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Python tensor.TensorType方法代码示例

本文整理汇总了Python中theano.tensor.TensorType方法的典型用法代码示例。如果您正苦于以下问题:Python tensor.TensorType方法的具体用法?Python tensor.TensorType怎么用?Python tensor.TensorType使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在theano.tensor的用法示例。


在下文中一共展示了tensor.TensorType方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: placeholder

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def placeholder(shape=None, ndim=None, dtype=None, sparse=False, name=None):
    '''Instantiate an input data placeholder variable.
    '''
    if dtype is None:
        dtype = floatx()
    if shape is None and ndim is None:
        raise ValueError('Specify either a shape or ndim value.')
    if shape is not None:
        ndim = len(shape)
    else:
        shape = tuple([None for _ in range(ndim)])

    broadcast = (False,) * ndim
    if sparse:
        _assert_sparse_module()
        x = th_sparse_module.csr_matrix(name=name, dtype=dtype)
    else:
        x = T.TensorType(dtype, broadcast)(name)
    x._keras_shape = shape
    x._uses_learning_phase = False
    return x 
开发者ID:lingluodlut,项目名称:Att-ChemdNER,代码行数:23,代码来源:theano_backend.py

示例2: make_node

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [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]) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:19,代码来源:opt.py

示例3: test_op_sd

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def test_op_sd(self):
        for format in sparse.sparse_formats:
            for dtype in sparse.all_dtypes:
                variable, data = sparse_random_inputs(format,
                                                      shape=(10, 10),
                                                      out_dtype=dtype,
                                                      n=2,
                                                      p=0.1)
                variable[1] = tensor.TensorType(dtype=dtype,
                                                broadcastable=(False, False))()
                data[1] = data[1].toarray()

                f = theano.function(variable, self.op(*variable))

                tested = f(*data)
                expected = numpy.dot(data[0].toarray(), data[1])

                assert tested.format == format
                assert tested.dtype == expected.dtype
                tested = tested.toarray()
                utt.assert_allclose(tested, expected) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:23,代码来源:test_basic.py

示例4: _is_sparse_variable

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def _is_sparse_variable(x):
    """

    Returns
    -------
    boolean
        True iff x is a L{SparseVariable} (and not a L{tensor.TensorType},
        for instance).

    """
    if not isinstance(x, gof.Variable):
        raise NotImplementedError("this function should only be called on "
                                  "*variables* (of type sparse.SparseType "
                                  "or tensor.TensorType, for instance), not ",
                                  x)
    return isinstance(x.type, SparseType) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:18,代码来源:basic.py

示例5: make_node

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [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)()]) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:22,代码来源:corr.py

示例6: local_abstractconv_gemm

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def local_abstractconv_gemm(node):
    if theano.config.cxx == "" or not theano.config.blas.ldflags:
        return
    if not isinstance(node.op, AbstractConv2d):
        return None
    img, kern = node.inputs
    if not isinstance(img.type, TensorType) or \
       not isinstance(kern.type, TensorType):
        return None

    # need to flip the kernel if necessary
    if node.op.filter_flip:
        kern = kern[:, :, ::-1, ::-1]
    rval = CorrMM(border_mode=node.op.border_mode,
                  subsample=node.op.subsample)(img, kern)
    copy_stack_trace(node.outputs[0], rval)

    return [rval] 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:20,代码来源:opt.py

示例7: local_abstractconv_gradweight_gemm

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def local_abstractconv_gradweight_gemm(node):
    if theano.config.cxx == "" or not theano.config.blas.ldflags:
        return
    if not isinstance(node.op, AbstractConv2d_gradWeights):
        return None
    img, topgrad, shape = node.inputs
    if not isinstance(img.type, TensorType) or \
       not isinstance(topgrad.type, TensorType):
        return None

    rval = CorrMM_gradWeights(border_mode=node.op.border_mode,
                              subsample=node.op.subsample)(img, topgrad, shape)
    copy_stack_trace(node.outputs[0], rval)

    # need to flip the kernel if necessary
    if node.op.filter_flip:
        rval = rval[:, :, ::-1, ::-1]
    rval = theano.tensor.patternbroadcast(rval, node.outputs[0].broadcastable)
    copy_stack_trace(node.outputs[0], rval)

    return [rval] 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:23,代码来源:opt.py

