paddle.static. data ( name, shape, dtype=None, lod_level=0 ) [source]

This function creates a variable on the global block. The global variable can be accessed by all the following operators in the graph. The variable is a placeholder that could be fed with input, such as Executor can feed input into the variable. When dtype is None, the dtype will get from the global dtype by paddle.get_default_dtype().

  • name (str) – The name/alias of the variable, see Name for more details.

  • shape (list|tuple) – List|Tuple of integers declaring the shape. You can set None or -1 at a dimension to indicate the dimension can be of any size. For example, it is useful to set changeable batch size as None or -1.

  • dtype (np.dtype|str, optional) – The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: None. When dtype is not set, the dtype will get from the global dtype by paddle.get_default_dtype().

  • lod_level (int, optional) – The LoD level of the LoDTensor. Usually users don’t have to set this value. Default: 0.


The global variable that gives access to the data.

Return type



>>> import numpy as np
>>> import paddle
>>> paddle.enable_static()

# Creates a variable with fixed size [3, 2, 1]
# User can only feed data of the same shape to x
# the dtype is not set, so it will set "float32" by
# paddle.get_default_dtype(). You can use paddle.get_default_dtype() to
# change the global dtype
>>> x ='x', shape=[3, 2, 1])

# Creates a variable with changeable batch size -1.
# Users can feed data of any batch size into y,
# but size of each data sample has to be [2, 1]
>>> y ='y', shape=[-1, 2, 1], dtype='float32')

>>> z = x + y

# In this example, we will feed x and y with np-ndarray "1"
# and fetch z, like implementing "1 + 1 = 2" in PaddlePaddle
>>> feed_data = np.ones(shape=[3, 2, 1], dtype=np.float32)

>>> exe = paddle.static.Executor(paddle.framework.CPUPlace())
>>> out =,
...             feed={
...                 'x': feed_data,
...                 'y': feed_data
...             },
...             fetch_list=[])

# np-ndarray of shape=[3, 2, 1], dtype=float32, whose elements are 2
>>> print(out)
        [2.]]], dtype=float32)]