Python gof.Apply方法代码示例

# 需要导入模块: from theano import gof [as 别名]
# 或者: from theano.gof import Apply [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 gof [as 别名]
# 或者: from theano.gof import Apply [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 gof [as 别名]
# 或者: from theano.gof import Apply [as 别名]
def c_code_cache_version_apply(self, node):
        version = [12]  # the version corresponding to the c code in this Op

        # now we insert versions for the ops on which we depend...
        scalar_node = Apply(
            self.scalar_op,
            [get_scalar_type(dtype=input.type.dtype).make_variable()
             for input in node.inputs],
            [get_scalar_type(dtype=output.type.dtype).make_variable()
             for output in node.outputs])
        version.append(self.scalar_op.c_code_cache_version_apply(scalar_node))
        for i in node.inputs + node.outputs:
            version.append(get_scalar_type(dtype=i.type.dtype).c_code_cache_version())
        version.append(('openmp', self.openmp))
        if all(version):
            return tuple(version)
        else:
            return () 

# 需要导入模块: from theano import gof [as 别名]
# 或者: from theano.gof import Apply [as 别名]
def make_node(self, x, b, y_idx):
        x = tensor.as_tensor_variable(x)
        b = tensor.as_tensor_variable(b)
        y_idx = tensor.as_tensor_variable(y_idx)
        if x.type.ndim != 2 \
                or x.type.dtype not in tensor.float_dtypes:
            raise ValueError('x must be 2-d tensor of floats', x.type)
        if b.type.ndim != 1 \
                or x.type.dtype not in tensor.float_dtypes:
            raise ValueError('b must be 1-d tensor of floats', b.type)
        if y_idx.type.ndim != 1 \
                or y_idx.type.dtype not in tensor.discrete_dtypes:
            raise ValueError('y_idx must be 1-d tensor of [u]ints', y_idx.type)

#       TODO: Is this correct? It used to be y, not y_idx
        nll = tensor.TensorType(x.type.dtype,
                                y_idx.type.broadcastable).make_variable()
#        nll = TensorType(x.dtype, y.broadcastable)
        sm = x.type()
        am = y_idx.type()
        return Apply(self, [x, b, y_idx], [nll, sm, am]) 

# 需要导入模块: from theano import gof [as 别名]
# 或者: from theano.gof import Apply [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 gof [as 别名]
# 或者: from theano.gof import Apply [as 别名]
def make_node(self, a, val, offset):
        a = tensor.as_tensor_variable(a)
        val = tensor.as_tensor_variable(val)
        offset = tensor.as_tensor_variable(offset)
        if a.ndim != 2:
            raise TypeError('%s: first parameter must have exactly'
                            ' two dimensions' % self.__class__.__name__)
        elif val.ndim != 0:
            raise TypeError('%s: second parameter must be a scalar'
                            % self.__class__.__name__)
        elif offset.ndim != 0:
            raise TypeError('%s: third parameter must be a scalar'
                            % self.__class__.__name__)
        val = tensor.cast(val, dtype=scalar.upcast(a.dtype, val.dtype))
        if val.dtype != a.dtype:
            raise TypeError('%s: type of second parameter must be the same'
                            ' as the first\'s' % self.__class__.__name__)
        elif offset.dtype[:3] != 'int':
            raise TypeError('%s: type of third parameter must be as integer'
                            ' use theano.tensor.cast( input, \'int32/int64\')'
                            % self.__class__.__name__)

        return gof.Apply(self, [a, val, offset], [a.type()]) 

# 需要导入模块: from theano import gof [as 别名]
# 或者: from theano.gof import Apply [as 别名]
def make_node(self, x):
        x = as_sparse_variable(x)
        return gof.Apply(self, [x], [x.type()]) 

# 需要导入模块: from theano import gof [as 别名]
# 或者: from theano.gof import Apply [as 别名]
def make_node(self, x):
            x = as_sparse_variable(x)
            return gof.Apply(self, [x], [x.type()]) 

# 需要导入模块: from theano import gof [as 别名]
# 或者: from theano.gof import Apply [as 别名]
def as_sparse_variable(x, name=None):
    """
    Wrapper around SparseVariable constructor to construct
    a Variable with a sparse matrix with the same dtype and
    format.

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

    Returns
    -------
    object
        SparseVariable version of `x`.

    """

    # TODO
    # Verify that sp is sufficiently sparse, and raise a
    # warning if it is not

    if isinstance(x, gof.Apply):
        if len(x.outputs) != 1:
            raise ValueError("It is ambiguous which output of a "
                             "multi-output Op has to be fetched.", x)
        else:
            x = x.outputs[0]
    if isinstance(x, gof.Variable):
        if not isinstance(x.type, SparseType):
            raise TypeError("Variable type field must be a SparseType.", x,
                            x.type)
        return x
    try:
        return constant(x, name=name)
    except TypeError:
        raise TypeError("Cannot convert %s to SparseType" % x, type(x))