/TensorFlow 2.4

# tf.reshape

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`.