|---| | [ View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.21.0/tensorflow/python/ops/array_ops.py#L63-L201) | Reshapes a tensor. #### View aliases **Compat aliases for migration** See [Migration guide](https://www.tensorflow.org/guide/migrate) for more details. [`tf.compat.v1.reshape`](https://www.tensorflow.org/api_docs/python/tf/reshape)
tf.reshape( tensor, shape, name=None ) ### Used in the notebooks | Used in the guide | Used in the tutorials | |---|---| | - [Introduction to Tensors](https://www.tensorflow.org/guide/tensor) - [Advanced automatic differentiation](https://www.tensorflow.org/guide/advanced_autodiff) - [DTensor concepts](https://www.tensorflow.org/guide/dtensor_overview) - [Multilayer perceptrons for digit recognition with Core APIs](https://www.tensorflow.org/guide/core/mlp_core) - [Distributed training with Core APIs and DTensor](https://www.tensorflow.org/guide/core/distribution) | - [Learned data compression](https://www.tensorflow.org/tutorials/generative/data_compression) - [Adversarial example using FGSM](https://www.tensorflow.org/tutorials/generative/adversarial_fgsm) - [Convolutional Variational Autoencoder](https://www.tensorflow.org/tutorials/generative/cvae) - [Load CSV data](https://www.tensorflow.org/tutorials/load_data/csv) - [Load text](https://www.tensorflow.org/tutorials/load_data/text) | Given `tensor`, this operation returns a new [`tf.Tensor`](https://www.tensorflow.org/api_docs/python/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.reshape(t2, [3, 2]) The [`tf.reshape`](https://www.tensorflow.org/api_docs/python/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`](https://www.tensorflow.org/api_docs/python/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.reshape(t, [3, -1]) tf.reshape(t, [-1, 2]) `tf.reshape(t, [])` reshapes a tensor `t` with one element to a scalar. tf.reshape([7], []).numpy().item() 7 #### More examples: t = [1, 2, 3, 4, 5, 6, 7, 8, 9] print(tf.shape(t).numpy()) [9] tf.reshape(t, [3, 3]) t = [[[1, 1], [2, 2]], [[3, 3], [4, 4]]] print(tf.shape(t).numpy()) [2 2 2] tf.reshape(t, [2, 4]) 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]) # -- Using -1 to infer the shape -- # Here -1 is inferred to be 9: tf.reshape(t, [2, -1]) # -1 is inferred to be 2: tf.reshape(t, [-1, 9]) # -1 is inferred to be 3: tf.reshape(t, [ 2, -1, 3])


| ## 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`. ||