# 3.21. Experiments¶

This notebook documents the use of the Experiment class for running many experiments, and comparing the results.

In [1]:

from conx import Network, Experiment

Using Theano backend.
conx, version 3.4.3


First, we create an experiment object:

In [2]:

exp = Experiment("XOR-Test1")


Experiment takes a function, and any number of variations of values.

The function takes whatever argumnets you wish, creates a network, trains it, and returns the network and a category describing the run:

In [3]:

def function(optimizer, activation, **options):
net = Network("XOR", 2, 2, 1, activation=activation)
net.compile(error="mse", optimizer=optimizer)
net.dataset.add_by_function(2, (0, 4), "binary", lambda i,v: [int(sum(v) == len(v))])
net.train(report_rate=10000, verbose=0, **options)
category = "%s-%s" % (optimizer, activation)
return category, net


Then we run a number of trials (perhaps just 1). Notice that each argument should be given as a list. The total number of runs per trial is the product of the lengths of the arguments.

In [4]:

exp.run(function,
trials=2,
epochs=[1000],
accuracy=[0.8],
tolerance=[0.2],
activation=["sigmoid", "relu"],
dir="/tmp/")


The results is a list of (category, network-name) pairs:

In [5]:

exp.results

Out[5]:

[('adam-sigmoid', '/tmp/XOR-Test1-00001-00001'),
('sgd-sigmoid', '/tmp/XOR-Test1-00001-00002'),
('sgd-relu', '/tmp/XOR-Test1-00001-00004'),
('sgd-sigmoid', '/tmp/XOR-Test1-00002-00002'),
('sgd-relu', '/tmp/XOR-Test1-00002-00004')]


Often, you may wish to plot the results of learning. This may take some time, as the function will re-load each network:

In [6]:

exp.plot("loss")


Notice that each category has its own color.

There is also a generic apply method for calling a function with each of the (category, network-names). Ususally, you would probably want to re-load the network, and perform some operation in the function.

In [7]:

exp.apply(lambda category, exp_name: (category, exp_name))

Out[7]:

[('adam-sigmoid', '/tmp/XOR-Test1-00001-00001'),
('sgd-sigmoid', '/tmp/XOR-Test1-00001-00002'),