empty¶

paddle. empty ( shape, dtype=None, name=None ) [source]

Returns a Tensor with uninitialized data which size is same as shape.

Parameters

• shape (list|tuple|Tensor) – Shape of the Tensor to be created. The data type of dimension of shape is int32 or int64 . If shape is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If shape is an Tensor, it should be an 1-D Tensor.

• dtype (np.dtype|str, optional) – Data type of the output Tensor which can be bool, float16, float32, float64, int32, int64, if dytpe is None, the data type of created Tensor use global default dtype (see get_default_dtype for details).

• name (str, optional) – For details, please refer to Name. Generally, no setting is required. Default: None.

Returns

Tensor which is created according to shape and dtype, and is uninitialized.

Return type

Tensor

Examples

import paddle

paddle.set_device("cpu")  # and use cpu device

# example 1: argument ``shape`` is a list which doesn't contain Tensor.
data1 = paddle.empty(shape=[2, 3], dtype='float32')
print(data1)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
#        [[0.00000000, 0.        , 0.00000000],
#         [0.        , 0.29652897, 0.09356152]])       # uninitialized

# example 2: argument ``shape`` is a Tensor, the data type must be int64 or int32.
shape_data = paddle.to_tensor([2, 3]).astype('int32')
data2 = paddle.empty(shape=shape_data, dtype='float32')
print(data2)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
#        [[-0.50543123, -0.09872390, -0.92634487],
#         [-0.51007903, -0.02454148,  1.29315734]])    # uninitialized

# example 3: argument ``shape`` is a list which contains Tensor.
dim2 = paddle.to_tensor([3]).astype('int32')
data3 = paddle.empty(shape=[2, dim2], dtype='float32')
print(data3)
# Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
#        [[ 0.00000000,  0.        , -0.92634487],
#         [-0.51007903, -0.02454148,  1.29315734]])    # uninitialized