示例8: local_conv2d_cpu

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def local_conv2d_cpu(node):

    if not isinstance(node.op, AbstractConv2d):
        return None

    img, kern = node.inputs
    if ((not isinstance(img.type, TensorType) or
         not isinstance(kern.type, TensorType))):
        return None
    if node.op.border_mode not in ['full', 'valid']:
        return None
    if not node.op.filter_flip:
        # Not tested yet
        return None

    rval = conv2d(img, kern,
                  node.op.imshp, node.op.kshp,
                  border_mode=node.op.border_mode,
                  subsample=node.op.subsample)

    copy_stack_trace(node.outputs[0], rval)
    return [rval] 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:24,代码来源:opt.py

示例9: test_infer_shape

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def test_infer_shape(self):

        for s_left, s_right in [((5, 6), (5, 6)),
                           ((5, 6), (5, 1)),
                           ((5, 6), (1, 6)),
                           ((5, 1), (5, 6)),
                           ((1, 6), (5, 6)),
                           ((2, 3, 4, 5), (2, 3, 4, 5)),
                           ((2, 3, 4, 5), (2, 3, 1, 5)),
                            ((2, 3, 4, 5), (1, 3, 4, 5)),
                            ((2, 1, 4, 5), (2, 3, 4, 5)),
                            ((2, 3, 4, 1), (2, 3, 4, 5))]:
            dtype = theano.config.floatX
            t_left = TensorType(dtype, [(entry == 1) for entry in s_left])()
            t_right = TensorType(dtype, [(entry == 1) for entry in s_right])()
            t_left_val = numpy.zeros(s_left, dtype=dtype)
            t_right_val = numpy.zeros(s_right, dtype=dtype)
            self._compile_and_check([t_left, t_right],
                            [Elemwise(scalar.add)(t_left, t_right)],
                            [t_left_val, t_right_val], Elemwise) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:22,代码来源:test_elemwise.py

示例10: __init__

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def __init__(self, name, shared=tensor._shared,
                 sub=tensor.Subtensor,
                 inc_sub=tensor.IncSubtensor,
                 adv_sub1=tensor.AdvancedSubtensor1,
                 adv_incsub1=tensor.AdvancedIncSubtensor1,
                 mode=None,
                 dtype=theano.config.floatX,
                 type=tensor.TensorType,
                 ignore_topo=DeepCopyOp):
        self.shared = shared
        self.sub = sub
        self.inc_sub = inc_sub
        self.adv_sub1 = adv_sub1
        self.adv_incsub1 = adv_incsub1
        if mode is None:
            mode = theano.compile.mode.get_default_mode()
        self.mode = mode
        self.dtype = dtype
        self.type = type
        self.ignore_topo = ignore_topo
        self.fast_compile = theano.config.mode == 'FAST_COMPILE'
        self.ops = (sub, inc_sub, adv_sub1, adv_incsub1)
        return super(T_subtensor, self).__init__(name) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:25,代码来源:test_subtensor.py

示例11: uniform

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def uniform(random_state, size=None, low=0.0, high=1.0, ndim=None, dtype=None):
    """
    Sample from a uniform distribution between low and high.

    If the size argument is ambiguous on the number of dimensions, ndim
    may be a plain integer to supplement the missing information.

    If size is None, the output shape will be determined by the shapes
    of low and high.

    If dtype is not specified, it will be inferred from the dtype of
    low and high, but will be at least as precise as floatX.

    """
    low = tensor.as_tensor_variable(low)
    high = tensor.as_tensor_variable(high)
    if dtype is None:
        dtype = tensor.scal.upcast(theano.config.floatX, low.dtype, high.dtype)
    ndim, size, bcast = _infer_ndim_bcast(ndim, size, low, high)
    op = RandomFunction('uniform',
                        tensor.TensorType(dtype=dtype, broadcastable=bcast))
    return op(random_state, size, low, high) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:24,代码来源:raw_random.py

示例12: normal

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def normal(random_state, size=None, avg=0.0, std=1.0, ndim=None, dtype=None):
    """
    Sample from a normal distribution centered on avg with
    the specified standard deviation (std).

    If the size argument is ambiguous on the number of dimensions, ndim
    may be a plain integer to supplement the missing information.

