def main():
""" Main function. """
# Obtain the search space
nn_domain = get_nn_domain_from_constraints('cnn', MAX_NUM_LAYERS,
MIN_NUM_LAYERS, MAX_MASS,
MIN_MASS, MAX_IN_DEGREE,
MAX_OUT_DEGREE, MAX_NUM_EDGES,
MAX_NUM_UNITS_PER_LAYER,
MIN_NUM_UNITS_PER_LAYER)
# Obtain a worker manager: A worker manager (defined in opt/worker_manager.py) is used
# to manage (possibly) multiple workers. For a synthetic experiment, we will use a
# synthetic worker manager with 1 worker.
worker_manager = SyntheticWorkerManager(1)
# Obtain a function caller: A function_caller is used to evaluate a function defined on
# neural network architectures. Here, we have obtained a function_caller from a
# synthetic function, but for real experiments, you might have to write your own caller.
# See the MLP/CNN demos for an example.
func_caller = FunctionCaller(cnn_syn_func1, nn_domain)
# Finally, specify the budget. In this case, it will be just the number of evaluations.
budget = 20
# Run nasbot
opt_val, opt_nn, _ = nasbot.nasbot(func_caller, worker_manager, budget)
# Print the optimal value and visualise the best network.
print '\nOptimum value found: ', opt_val
print 'Optimal network visualised in syn_opt_network.eps.'
visualise_nn(opt_nn, 'syn_opt_network')
def main():
""" Main function. """
# Obtain the search space
nn_domain = get_nn_domain_from_constraints('cnn', MAX_NUM_LAYERS,
MIN_NUM_LAYERS, MAX_MASS,
MIN_MASS, MAX_IN_DEGREE,
MAX_OUT_DEGREE, MAX_NUM_EDGES,
MAX_NUM_UNITS_PER_LAYER,
MIN_NUM_UNITS_PER_LAYER)
# Obtain a worker manager: A worker manager (defined in opt/worker_manager.py) is used
# to manage (possibly) multiple workers. For a RealWorkerManager, the budget should be
# given in wall clock seconds.
worker_manager = RealWorkerManager(GPU_IDS, EXP_DIR)
# Obtain a function caller: A function_caller is used to evaluate a function defined on
# neural network architectures. We have defined the CNNFunctionCaller in
# demos/cnn_function_caller.py. The train_params argument can be used to specify
# additional training parameters such as the learning rate etc.
train_params = Namespace(data_dir=DATA_DIR)
func_caller = CNNFunctionCaller('cifar10',
nn_domain,
train_params,
tmp_dir=TMP_DIR,
reporter=REPORTER)
# Run nasbot
opt_val, opt_nn, _ = nasbot.nasbot(func_caller,
worker_manager,
BUDGET,
reporter=REPORTER)
# Print the optimal value and visualise the best network.
REPORTER.writeln('\nOptimum value found: %f' % opt_val)
visualise_file = os.path.join(EXP_DIR, 'cnn_opt_network')
REPORTER.writeln('Optimal network visualised in %s.eps' % (visualise_file))
visualise_nn(opt_nn, EXP_DIR + visualise_file)
def main():
""" Main function. """
# Obtain the search space
nn_domain = get_nn_domain_from_constraints('mlp-reg', MAX_NUM_LAYERS,
MIN_NUM_LAYERS, MAX_MASS,
MIN_MASS, MAX_IN_DEGREE,
MAX_OUT_DEGREE, MAX_NUM_EDGES,
MAX_NUM_UNITS_PER_LAYER,
MIN_NUM_UNITS_PER_LAYER)
# Obtain a worker manager: A worker manager (defined in opt/worker_manager.py) is used
# to manage (possibly) multiple workers. For a RealWorkerManager, the budget should be
# given in wall clock seconds.
worker_manager = RealWorkerManager(GPU_IDS)
# Obtain a function caller: A function_caller is used to evaluate a function defined on
# neural network architectures. We have defined the MLPFunctionCaller in
# demos/mlp_function_caller.py. The train_params can be used to specify additional
# training parameters such as the learning rate etc.
train_params = Namespace(data_train_file=get_train_file_name(DATASET))
func_caller = MLPFunctionCaller(DATASET,
nn_domain,
train_params,
reporter=REPORTER,
tmp_dir=TMP_DIR)
# Run nasbot
opt_val, opt_nn, _ = nasbot.nasbot(func_caller,
worker_manager,
BUDGET,
reporter=REPORTER)
# Print the optimal value and visualise the best network.
REPORTER.writeln('\nOptimum value found: ' % (opt_val))
REPORTER.writeln('Optimal network visualised in mlp_opt_network.eps.')
visualise_nn(opt_nn, 'mlp_opt_network')
def test_nasbot_optimisation_asynchronous(self):
""" Tests optimisation of a single point. """
self.report('Testing NASBOT with 4 asynchronous workers.')
worker_manager = SyntheticWorkerManager(4, time_distro='halfnormal')
func_caller = FunctionCaller(mlp_syn_func1, self.mlp_domain)
tp_comp = get_tp_comp('mlp-reg')
options, options_clone, reporter, _, _ = self._get_optimiser_args(
'mlp-reg')
opt_val, opt_pt, history = nasbot.nasbot(func_caller,
worker_manager,
5,
tp_comp,
options=options,
reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options,
options_clone)
self.report('')
def test_nasbot_optimisation_single(self):
""" Tests optimisation with a single worker. """
self.report('Testing NASBOT with a single worker.')
worker_manager = SyntheticWorkerManager(1, time_distro='const')
func_caller = FunctionCaller(cnn_syn_func1, self.cnn_domain)
tp_comp = get_tp_comp('cnn')
options, options_clone, reporter, _, _ = self._get_optimiser_args(
'cnn')
opt_val, opt_pt, history = nasbot.nasbot(func_caller,
worker_manager,
10,
tp_comp,
options=options,
reporter=reporter)
self._test_optimiser_results(opt_val, opt_pt, history, options,
options_clone)
self.report('')
