While

class paddle.fluid.layers.control_flow. While ( cond, is_test=False, name=None ) [source]
Api_attr

Static Graph

while loop control flow. Repeat while body until cond is False.

Note

A new OP api_fluid_layers_while_loop is highly recommended instead of While if the shape of parameter cond is [1]. OP api_fluid_layers_while_loop is easier to use and is called with less code but does the same thing as While .

Notice:

Local variables created in While are similar to that created in while of C++, and cannot be referenced externally. As a result, they cannot be obtained through fetch_list of Executor. If you would like to access the variable out of while , PaddlePaddle provides assign API to assign local variables to external. Please refer to example code 2 or refer to issue#22724.

Parameters

  • cond (Variable) – A Tensor whose data type is bool controlling whether to continue looping.

  • is_test (bool, optional) – A flag indicating whether execution is in test phase. Default value is False.

  • name (str, optional) – The default value is None. Normally there is no need for user to set this property. For more information, please refer to Name .

Examples 1: 
import paddle.fluid as fluid
import numpy as np

i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)           # loop counter

loop_len = fluid.layers.fill_constant(shape=[1],dtype='int64', value=10)    # loop length

cond = fluid.layers.less_than(x=i, y=loop_len)
while_op = fluid.layers.While(cond=cond)
with while_op.block():
    i = fluid.layers.increment(x=i, value=1, in_place=True)
    fluid.layers.less_than(x=i, y=loop_len, cond=cond)

exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())

res = exe.run(fluid.default_main_program(), feed={}, fetch_list=[i])
print(res) # [array([10])]
Examples 2: 
import paddle.fluid as fluid
import numpy as np

i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=0)
loop_len = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
one = fluid.layers.fill_constant(shape=[1], dtype='float32', value=1)
data = fluid.data(name='data', shape=[1], dtype='float32')
sums = fluid.layers.fill_constant(shape=[1], dtype='float32', value=0)  # Define the variable to be obtained ouside of While, which name should be different from the variable inside the While to be obtained

cond = fluid.layers.less_than(x=i, y=loop_len)
while_op = fluid.layers.While(cond=cond)
with while_op.block():
    sums_tensor = fluid.layers.elementwise_add(x=data, y=data)
    fluid.layers.assign(sums_tensor, sums)  # Update the value of sums_tensor defined in While to the sums which defined outside of While through layers.assign
    i = fluid.layers.increment(x=i, value=1, in_place=True)
    data = fluid.layers.elementwise_add(x=data, y=one)
    fluid.layers.less_than(x=i, y=loop_len, cond=cond)

feed_data = np.ones(1).astype('float32')
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_startup_program())
res = exe.run(fluid.default_main_program(), feed={'data': feed_data}, fetch_list=sums)
print(res[0])  # [2.]    # Because the data in While does not update the value outside the While, the value of sums is [2.] after the loop