# Conditions¶

## IfElse vs Switch¶

Both ops build a condition over symbolic variables.

`IfElse`

takes a*boolean*condition and two variables as inputs.`Switch`

takes a*tensor*as condition and two variables as inputs.`switch`

is an elementwise operation and is thus more general than`ifelse`

.Whereas

`switch`

evaluates both*output*variables,`ifelse`

is lazy and only evaluates one variable with respect to the condition.

**Example**

```
from theano import tensor as T
from theano.ifelse import ifelse
import theano, time, numpy
a,b = T.scalars('a', 'b')
x,y = T.matrices('x', 'y')
z_switch = T.switch(T.lt(a, b), T.mean(x), T.mean(y))
z_lazy = ifelse(T.lt(a, b), T.mean(x), T.mean(y))
f_switch = theano.function([a, b, x, y], z_switch,
mode=theano.Mode(linker='vm'))
f_lazyifelse = theano.function([a, b, x, y], z_lazy,
mode=theano.Mode(linker='vm'))
val1 = 0.
val2 = 1.
big_mat1 = numpy.ones((10000, 1000))
big_mat2 = numpy.ones((10000, 1000))
n_times = 10
tic = time.clock()
for i in range(n_times):
f_switch(val1, val2, big_mat1, big_mat2)
print('time spent evaluating both values %f sec' % (time.clock() - tic))
tic = time.clock()
for i in range(n_times):
f_lazyifelse(val1, val2, big_mat1, big_mat2)
print('time spent evaluating one value %f sec' % (time.clock() - tic))
```

In this example, the `IfElse`

op spends less time (about half as much) than `Switch`

since it computes only one variable out of the two.

```
$ python ifelse_switch.py
time spent evaluating both values 0.6700 sec
time spent evaluating one value 0.3500 sec
```

Unless `linker='vm'`

or `linker='cvm'`

are used, `ifelse`

will compute both
variables and take the same computation time as `switch`

. Although the linker
is not currently set by default to `cvm`

, it will be in the near future.

There is no automatic optimization replacing a `switch`

with a
broadcasted scalar to an `ifelse`

, as this is not always faster. See
this ticket.

Note

If you use test values, then all branches of the IfElse will be computed. This is normal, as using test_value means everything will be computed when we build it, due to Python’s greedy evaluation and the semantic of test value. As we build both branches, they will be executed for test values. This doesn’t cause any changes during the execution of the compiled Theano function.