def search_default(cls) -> "FullyConv1Resnet.ModelParams":
return cls(
num_groups=params.Discrete([8, 16, 32, 64]),
num_blocks_per_residual=params.Integer(1, 5),
activation=params.Categorical([nn.LeakyReLU, nn.GELU]),
do_include_first_norm=params.Boolean(p_true=0.8),
)
class ModelHyperParams(params.ParameterSet):
num_hidden_layers = params.Integer(1, 4)
num_neurons_per_layer = params.Discrete(
np.logspace(2, 7, num=6, base=2, dtype=np.int))
dropout_rate = params.Float(0.0, 0.99)
activation = params.Categorical(['relu', 'sigmoid'])
output_dir = '/tmp/output'
filter_size = 3
'korv_which', 'conv', 'sharp', 'search', 'v0.0.1'
]
hp.exp.sources_glob_str = constants.THIS_DIR.parent.parent / '**/*.py'
hp.embed.dim = 16
hp.embed.requires_grad = True
hp.conv.num_features = params.Discrete([32, 64, 128, 256])
hp.conv.num_layers = params.Integer(2, 11)
hp.conv.kernel_size = 3
hp.conv.num_groups = params.Discrete([8, 16, 32, 64])
hp.conv.num_blocks_per_pool = 20
hp.conv.num_blocks_per_skip = 2
hp.conv.requires_grad = True
hp.trans.impl = params.Categorical(['fast', 'fast-favor'])
hp.trans.num_layers = params.Discrete([0, 2, 4, 6, 8])
hp.trans.num_heads = params.Discrete([4, 8, 12])
hp.trans.num_query_features = params.Discrete([0, 32, 64, 128, 256, 512])
hp.trans.fc_dim_mult = params.Discrete([1, 2, 3, 4])
hp.fc.num_features = params.Discrete([32, 64, 128, 256])
hp.fc.num_layers = params.Integer(2, 7)
hp.fc.num_groups = params.Discrete([8, 16, 32, 64])
hp.fc.num_blocks_per_residual = params.Integer(1, 5)
hp.fc.num_blocks_per_dropout = params.Integer(1, 8)
hp.fc.requires_grad = True
hp.heads.num_features = params.Discrete([32, 64, 128, 256])
hp.heads.num_layers = params.Integer(2, 5)
hp.heads.num_groups = params.Discrete([8, 16, 32, 64])
env = os.environ.copy()
env['PROJECT_ID'] = GCLOUD_PROJECT_NAME
env['IMAGE_URI'] = CONTAINER_IMAGE_URI
env['BUCKET_NAME'] = GCLOUD_BUCKET_NAME
# Create a Cloud AI Platform Hyperparameter Search object
search = search.HyperparamSearchSpec(
max_trials=10,
max_parallel_trials=5,
max_failed_trials=2,
hyperparameter_metric_tag='val_acc',
)
# Add parameter search ranges for this problem.
my_param_ranges = train.MyParams(
activation=params.Categorical(['relu', 'tanh']),
num_layers=params.Integer(min_value=1, max_value=3),
num_neurons=params.Discrete(np.logspace(2, 8, num=7, base=2)),
dropout_rate=params.Double(min_value=-0.1, max_value=0.9),
learning_rate=params.Discrete(np.logspace(-6, 2, 17, base=10)),
batch_size=params.Integer(min_value=1, max_value=128),
)
search.add_parameters(my_param_ranges)
# Call a bash script to build a docker image for this repo, submit it to the docker registry defined in the script
# and run a training job on the Cloud AI Platform using this container and these hyperparameter ranges.
this_dir = Path(__file__).resolve().parent
retcode = subprocess.call([this_dir / 'build_push.sh'], env=env)
if retcode:
raise ValueError(f"Got returncode: {retcode}")
def search_default(cls) -> "SlabNet.ModelParams":
return cls(
num_features=params.Categorical([16, 32, 64, 128, 256]),
num_layers=params.Integer(1, 5),
**FullyConv1Resnet.ModelParams.search_default().__dict__,
)