Python theano.gof.Apply() 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, 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()]) 

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 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, index):
        x = as_sparse_variable(x)
        assert x.format in ["csr", "csc"]
        assert len(index) == 2

        input_op = [x]

        for ind in index:

            if isinstance(ind, slice):
                raise Exception("GetItemScalar called with a slice as index!")

            # in case of indexing using int instead of theano variable
            elif isinstance(ind, integer_types):
                ind = theano.tensor.constant(ind)
                input_op += [ind]

            # in case of indexing using theano variable
            elif ind.ndim == 0:
                input_op += [ind]
            else:
                raise NotImplemented()

        return gof.Apply(self, input_op, [tensor.scalar(dtype=x.dtype)]) 

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

        shape = as_tensor_variable(shape)
        broadcastable = [topgrad.type.broadcastable[0],
                         kern.type.broadcastable[1],
                         False, False]
        output = kern.type.clone(broadcastable=broadcastable)()
        return Apply(self, [kern, topgrad, shape], [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, 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, 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]) 

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 c_code_cache_version_apply(self, node):
        version = (6,)  # 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())
        if all(version):
            return tuple(version)
        else:
            return () 

def make_node(self, x, y):
        # NOTE
        # Because of trickiness of implementing,
        # we assume that the left argument x is a
        # SparseVariable (not dense)

        if x.type.dtype != y.type.dtype:
            raise NotImplementedError()

        if not _is_sparse_variable(x):
            raise TypeError(x)

        # These are the conversions performed by scipy.sparse.dot
        if x.type.format == "csc" or x.type.format == "coo":
            myformat = "csc"
        elif x.type.format == "csr":
            myformat = "csr"
        else:
            raise NotImplementedError()

        inputs = [x, y]  # Need to convert? e.g. assparse
        outputs = [SparseType(dtype=x.type.dtype, format=myformat)()]
        return gof.Apply(self, inputs, outputs) 

def make_node(self, a, b):

        a = as_sparse_variable(a)
        assert a.format in ["csr", "csc", "bsr"]

        if not _is_sparse_variable(a):
            raise TypeError('First argument must be of type SparseVariable '
                            'or SparseConstant')
        dtype_out = scalar.upcast(a.type.dtype, b.type.dtype)
        if b.type.ndim != 2:
            raise NotImplementedError('non-matrix b')

        if _is_sparse_variable(b):
            return gof.Apply(self, [a, b],
                             [SparseType(a.type.format, dtype_out)()])
        else:
            return gof.Apply(self, [a, b],
                             [tensor.tensor(dtype_out,
                                            (False, b.type.broadcastable[1]))]) 

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 () 

def make_node(self, dy, sm, y_idx, **kwargs):
        dy = tensor.as_tensor_variable(dy)
        sm = tensor.as_tensor_variable(sm)
        y_idx = tensor.as_tensor_variable(y_idx)
        if (dy.type.ndim > 1 or
                dy.type.dtype not in tensor.float_dtypes):
            raise ValueError('dy must be {0,1}-d tensor of floats', dy.type)
        if (sm.type.ndim != 2 or
                sm.type.dtype not in tensor.float_dtypes):
            raise ValueError('sm must be 2-d tensor of floats', sm.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)
        return Apply(self, [dy, sm, y_idx], [sm.type()]) 

def make_node(self, x):
        x = basic.as_tensor_variable(x)
        outputs = [basic.TensorType(broadcastable=[False], dtype=x.dtype)()]
        typ = basic.TensorType(broadcastable=[False], dtype='int64')
        if self.return_index:
            outputs.append(typ())
        if self.return_inverse:
            outputs.append(typ())
        if self.return_counts:
            outputs.append(typ())
        return theano.Apply(self, [x], outputs) 

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):
        x = tensor.as_tensor_variable(x)
        if x.type.ndim not in (1, 2) \
                or x.type.dtype not in tensor.float_dtypes:
            raise ValueError('x must be 1-d or 2-d tensor of floats. Got %s' %
                             x.type)
        if x.ndim == 1:
            x = tensor.shape_padleft(x, n_ones=1)

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

def make_node(self, path):
        if isinstance(path, str):
            path = Constant(Generic(), path)
        return gof.Apply(self, [path], [tensor(self.dtype,
                                        broadcastable=self.broadcastable)]) 

def make_node(self, x):
        x = tensor.as_tensor_variable(x)
        if x.type.ndim not in (1, 2) \
                or x.type.dtype not in tensor.float_dtypes:
            raise ValueError('x must be 1-d or 2-d tensor of floats. Got %s' %
                             x.type)
        if x.ndim == 1:
            x = tensor.shape_padleft(x, n_ones=1)