    If size is None, the output shape will be determined by the shapes
    of avg and std.

    If dtype is not specified, it will be inferred from the dtype of
    avg and std, but will be at least as precise as floatX.

    """
    avg = tensor.as_tensor_variable(avg)
    std = tensor.as_tensor_variable(std)
    if dtype is None:
        dtype = tensor.scal.upcast(theano.config.floatX, avg.dtype, std.dtype)
    ndim, size, bcast = _infer_ndim_bcast(ndim, size, avg, std)
    op = RandomFunction('normal',
                        tensor.TensorType(dtype=dtype, broadcastable=bcast))
    return op(random_state, size, avg, std) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:25,代码来源:raw_random.py

示例13: test_ctors

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def test_ctors(self):

        if theano.configdefaults.python_int_bitwidth() == 32:
            assert shared(7).type == theano.tensor.iscalar, shared(7).type
        else:
            assert shared(7).type == theano.tensor.lscalar, shared(7).type
        assert shared(7.0).type == theano.tensor.dscalar
        assert shared(numpy.float32(7)).type == theano.tensor.fscalar

        # test tensor constructor
        b = shared(numpy.zeros((5, 5), dtype='int32'))
        assert b.type == TensorType('int32', broadcastable=[False, False])
        b = shared(numpy.random.rand(4, 5))
        assert b.type == TensorType('float64', broadcastable=[False, False])
        b = shared(numpy.random.rand(5, 1, 2))
        assert b.type == TensorType('float64', broadcastable=[False, False, False])

        assert shared([]).type == generic

        def badfunc():
            shared(7, bad_kw=False)
        self.assertRaises(TypeError, badfunc) 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:24,代码来源:test_shared.py

示例14: local_gpu_extract_diagonal

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def local_gpu_extract_diagonal(node):
    """
    extract_diagonal(host_from_gpu()) -> host_from_gpu(extract_diagonal)

    gpu_from_host(extract_diagonal) -> extract_diagonal(gpu_from_host)

    """
    if (isinstance(node.op, nlinalg.ExtractDiag) and
        isinstance(node.inputs[0].type,
                   theano.tensor.TensorType)):
        inp = node.inputs[0]
        if inp.owner and isinstance(inp.owner.op, HostFromGpu):
            return [host_from_gpu(nlinalg.extract_diag(
                as_cuda_ndarray_variable(inp)))]
    if isinstance(node.op, GpuFromHost):
        host_input = node.inputs[0]
        if (host_input.owner and
            isinstance(host_input.owner.op, nlinalg.ExtractDiag) and
            isinstance(host_input.owner.inputs[0].type,
                       theano.tensor.TensorType)):
            diag_node = host_input.owner
            return [nlinalg.extract_diag(
                as_cuda_ndarray_variable(diag_node.inputs[0]))]
    return False 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:26,代码来源:opt.py

示例15: test_maxpool

# 需要导入模块: from theano import tensor [as 别名]
# 或者: from theano.tensor import TensorType [as 别名]
def test_maxpool():
        """TODO: test the gpu version!!! """
        for d0, d1, r_true, r_false in [(4, 4, [[[[5, 7], [13, 15]]]], [[[[5, 7], [13, 15]]]]),
                                        (5, 5, [[[[6, 8], [ 16, 18], [ 21, 23]]]],
                                         [[[[6, 8, 9], [ 16, 18, 19], [ 21, 23, 24]]]])]:
            for border, ret in [(True, r_true), (False, r_false)]:
                ret = numpy.array(ret)
                a = tcn.blas.Pool((2, 2), border)
                dmatrix4 = tensor.TensorType("float32", (False, False, False, False))
                b = dmatrix4()
                f = pfunc([b], [a(b)], mode=mode_with_gpu)

                bval = numpy.arange(0, d0*d1).reshape(1, 1, d0, d1)
                r = f(bval)[0]
    #            print bval, bval.shape, border
                # print r, r.shape
                assert (ret == r).all() 
开发者ID:muhanzhang,项目名称:D-VAE,代码行数:19,代码来源:test_blas.py


注:本文中的theano.tensor.TensorType方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版权归原作者所有,传播和使用请参考对应项目的License;未经允许,请勿转载。