tf.reshape

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Reshapes a tensor.

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.manip.reshape, tf.compat.v1.reshape

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

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.