"""
import tensorflow as tf
import determined as det
from determined import experimental
from determined.experimental.keras import init
config = {
"searcher": {
"name": "adaptive_simple",
"metric": "val_loss",
"max_steps": 5,
"max_trials": 5
},
"hyperparameters": {
"num_units": det.Integer(64, 256),
"dropout": det.Double(0.0, 0.5),
"activation": det.Categorical(["relu", "tanh", "sigmoid"]),
"global_batch_size": 32,
},
}
###############################################################################
#
# First, configure the ``hyperparameters`` field in the experiment
# configuration to set up a search space of hyperparameters.
# :py:func:`determined.Integer`, :py:func:`determined.Double`,
# :py:func:`determined.Categorical`, and :py:func:`determined.Log` are utility
# functions for specifying distributions.
#
# Next, use the ``searcher`` field to configure the desired hyperparameter
# search algorithm. In this case, we're configuring a
parser.add_argument("--mode",
dest="mode",
help="Specifies local mode or cluster mode.",
default="cluster")
parser.add_argument(
"--use-fit",
action="store_true",
help="If true, uses model.fit() instead of model.fit_generator()",
)
args = parser.parse_args()
config = {
"hyperparameters": {
"global_batch_size": det.Constant(value=32),
"kernel_size": det.Constant(value=3),
"dropout": det.Double(minval=0.0, maxval=0.5),
"activation": det.Constant(value="relu"),
},
"searcher": {
"name": "single",
"metric": "val_accuracy",
"max_steps": 40
},
}
config.update(json.loads(args.config))
context = keras.init(config,
mode=experimental.Mode(args.mode),
context_dir=str(pathlib.Path.cwd()))
(x_train, y_train), (x_test, y_test) = load_mnist_data()
default="{}",
)
parser.add_argument("--mode",
dest="mode",
help="Specifies local mode or cluster mode.",
default="cluster")
args = parser.parse_args()
config = {
"data": {
"url":
"https://s3-us-west-2.amazonaws.com/determined-ai-test-data/pytorch_mnist.tar.gz"
},
"hyperparameters": {
"learning_rate": det.Log(minval=-3.0, maxval=-1.0, base=10),
"dropout": det.Double(minval=0.2, maxval=0.8),
"global_batch_size": det.Constant(value=64),
"n_filters1": det.Constant(value=32),
"n_filters2": det.Constant(value=32),
},
"searcher": {
"name": "single",
"metric": "validation_error",
"max_steps": 20,
"smaller_is_better": True,
},
}
config.update(json.loads(args.config))
experimental.create(
trial_def=model_def.MNistTrial,
action="store_true",
help="Specifies local mode")
parser.add_argument("--test",
action="store_true",
help="Specifies test mode")
args = parser.parse_args()
config = {
"data": {
"url":
"https://s3-us-west-2.amazonaws.com/determined-ai-test-data/pytorch_mnist.tar.gz"
},
"hyperparameters": {
"learning_rate": det.Log(minval=-3.0, maxval=-1.0, base=10),
"global_batch_size": det.Constant(value=64),
"dropout1": det.Double(minval=0.2, maxval=0.8),
"dropout2": det.Double(minval=0.2, maxval=0.8),
"n_filters1": det.Constant(value=32),
"n_filters2": det.Constant(value=32),
},
"searcher": {
"name": "single",
"metric": "validation_loss",
"max_length": {
"batches": 2000,
},
"smaller_is_better": True,
},
}
config.update(json.loads(args.config))