3), border_mode)
            self.validate((3, 2, 7, 5), (as_t(5), as_t(2), as_t(2),
                          as_t(3)), 'valid')
            self.validate((3, 2, 7, 5), as_t([5, 2, 2, 3]), border_mode)
            self.validate(as_t([3, 2, 7, 5]), as_t([5, 2, 2, 3]), border_mode) 

def make_node(self, x, y, p_data, p_ind, p_ptr, p_ncols):
        x = tensor.as_tensor_variable(x)
        y = tensor.as_tensor_variable(y)
        p_data = tensor.as_tensor_variable(p_data)
        p_ind = tensor.as_tensor_variable(p_ind)
        p_ptr = tensor.as_tensor_variable(p_ptr)
        p_ncols = tensor.as_tensor_variable(p_ncols)

        assert p_ncols.dtype == 'int32'

        dtype_out = scalar.upcast(x.type.dtype, y.type.dtype,
                                  p_data.type.dtype)
        dot_out = scalar.upcast(x.type.dtype, y.type.dtype)

        # We call blas ?dot function that take only param of the same type
        x = tensor.cast(x, dot_out)
        y = tensor.cast(y, dot_out)

        return gof.Apply(self, [x, y, p_data, p_ind, p_ptr, p_ncols], [
            tensor.tensor(dtype=dtype_out, broadcastable=(False,)),
            tensor.tensor(dtype=p_ind.type.dtype, broadcastable=(False,)),
            tensor.tensor(dtype=p_ptr.type.dtype, broadcastable=(False,))
        ]) 

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) 

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) 

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) 

def as_sparse_or_tensor_variable(x, name=None):
    """
    Same as `as_sparse_variable` but if we can't make a
    sparse variable, we try to make a tensor variable.

    Parameters
    ----------
    x
        A sparse matrix.

    Returns
    -------
    SparseVariable or TensorVariable version of `x`

    """

    try:
        return as_sparse_variable(x, name)
    except (ValueError, TypeError):
        return theano.tensor.as_tensor_variable(x, name) 

def test_neibs_bad_shape(self):
        shape = (2, 3, 10, 10)
        for dtype in self.dtypes:
            images = shared(numpy.arange(
                numpy.prod(shape), dtype=dtype
                ).reshape(shape))

            for neib_shape in [(3, 2), (2, 3)]:
                neib_shape = T.as_tensor_variable(neib_shape)
                f = function([], images2neibs(images, neib_shape),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

                # Test that ignore border work in that case.
                f = function([],
                             images2neibs(images, neib_shape,
                                          mode='ignore_borders'),
                             mode=self.mode)
                assert self.op in [type(node.op)
                                   for node in f.maker.fgraph.toposort()]
                f() 

def make_node(self, x, y):
        x, y = as_sparse_variable(x), tensor.as_tensor_variable(y)

        assert x.format in ["csr", "csc"]

        # upcast the tensor. Is the cast of sparse done implemented?
        dtype = scalar.upcast(x.type.dtype, y.type.dtype)

        # The magic number two here arises because L{scipy.sparse}
        # objects must be matrices (have dimension 2)
        # Broadcasting of the sparse matrix is not supported.
        # We support nd == 0 used by grad of SpSum()
        assert y.type.ndim in [0, 2]
        out = SparseType(dtype=dtype,
                         format=x.type.format)()
        return gof.Apply(self, [x, y], [out]) 

def make_node(self, img, topgrad, shape=None):
        img = as_tensor_variable(img)
        topgrad = as_tensor_variable(topgrad)
        if img.type.ndim != 4:
            raise TypeError('img must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if self.subsample != (1, 1) or self.border_mode == "half":
            if shape is None:
                raise ValueError('shape must be given if subsample != (1, 1)'
                                 ' or border_mode == "half"')
            height_width = [as_tensor_variable(shape[0]).astype('int64'), as_tensor_variable(shape[1]).astype('int64')]
        else:
            height_width = []

        broadcastable = [topgrad.type.broadcastable[1], img.type.broadcastable[1],
                         False, False]
        dtype = img.type.dtype
        return Apply(self, [img, topgrad] + height_width,
                     [TensorType(dtype, broadcastable)()]) 

