# reshape¶

paddle.fluid.layers.nn. reshape ( x, shape, actual_shape=None, act=None, inplace=False, name=None ) [source]
Alias_main

This operator changes the shape of `x` without changing its data.

The target shape can be given by `shape` or `actual_shape`. When `shape` and `actual_shape` are set at the same time, `actual_shape` has a higher priority than `shape` but at this time `shape` can only be an integer list or tuple, and `shape` still should be set correctly to guarantee shape inference in compile-time.

Some tricks exist when specifying the target shape.

1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1.

2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The index of 0s in shape can not exceed the dimension of x.

Here are some examples to explain it.

1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x’s data unchanged.

2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape specified is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x’s data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions.

3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x’s data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x.

Note:

The parameter `actual_shape` will be deprecated in the future and only use `shape` instead to represent the target shape.

Parameters
• x (Tensor) – An N-D Tensor. The data type is `float32`, `float64`, `int32` or `int64`.

• shape (list|tuple|Tensor) – Define the target shape. At most one dimension of the target shape can be -1. The data type is `int32` . If `shape` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If `shape` is an Tensor, it should be an 1-D Tensor .

• actual_shape (variable, optional) – An 1-D `Tensor` or `LoDTensor` . The data type is `int32` . If provided, reshape according to this given shape rather than `shape` specifying shape. That is to say `actual_shape` has a higher priority than `shape(list|tuple)` but not `shape(Tensor)`. This argument `actual_shape` will be removed in a future version. Instructions for updating: `actual_shape` will be removed in future versions and replaced by `shape`.

• act (str, optional) – The non-linear activation to be applied to the reshaped input. Default None.

• inplace (bool, optional) – If `inplace` is True, the input and output of `layers.reshape` are the same variable. Otherwise, the input and output of `layers.reshape` are different variable. Default False. Note that if `x` is more than one OPs’ input, `inplace` must be False.

• name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name .

Returns

A reshaped Tensor with the same data type as `x`. It is a new tensor variable if `inplace` is `False`, otherwise it is `x`. If `act` is None, return the reshaped tensor variable, otherwise return the activated tensor variable.

Return type

Tensor

Examples

```import paddle

# example 1:
# attr shape is a list which doesn't contain Tensors.
data_1 = fluid.data(
name='data_1', shape=[2, 4, 6], dtype='float32')
reshaped_1 = fluid.layers.reshape(
x=data_1, shape=[-1, 0, 3, 2])
# the shape of reshaped_1 is [2,4,3,2].

# example 2:
# attr shape is a list which contains Tensors.
data_2 = fluid.layers.fill_constant([2,25], "int32", 3)
dim = fluid.layers.fill_constant([1], "int32", 5)
reshaped_2 = fluid.layers.reshape(data_2, shape=[dim, 10])
# the shape of reshaped_2 is [5,10].

# example 3:
data_3 = fluid.data(
name="data_3", shape=[2,4,6], dtype='float32')
reshaped_3 = fluid.layers.reshape(x=data_3, shape=[6,8])
# the shape of reshaped_3 is [6,8].
```