then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
Args:
input (Variable): The input Tensor to be unsqueezed. It is a N-D Tensor of data types float32, float64, int32.
input (Variable): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64.
axes (int|list|tuple|Variable): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axes`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axes`` is an Variable, it should be an 1-D Tensor .
name (str|None): Name for this layer.
Returns:
Variable: Output unsqueezed Tensor, with data type being float32, float64, int32, int64.
Variable: Unsqueezed Tensor, with the same data type as input.
Given a tensor such that tensor with shape [3, 4, 5],
then Unsqueezed tensor with axes=[0, 4] has shape [1, 3, 4, 5, 1].
Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one
required argument axis, a dimension or list of dimensions that will be inserted.
Dimension indices in axis are as seen in the output tensor.
Args:
input (Variable): The input Tensor to be unsqueezed. It is a N-D Tensor of data types float32, float64, int32.
axes (int|list|tuple|Variable): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axes`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axes`` is an Variable, it should be an 1-D Tensor .
name (str|None): Name for this layer.
x (Tensor): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64.
axis (int|list|tuple|Tensor): Indicates the dimensions to be inserted. The data type is ``int32`` .
If ``axis`` is a list or tuple, the elements of it should be integers or Tensors with shape [1].
If ``axis`` is a Tensor, it should be an 1-D Tensor .
If ``axis`` is negative, ``axis = axis + ndim(x) + 1``.
name (str|None): Name for this layer. Please refer to :ref:`api_guide_Name`, Default None.
Returns:
Variable: Output unsqueezed Tensor, with data type being float32, float64, int32, int64.
Tensor: Unsqueezed Tensor with the same data type as input Tensor.
input (Variable): The input Tensor/LoDTensor to be added to the final result.
x (Variable): The first input Tensor/LoDTensor for matrix multiplication.
y (Variable): The second input Tensor/LoDTensor for matrix multiplication.
alpha (float): Coefficient of $x*y$.
beta (float): Coefficient of $input$.
alpha (float): Coefficient of $x*y$.
name (str, optional): Name of the output. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Default is None.
raiseValueError("The dimention of input, x, y should be 2 but receive input's shape: {}, x's shape: {}, y's shape: {}".format(input_shape,x_shape,y_shape))
ifinput_shape[0]!=x_shape[0]:
ifinput_shape[0]!=1:
raiseValueError("When x's dimension[0] is not equal with input's dimension[0], input's dimension[0] must be 1 but got {}".format(input_shape[0]))
ifinput_shape[1]!=y_shape[1]andinput_shape[1]!=1:
raiseValueError("When y's dimension[1] is not equal with input's dimension[1], input's dimension[1] must be 1 but got {}".format(input_shape[1]))
ifinput_shape[1]!=y_shape[1]:
ifinput_shape[1]!=1:
raiseValueError("When y's dimension[1] is not equal with input's dimension[1], input's dimension[1] must be 1 but got {}".format(input_shape[1]))
ifinput_shape[0]!=x_shape[0]andinput_shape[0]!=1:
raiseValueError("When x's dimension[0] is not equal with input's dimension[0], input's dimension[0] must be 1 but got {}".format(input_shape[0]))
ifx_shape[1]!=y_shape[0]:
raiseValueError("The input Variable x's width must be equal with Variable y' height. But received x's shape = {}, y's shape = {}.".format(x_shape,y_shape))