def make_node(self, kern, topgrad, shape=None):
        kern = as_tensor_variable(kern)
        topgrad = as_tensor_variable(topgrad)
        if kern.type.ndim != 4:
            raise TypeError('kern must be 4D tensor')
        if topgrad.type.ndim != 4:
            raise TypeError('topgrad must be 4D tensor')
        if self.subsample != (1, 1) and shape is None:
            raise ValueError('shape must be given if subsample != (1, 1)')
        height_width = [as_tensor_variable(shape[0]).astype('int64'), as_tensor_variable(shape[1]).astype('int64')] if self.subsample != (1, 1) else []

        broadcastable = [topgrad.type.broadcastable[0], kern.type.broadcastable[1],
                         False, False]
        dtype = kern.type.dtype
        return Apply(self, [kern, topgrad] + height_width,
                     [TensorType(dtype, broadcastable)()]) 

def conv2d(input,
           filters,
           input_shape=None,
           filter_shape=None,
           border_mode='valid',
           subsample=(1, 1),
           filter_flip=True):
    """This function will build the symbolic graph for convolving a mini-batch of a
    stack of 2D inputs with a set of 2D filters. The implementation is modelled
    after Convolutional Neural Networks (CNN).

    Refer to :func:`nnet.conv2d <theano.tensor.nnet.conv2d>` for a more detailed documentation.
    """

    input = as_tensor_variable(input)
    filters = as_tensor_variable(filters)
    conv_op = AbstractConv2d(imshp=input_shape,
                             kshp=filter_shape,
                             border_mode=border_mode,
                             subsample=subsample,
                             filter_flip=filter_flip)
    return conv_op(input, filters) 

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) 

def make_node(self, x, index, gz):
        x = as_sparse_variable(x)
        gz = as_sparse_variable(gz)

        assert x.format in ["csr", "csc"]
        assert gz.format in ["csr", "csc"]

        ind = tensor.as_tensor_variable(index)
        assert ind.ndim == 1
        assert "int" in ind.dtype

        scipy_ver = [int(n) for n in scipy.__version__.split('.')[:2]]

        if not scipy_ver >= [0, 13]:
            raise NotImplementedError("Scipy version is to old")

        return gof.Apply(self, [x, ind, gz], [x.type()]) 

def test_slice_canonical_form_2(self):
        start = tensor.iscalar('b')
        step = tensor.iscalar('s')
        length = tensor.iscalar('l')
        cnf = get_canonical_form_slice(slice(start, None, step), length)
        f = self.function([start, 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 step in [-6, -3, -1, 2, 5]:
                out = f(start, step, length)
                t_out = a[out[0]:out[1]:out[2]][::out[3]]
                v_out = a[start:None:step]
                assert numpy.all(t_out == v_out)
                assert numpy.all(t_out.shape == v_out.shape) 

def make_node(self, x):
        x = tensor.as_tensor_variable(x)
        if x.ndim > 2:
            raise TypeError(
                "Theano does not have sparse tensor types with more "
                "than 2 dimensions, but %s.ndim = %i" % (x, x.ndim))
        elif x.ndim == 1:
            x = x.dimshuffle('x', 0)
        elif x.ndim == 0:
            x = x.dimshuffle('x', 'x')
        else:
            assert x.ndim == 2

        return gof.Apply(self,
                         [x],
                         [SparseType(dtype=x.type.dtype,
                                     format=self.format)()]) 

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) 

def test_destroy_map0(self):
        """test that only first input can be overwritten"""
        Z = as_tensor_variable(self.rand(2, 2))
        try:
            gemm_inplace(Z, 1.0, Z, Z, 1.0)
        except InconsistencyError as e:
            if exc_message(e) == Gemm.E_z_uniq:
                return
        self.fail() 

def b(self, bval):
        return tensor.as_tensor_variable(numpy.asarray(bval, dtype=self.dtype)) 

def test_neibs_bad_shape_wrap_centered(self):
        shape = (2, 3, 10, 10)

        for dtype in self.dtypes:
            images = shared(numpy.arange(
                numpy.prod(shape), dtype=dtype
                ).reshape(shape))

            for neib_shape in [(3, 2), (2, 3)]:
                neib_shape = T.as_tensor_variable(neib_shape)

                f = function([], images2neibs(images, neib_shape,
                                              mode="wrap_centered"),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

            for shape in [(2, 3, 2, 3), (2, 3, 3, 2)]:
                images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
                neib_shape = T.as_tensor_variable((3, 3))
                f = function([], images2neibs(images, neib_shape,
                                              mode="wrap_centered"),
                             mode=self.mode)
                self.assertRaises(TypeError, f)