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

def make_node(self, x, b):
        x = tensor.as_tensor_variable(x)
        b = tensor.as_tensor_variable(b)
        if x.type.ndim != 2 \
                or x.type.dtype not in tensor.float_dtypes:
            raise ValueError('x must be 2-d tensor of floats')
        if b.type.ndim != 1 \
                or x.type.dtype not in tensor.float_dtypes:
            raise ValueError('b must be 1-d tensor of floats')

        sm = x.type()
        return Apply(self, [x, b], [sm]) 

def make_node(self, inputs, kerns):
        # TODO: find a way to make ConvOp work for N-D (after NIPS09)
        """
        Parameters
        ----------
        inputs
            4 dim: batches x stacksize x rows x cols.
        kerns
            4 dim: nkern x stackidx x rows x cols.

        """
        _inputs = as_tensor_variable(inputs)
        _kerns = as_tensor_variable(kerns)
        # TODO: lift this restriction by upcasting either inputs or kerns
        if _inputs.ndim != 4:
            raise TypeError('ConvOp (make_node) requires input be a 4D tensor;'
                            ' received "%s" (%i dims)' %
                            (inputs, _inputs.ndim))
        if _kerns.ndim != 4:
            raise TypeError('make_node requires 4D tensor of kernels')
        if _inputs.type.dtype != _kerns.type.dtype:
            raise NotImplementedError(
                "The image and the kernel must have the same type."
                "inputs(%s), kerns(%s)" % (_inputs.dtype, _kerns.dtype))
        bcastable23 = [self.outshp[0] == 1, self.outshp[1] == 1]
        output = theano.tensor.tensor(dtype=_inputs.type.dtype,
                                      broadcastable=[_inputs.broadcastable[0],
                                                     _kerns.broadcastable[0]] +
                                      bcastable23)

        return Apply(self, [_inputs, _kerns], [output]) 

def make_node(self, request, data):
        return gof.Apply(self, [request, data],
                               [tensor(data.dtype,
                                       broadcastable=data.broadcastable)]) 

def make_node(self, data):
        return gof.Apply(self, [data],
                               [theano.Variable(Generic()), data.type()]) 

def make_node(self, request, data):
        return gof.Apply(self, [request, data],
                               [theano.Variable(Generic())]) 

def make_node(self, input):
        input = as_tensor_variable(input)

        if self.axis is not None:
            for axis in self.axis:
                if (axis >= input.type.ndim or
                        (axis < 0 and abs(axis) > input.type.ndim)):
                    raise ValueError((
                        'Not enough dimensions on %s to reduce on axis %s'
                        % (input, axis)))
        input = as_tensor_variable(input)
        axis = self.axis
        if axis is None:
            axis = list(range(len(input.type.broadcastable)))
        if any(a < 0 for a in axis):
            axis2 = []
            for a in self.axis:
                if a < 0:
                    axis2.append(a + input.type.ndim)
                else:
                    axis2.append(a)
            assert len(axis) == len(axis2)
            axis = tuple(axis2)
            # We can't call self.__class__() as there is class that
            # inherit from CAReduce that don't have the same signature
            op = copy(self)
            op.set_ufunc(op.scalar_op)
            op.axis = axis
        else:
            op = self
        broadcastable = [x for i, x in enumerate(input.type.broadcastable)
                         if i not in axis]
        output = TensorType(dtype=self._output_dtype(input.type.dtype),
                            broadcastable=broadcastable)()
        return Apply(op, [input], [output]) 

def make_node(self, slc, stop=None, step=None):
        # We need to accept and handle in make_node inputs the node
        # inputs to allow redoing a new op elsewhere in the graph by
        # optimization.
        if isinstance(slc, slice):
            assert stop is None
            assert step is None
            inp = [slc.start, slc.stop, slc.step]
        else:
            inp = [slc, stop, step]
        return Apply(self,
                     list(map(as_int_none_variable, inp)),
                     [slicetype()]) 

def make_node(self, mat):
        # check type of input
        x = tensor.as_tensor_variable(mat)
        if not mat.type.broadcastable == (False, False):
            raise TypeError("Expected a matrix as input")
        y = tensor.as_tensor_variable(self.val)
        assert y.ndim == 0
        if x.type.dtype != y.type.dtype:
            TypeError(
                "the value to prepend don't have the same type as the matrix")

        node = Apply(op=self, inputs=[mat], outputs=[mat.type()])
        return node