# mindspore.ops.Tile

class mindspore.ops.Tile[source]

Replicates a tensor with given multiples times.

Creates a new tensor by replicating input_x multiples times. The i’th dimension of output tensor has input_x.shape(i) * multiples[i] elements, and the values of input_x are replicated multiples[i] times along the i’th dimension.

Note

The length of multiples must be greater or equal to the length of dimension in input_x.

Inputs:
• input_x (Tensor) - 1-D or higher Tensor. Set the shape of input tensor as $$(x_1, x_2, ..., x_S)$$.

• multiples (tuple[int]) - The input tuple is constructed by multiple integers, i.e., $$(y_1, y_2, ..., y_S)$$. The length of multiples cannot be smaller than the length of the shape of input_x. Only constant value is allowed.

Outputs:

Tensor, has the same data type as the input_x.

• If the length of multiples is the same as the length of shape of input_x, then the shape of their corresponding positions can be multiplied, and the shape of Outputs is $$(x_1*y_1, x_2*y_2, ..., x_S*y_R)$$.

• If the length of multiples is larger than the length of shape of input_x, fill in multiple 1 in the length of the shape of input_x until their lengths are consistent. Such as set the shape of input_x as $$(1, ..., x_1, x_2, ..., x_S)$$, then the shape of their corresponding positions can be multiplied, and the shape of Outputs is $$(1*y_1, ..., x_S*y_R)$$.

Raises
• TypeError – If multiples is not a tuple or its elements are not all int.

• ValueError – If the elements of multiples are not all greater than 0.

• ValueError – If the length of multiples are smaller than the length of dimension in input_x.

Supported Platforms:

Ascend GPU CPU

Examples

>>> tile = ops.Tile()
>>> input_x = Tensor(np.array([[1, 2], [3, 4]]), mindspore.float32)
>>> multiples = (2, 3)
>>> output = tile(input_x, multiples)
>>> print(output)
[[1.  2.  1.  2.  1.  2.]
[3.  4.  3.  4.  3.  4.]
[1.  2.  1.  2.  1.  2.]
[3.  4.  3.  4.  3.  4.]]
>>> multiples = (2, 3, 2)
>>> output = tile(input_x, multiples)
>>> print(output)
[[[1. 2. 1. 2.]
[3. 4. 3. 4.]
[1. 2. 1. 2.]
[3. 4. 3. 4.]
[1. 2. 1. 2.]
[3. 4. 3. 4.]]
[[1. 2. 1. 2.]
[3. 4. 3. 4.]
[1. 2. 1. 2.]
[3. 4. 3. 4.]
[1. 2. 1. 2.]
[3. 4. 3. 4.]]]