def main(config):
searcher = se.RandomSearcher(get_search_space(
config.num_classes)) # random searcher
# create a logging folder to log information (config and features)
logger = log.SearchLogger('logs',
config.exp_name,
resume_if_exists=True,
create_parent_folders=True)
# return values
architectures = dict()
for i in range(int(config.num_samples)):
print("Sampling architecture %d" % i)
inputs, outputs, h_value_hist, searcher_eval_token = searcher.sample()
eval_logger = logger.get_current_evaluation_logger()
eval_logger.log_config(h_value_hist, searcher_eval_token)
eval_logger.log_features(inputs, outputs)
architectures[i] = {
'config_filepath': eval_logger.config_filepath,
'evaluation_filepath': eval_logger.get_evaluation_folderpath()
}
# write to a json file to communicate with master
ut.write_jsonfile(architectures, config.result_fp)
def main():
num_classes = 10
num_samples = 3 # number of architecture to sample
best_val_acc, best_architecture = 0., -1
# load and normalize data
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# defining evaluator and searcher
evaluator = SimpleClassifierEvaluator((x_train, y_train),
num_classes,
max_num_training_epochs=5)
searcher = se.RandomSearcher(get_search_space(num_classes))
for i in xrange(num_samples):
print("Sampling architecture %d" % i)
inputs, outputs, _, searcher_eval_token = searcher.sample()
val_acc = evaluator.evaluate(
inputs,
outputs)['val_acc'] # evaluate and return validation accuracy
print("Finished evaluating architecture %d, validation accuracy is %f" %
(i, val_acc))
if val_acc > best_val_acc:
best_val_acc = val_acc
best_architecture = i
searcher.update(val_acc, searcher_eval_token)
print("Best validation accuracy is %f with architecture %d" %
(best_val_acc, best_architecture))
def main():
num_classes = 10
num_samples = 3 # number of architecture to sample
best_val_acc, best_architecture = 0., -1
# donwload and normalize data, using test as val for simplicity
X_train, y_train, X_val, y_val, _, _ = load_mnist('data/mnist',
normalize_range=True)
# defining evaluator
evaluator = SimpleClassifierEvaluator((X_train, y_train), (X_val, y_val),
num_classes,
max_num_training_epochs=5,
log_output_to_terminal=True)
searcher = se.RandomSearcher(get_search_space(num_classes))
for i in xrange(num_samples):
print("Sampling architecture %d" % i)
M.renew_collection()
inputs, outputs, _, searcher_eval_token = searcher.sample()
val_acc = evaluator.evaluate(
inputs,
outputs)['val_acc'] # evaluate and return validation accuracy
print("Finished evaluating architecture %d, validation accuracy is %f" %
(i, val_acc))
if val_acc > best_val_acc:
best_val_acc = val_acc
best_architecture = i
searcher.update(val_acc, searcher_eval_token)
print("Best validation accuracy is %f with architecture %d" %
(best_val_acc, best_architecture))
def main():
num_classes = 10
num_samples = 4
num_training_epochs = 2
# NOTE: change to True for graph visualization
show_graph = False
# load data
(X_train, y_train, X_val, y_val, X_test, y_test) = load_mnist('data/mnist',
flatten=True)
train_dataset = InMemoryDataset(X_train, y_train, True)
val_dataset = InMemoryDataset(X_val, y_val, False)
test_dataset = InMemoryDataset(X_test, y_test, False)
# defining evaluator and searcher
evaluator = SimpleClassifierEvaluator(
train_dataset,
val_dataset,
num_classes,
num_training_epochs=num_training_epochs,
log_output_to_terminal=True)
ssf = mo.SearchSpaceFactory(lambda: dnn_net(num_classes))
searcher = se.RandomSearcher(ssf.get_search_space)
for i in range(num_samples):
inputs, outputs, _, searcher_eval_token = searcher.sample()
if show_graph:
# try setting draw_module_hyperparameter_info=False and
# draw_hyperparameters=True for a different visualization.
vi.draw_graph(outputs,
draw_module_hyperparameter_info=False,
draw_hyperparameters=True)
results = evaluator.evaluate(inputs, outputs)
# updating the searcher. no-op for the random searcher.
searcher.update(results['validation_accuracy'], searcher_eval_token)
def main():
num_classes = 10
num_samples = 3 # number of architecture to sample
metric = 'val_accuracy' # evaluation metric
resource_type = 'epoch'
max_resource = 81 # max resource that a configuration can have
# load and normalize data
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
# defining searcher and evaluator
evaluator = SimpleClassifierEvaluator((x_train, y_train), num_classes,
max_num_training_epochs=5)
searcher = se.RandomSearcher(get_search_space(num_classes))
hyperband = SimpleArchitectureSearchHyperBand(searcher, hyperband, metric, resource_type)
(best_config, best_perf) = hyperband.evaluate(max_resource)
print("Best %s is %f with architecture %d" % (metric, best_perf[0], best_config[0]))
def main():
