深入浅出TensorFlow2函数——tf.reshape

分类目录：《深入浅出TensorFlow2函数》总目录

语法

tf.reshape(
    tensor, shape, name=None
)

参数

返回值

返回一个新的形状为

shape

的

tf.Tensor

且具有与

tensor

以同样的顺序和相同的值。

实例

输入：

t1 = [[1, 2, 3],
      [4, 5, 6]]
print(tf.shape(t1).numpy())        # [2 3]
t2 = tf.reshape(t1, [6])
t2        #<tf.Tensor: shape=(6,), dtype=int32, numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
tf.reshape(t2, [3, 2])        # <tf.Tensor: shape=(3, 2), dtype=int32, numpy= array([[1, 2], [3, 4], [5, 6]], dtype=int32)>

如果

shape

的一个参数为是

-1

，则计算该维度的大小，使总大小保持不变。特别是，若

shape

为

[-1]

，则将

tensor

展平为一维。

shape

的参数最多只能有一个

-1

。此外，若

t

为仅含一个元素的

tensor

，则

tf.reshape(t, [])

将

t

转变为一个标量。

函数实现

@tf_export("reshape", v1=["reshape", "manip.reshape"])
@dispatch.add_dispatch_support
def reshape(tensor, shape, name=None):  # pylint: disable=redefined-outer-name
  r"""Reshapes a tensor.
  Given `tensor`, this operation returns a new `tf.Tensor` that has the same
  values as `tensor` in the same order, except with a new shape given by
  `shape`.
  >>> t1 = [[1, 2, 3],
  ...       [4, 5, 6]]
  >>> print(tf.shape(t1).numpy())
  [2 3]
  >>> t2 = tf.reshape(t1, [6])
  >>> t2
  <tf.Tensor: shape=(6,), dtype=int32,
    numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
  >>> tf.reshape(t2, [3, 2])
  <tf.Tensor: shape=(3, 2), dtype=int32, numpy=
    array([[1, 2],
           [3, 4],
           [5, 6]], dtype=int32)>
  The `tf.reshape` does not change the order of or the total number of elements
  in the tensor, and so it can reuse the underlying data buffer. This makes it
  a fast operation independent of how big of a tensor it is operating on.
  >>> tf.reshape([1, 2, 3], [2, 2])
  Traceback (most recent call last):
  ...
  InvalidArgumentError: Input to reshape is a tensor with 3 values, but the
  requested shape has 4
  To instead reorder the data to rearrange the dimensions of a tensor, see
  `tf.transpose`.
  >>> t = [[1, 2, 3],
  ...      [4, 5, 6]]
  >>> tf.reshape(t, [3, 2]).numpy()
  array([[1, 2],
         [3, 4],
         [5, 6]], dtype=int32)
  >>> tf.transpose(t, perm=[1, 0]).numpy()
  array([[1, 4],
         [2, 5],
         [3, 6]], dtype=int32)
  If one component of `shape` is the special value -1, the size of that
  dimension is computed so that the total size remains constant.  In particular,
  a `shape` of `[-1]` flattens into 1-D.  At most one component of `shape` can
  be -1.
  >>> t = [[1, 2, 3],
  ...      [4, 5, 6]]
  >>> tf.reshape(t, [-1])
  <tf.Tensor: shape=(6,), dtype=int32,
    numpy=array([1, 2, 3, 4, 5, 6], dtype=int32)>
  >>> tf.reshape(t, [3, -1])
  <tf.Tensor: shape=(3, 2), dtype=int32, numpy=
    array([[1, 2],
           [3, 4],
           [5, 6]], dtype=int32)>
  >>> tf.reshape(t, [-1, 2])
  <tf.Tensor: shape=(3, 2), dtype=int32, numpy=
    array([[1, 2],
           [3, 4],
           [5, 6]], dtype=int32)>
  `tf.reshape(t, [])` reshapes a tensor `t` with one element to a scalar.
  >>> tf.reshape([7], []).numpy()
  7
  More examples:
  >>> t = [1, 2, 3, 4, 5, 6, 7, 8, 9]
  >>> print(tf.shape(t).numpy())
  [9]
  >>> tf.reshape(t, [3, 3])
  <tf.Tensor: shape=(3, 3), dtype=int32, numpy=
    array([[1, 2, 3],
           [4, 5, 6],
           [7, 8, 9]], dtype=int32)>
  >>> t = [[[1, 1], [2, 2]],
  ...      [[3, 3], [4, 4]]]
  >>> print(tf.shape(t).numpy())
  [2 2 2]
  >>> tf.reshape(t, [2, 4])
  <tf.Tensor: shape=(2, 4), dtype=int32, numpy=
    array([[1, 1, 2, 2],
           [3, 3, 4, 4]], dtype=int32)>
  >>> t = [[[1, 1, 1],
  ...       [2, 2, 2]],
  ...      [[3, 3, 3],
  ...       [4, 4, 4]],
  ...      [[5, 5, 5],
  ...       [6, 6, 6]]]
  >>> print(tf.shape(t).numpy())
  [3 2 3]
  >>> # Pass '[-1]' to flatten 't'.
  >>> tf.reshape(t, [-1])
  <tf.Tensor: shape=(18,), dtype=int32,
    numpy=array([1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],
    dtype=int32)>
  >>> # -- Using -1 to infer the shape --
  >>> # Here -1 is inferred to be 9:
  >>> tf.reshape(t, [2, -1])
  <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
    array([[1, 1, 1, 2, 2, 2, 3, 3, 3],
           [4, 4, 4, 5, 5, 5, 6, 6, 6]], dtype=int32)>
  >>> # -1 is inferred to be 2:
  >>> tf.reshape(t, [-1, 9])
  <tf.Tensor: shape=(2, 9), dtype=int32, numpy=
    array([[1, 1, 1, 2, 2, 2, 3, 3, 3],
           [4, 4, 4, 5, 5, 5, 6, 6, 6]], dtype=int32)>
  >>> # -1 is inferred to be 3:
  >>> tf.reshape(t, [ 2, -1, 3])
  <tf.Tensor: shape=(2, 3, 3), dtype=int32, numpy=
    array([[[1, 1, 1],
            [2, 2, 2],
            [3, 3, 3]],
           [[4, 4, 4],
            [5, 5, 5],
            [6, 6, 6]]], dtype=int32)>
  Args:
    tensor: A `Tensor`.
    shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
      Defines the shape of the output tensor.
    name: Optional string. A name for the operation.
  Returns:
    A `Tensor`. Has the same type as `tensor`.
  """
  result = gen_array_ops.reshape(tensor, shape, name)
  tensor_util.maybe_set_static_shape(result, shape)
  return result