            # Test a valid shapes
            shape = (2, 3, 3, 3)
            images = shared(numpy.arange(numpy.prod(shape)).reshape(shape))
            neib_shape = T.as_tensor_variable((3, 3))

            f = function([],
                         images2neibs(images, neib_shape, mode="wrap_centered"),
                         mode=self.mode)
            f() 

def cmp(self, z_, a_, x_, y_, b_):
        for dtype in ['float32', 'float64', 'complex64', 'complex128']:
            z = numpy.asarray(z_, dtype=dtype)
            a = numpy.asarray(a_, dtype=dtype)
            x = numpy.asarray(x_, dtype=dtype)
            y = numpy.asarray(y_, dtype=dtype)
            b = numpy.asarray(b_, dtype=dtype)

            def cmp_linker(z, a, x, y, b, l):
                z, a, x, y, b = [numpy.asarray(p) for p in (z, a, x, y, b)]
                z_orig = z.copy()
                tz, ta, tx, ty, tb = [as_tensor_variable(p).type()
                                      for p in (z, a, x, y, b)]

                f = inplace_func([tz, ta, tx, ty, tb],
                                 gemm_inplace(tz, ta, tx, ty, tb),
                                 mode=compile.Mode(optimizer=None, linker=l))
                new_z = f(z, a, x, y, b)
                z_after = self._gemm(z_orig, a, x, y, b)

                # print z_orig, z_after, z, type(z_orig), type(z_after), type(z)
                unittest_tools.assert_allclose(z_after, z)
                if a == 0.0 and b == 1.0:
                    return
                elif z_orig.size == 0:
                    self.assertTrue(z.size == 0)
                else:
                    self.assertFalse(numpy.all(z_orig == z))

            cmp_linker(copy(z), a, x, y, b, 'c|py')
            cmp_linker(copy(z), a, x, y, b, 'py')
            if (config.blas.ldflags and not dtype.startswith("complex")
                and theano.config.cxx):
                # If blas.ldflags is equal to '', the C code will not
                # be generated
                cmp_linker(copy(z), a, x, y, b, 'c') 

def make_node(self, x, gz, dummy=None):
        # make_node should only be called by the grad function of
        # Pool, so these asserts should not fail.
        assert isinstance(x, Variable) and x.ndim == 4
        assert isinstance(gz, Variable) and gz.ndim == 4
        x = tensor.as_tensor_variable(x)
        gz = tensor.as_tensor_variable(gz)

        return Apply(self, [x, gz], [x.type()]) 

def make_node(self, x, maxout, gz):
        # make_node should only be called by the grad function of
        # Pool, so these asserts should not fail.
        assert isinstance(x, Variable) and x.ndim == 4
        assert isinstance(maxout, Variable) and maxout.ndim == 4
        assert isinstance(gz, Variable) and gz.ndim == 4
        x = tensor.as_tensor_variable(x)
        maxout = tensor.as_tensor_variable(maxout)
        gz = tensor.as_tensor_variable(gz)

        return Apply(self, [x, maxout, gz], [x.type()]) 

def make_node(self, x):
        if x.type.ndim != 4:
            raise TypeError()
        # TODO: consider restricting the dtype?
        x = tensor.as_tensor_variable(x)
        # If the input shape are broadcastable we can have 0 in the output shape
        broad = x.broadcastable[:2] + (False, False)
        out = tensor.TensorType(x.dtype, broad)
        return gof.Apply(self, [x], [out()]) 

def speed_neibs_wrap_centered(self):
        shape = (100, 40, 18, 18)
        images = shared(numpy.arange(numpy.prod(shape),
                                     dtype='float32').reshape(shape))
        neib_shape = T.as_tensor_variable((3, 3))

        f = function([],
                     images2neibs(images, neib_shape, mode="wrap_centered"),
                     mode=self.mode)

        for i in range(1000):
            f()