tf.reshape  |  TensorFlow v2.16.1 (original) (raw)

Reshapes a tensor.

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tf.compat.v1.reshape

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

Used in the notebooks

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 byshape.

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, seetf.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)>