tf.reshape(tensor, shape, name=None)改变tensor的形状

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

Given `tensor`, this operation returns a tensor that has the same values as `tensor` with shape `shape`.

Args:
  tensor: A `Tensor`.
  shape: A `Tensor`. Must be one of the following types: `int32`, `int64`.
    Defines the shape of the output tensor.
  name: A name for the operation (optional).

Returns:
  A `Tensor`. Has the same type as `tensor`.

注意：（1）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.

（2） If `shape` is 1-D or higher, then the operation returns a tensor with shape `shape` filled with the values of `tensor`. In this case, the number of elements implied by `shape` must be the same as the number of elements in `tensor`.

举例：

  # tensor 't' is [1, 2, 3, 4, 5, 6, 7, 8, 9]   # tensor 't' has shape [9]   reshape(t, [3, 3]) ==> [[1, 2, 3],                           [4, 5, 6],                           [7, 8, 9]]    # tensor 't' is [[[1, 1], [2, 2]],   #                [[3, 3], [4, 4]]]   # tensor 't' has shape [2, 2, 2]   reshape(t, [2, 4]) ==> [[1, 1, 2, 2],                           [3, 3, 4, 4]]    # tensor 't' is [[[1, 1, 1],   #                 [2, 2, 2]],   #                [[3, 3, 3],   #                 [4, 4, 4]],   #                [[5, 5, 5],   #                 [6, 6, 6]]]   # tensor 't' has shape [3, 2, 3]   # pass '[-1]' to flatten 't'   reshape(t, [-1]) ==> [1, 1, 1, 2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6]    # -1 can also be used to infer the shape    # -1 is inferred to be 9:   reshape(t, [2, -1]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],                            [4, 4, 4, 5, 5, 5, 6, 6, 6]]   # -1 is inferred to be 2:   reshape(t, [-1, 9]) ==> [[1, 1, 1, 2, 2, 2, 3, 3, 3],                            [4, 4, 4, 5, 5, 5, 6, 6, 6]]   # -1 is inferred to be 3:   reshape(t, [ 2, -1, 3]) ==> [[[1, 1, 1],                                 [2, 2, 2],                                 [3, 3, 3]],                                [[4, 4, 4],                                 [5, 5, 5],                                 [6, 6, 6]]]    # tensor 't' is [7]   # shape `[]` reshapes to a scalar   reshape(t, []) ==> 7