Python tensor.as_tensor_variable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 as_tensor_variable [as 别名]
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)()]) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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]) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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)()]) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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)()]) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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]) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [as 别名]
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() 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [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 as_tensor_variable [as 别名]
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) 

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import as_tensor_variable [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 as_tensor_variable [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 as_tensor_variable [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 as_tensor_variable [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)