# IfElse¶

class `paddle.fluid.layers.``IfElse`(cond, name=None)[source]

This class is used to implement IfElse branch control function. IfElse contains two blocks, true_block and false_block. IfElse will put data satisfying True or False conditions into different blocks to run.

Cond is a 2-D Tensor with shape [N, 1] and data type bool, representing the execution conditions of the corresponding part of the input data.

IfElse OP is different from other OPs in usage, which may cause some users confusion. Here is a simple example to illustrate this OP.

```# The following code completes the function: subtract 10 from the data greater than 0 in x, add 10 to the data less than 0 in x, and sum all the data.
import numpy as np

x = fluid.layers.data(name='x', shape=[4, 1], dtype='float32', append_batch_size=False)
y = fluid.layers.data(name='y', shape=[4, 1], dtype='float32', append_batch_size=False)

x_d = np.array([, , [-2], [-3]]).astype(np.float32)
y_d = np.zeros((4, 1)).astype(np.float32)

# Compare the size of x, y pairs of elements, output cond, cond is shape [4, 1], data type bool 2-D tensor.
# Based on the input data x_d, y_d, it can be inferred that the data in cond are [[true], [true], [false], [false]].
cond = fluid.layers.greater_than(x, y)
# Unlike other common OPs, ie below returned by the OP is an IfElse OP object
ie = fluid.layers.IfElse(cond)

with ie.true_block():
# In this block, according to cond condition, the data corresponding to true dimension in X is obtained and subtracted by 10.
out_1 = ie.input(x)
out_1 = out_1 - 10
ie.output(out_1)
with ie.false_block():
# In this block, according to cond condition, get the data of the corresponding condition in X as false dimension, and add 10
out_1 = ie.input(x)
out_1 = out_1 + 10
ie.output(out_1)

# According to cond condition, the data processed in the two blocks are merged. The output here is output, the type is List, and the element type in List is Variable.
output = ie() #  [array([[-7.], [-9.], [ 8.], [ 7.]], dtype=float32)]

# Get the first Variable in the output List and add all elements.
out = fluid.layers.reduce_sum(output)

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

res = exe.run(fluid.default_main_program(), feed={"x":x_d, "y":y_d}, fetch_list=[out])
print res
# [array([-1.], dtype=float32)]
```
Parameters
• cond (Variable) – cond is a 2-D Tensor with shape [N, 1] and data type bool, representing the corresponding execution conditions of N input data. The data type is bool.

• 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 .

Returns

Unlike other common OPs, the OP call returns an IfElse OP object (e.g. ie in the example), which branches the input data by calling the internal functions of the object `true_block ()`, `false_block ()`, `input ()`, `output ()`, and integrates the data processed by different branches as the overall output by calling the internal `call ()` function. The output type is a list, and the type of each element in the list is Variable.

Internal Functions:

The block is constructed by calling the `with ie. true_block()` function in the object, and the computational logic under condition true is put into the block. If no corresponding block is constructed, the input data in the corresponding conditional dimension is unchanged.

The block is constructed by calling the `with ie. false_block()` function in the object, and the computational logic under condition false is put into the block. If no corresponding block is constructed, the input data in the corresponding conditional dimension is unchanged.

`Out = ie. input (x)` will take out the data of the corresponding conditional dimension in X and put it into out, supporting the internal processing of multiple inputs in block.

`ie. output (out)` writes the result to the output of the corresponding condition.

There is a `call ()` function inside the object, that is, by calling `output = ie ()`, all the outputs inside the block of False are fused as the whole output, the output type is a list, and the type of each element in the list is Variable.