num_classes = 10
num_samples = 4
num_training_epochs = 2
validation_frac = 0.2
# NOTE: change to True for graph visualization
show_graph = False
# load the data.
(X_train, y_train), (X_test, y_test) = mnist.load_data()
fn = lambda X: X.reshape((X.shape[0], -1))
X_train = fn(X_train) / 255.0
X_test = fn(X_test) / 255.0
num_train = int((1.0 - validation_frac) * X_train.shape[0])
X_train, X_val = X_train[:num_train], X_train[num_train:]
y_train, y_val = y_train[:num_train], y_train[num_train:]
# define the search and the evalutor
evaluator = SimpleClassifierEvaluator(
X_train,
y_train,
X_val,
y_val,
num_classes,
num_training_epochs=num_training_epochs)
search_space_fn = lambda: dnn_net(num_classes)
searcher = se.RandomSearcher(search_space_fn)
for i in range(num_samples):
(inputs, outputs, hyperp_value_lst,
searcher_eval_token) = searcher.sample()
if show_graph:
# try setting draw_module_hyperparameter_info=False and
# draw_hyperparameters=True for a different visualization.
vi.draw_graph(outputs,
draw_module_hyperparameter_info=False,
draw_hyperparameters=True)
results = evaluator.evaluate(inputs, outputs)
# updating the searcher. no-op for the random searcher.
searcher.update(results['validation_accuracy'], searcher_eval_token)
def main():
num_classes = 10
num_samples = 3
num_training_epochs = 2
batch_size = 256
# NOTE: change to True for graph visualization
show_graph = False
# load and normalize data
(X_train, y_train, X_val, y_val, X_test,
y_test) = load_mnist(flatten=True, one_hot=False)
train_dataset = InMemoryDataset(X_train, y_train, True)
val_dataset = InMemoryDataset(X_val, y_val, False)
test_dataset = InMemoryDataset(X_test, y_test, False)
# defining evaluator and searcher
evaluator = SimpleClassifierEvaluator(
train_dataset,
val_dataset,
num_classes,
num_training_epochs=num_training_epochs,
batch_size=batch_size,
log_output_to_terminal=True)
search_space_fn = lambda: dnn_net(num_classes)
searcher = se.RandomSearcher(search_space_fn)
for i in range(num_samples):
inputs, outputs, _, searcher_eval_token = searcher.sample()
if show_graph:
# try setting draw_module_hyperparameter_info=False and
# draw_hyperparameters=True for a different visualization.
vi.draw_graph(outputs,
draw_module_hyperparameter_info=False,
draw_hyperparameters=True)
results = evaluator.evaluate(inputs, outputs)
searcher.update(results['validation_accuracy'], searcher_eval_token)
from __future__ import absolute_import
from deep_architect.searchers.regularized_evolution import EvolutionSearcher, mutatable
import deep_architect.searchers.random as ra
from deep_architect.searchers.smbo_mcts import SMBOSearcherWithMCTSOptimizer
from deep_architect.searchers.mcts import MCTSSearcher
from deep_architect.searchers.smbo_random import SMBOSearcher
from deep_architect.surrogates.hashing import HashingSurrogate
name_to_searcher_fn = {
'random':
lambda ssf: ra.RandomSearcher(ssf),
'evolution_pop=100_samp=25_reg=t':
lambda ssf: EvolutionSearcher(ssf, mutatable, 100, 25, regularized=True),
'evolution_pop=64_samp=16_reg=t':
lambda ssf: EvolutionSearcher(ssf, mutatable, 64, 16, regularized=True),
'evolution_pop=20_samp=20_reg=t':
lambda ssf: EvolutionSearcher(ssf, mutatable, 20, 20, regularized=True),
'evolution_pop=100_samp=50_reg=t':
lambda ssf: EvolutionSearcher(ssf, mutatable, 100, 50, regularized=True),
'evolution_pop=100_samp=2_reg=t':
lambda ssf: EvolutionSearcher(ssf, mutatable, 100, 2, regularized=True),
'evolution_pop=100_samp=25_reg=f':
lambda ssf: EvolutionSearcher(ssf, mutatable, 100, 25, regularized=False),
'evolution_pop=64_samp=16_reg=f':
lambda ssf: EvolutionSearcher(ssf, mutatable, 64, 16, regularized=False),
'evolution_pop=20_samp=20_reg=f':
lambda ssf: EvolutionSearcher(ssf, mutatable, 20, 20, regularized=False),
'evolution_pop=100_samp=50_reg=f':
lambda ssf: EvolutionSearcher(ssf, mutatable, 100, 50, regularized=False),
'evolution_pop=100_samp=2_reg=f':
lambda ssf: EvolutionSearcher(ssf, mutatable, 100, 2, regularized=False),
