Fixed minor bug in storm

deep_autoviml/__version__.py

@@ -20,6 +20,6 @@
 __author__ = "Ram Seshadri"
 __description__ = "deep_autoviml - build and test multiple Tensorflow 2.0 models and pipelines"
 __url__ = "https://github.com/Auto_ViML/deep_autoviml.git"
-__version__ = "0.0.81"
+__version__ = "0.0.82"
 __license__ = "Apache License 2.0"
 __copyright__ = "2020-21 Google"

deep_autoviml/modeling/train_custom_model.py

@@ -234,7 +234,7 @@ def build_model_storm(hp, *args):
         batch_size = args[0]
         batch_size = hp.Param('batch_size', [batch_size])
     else:
-        batch_size = hp.Param('batch_size', [64])
+        batch_size = hp.Param('batch_size', [32, 64, 128, 256, 512, 1024, 2048])
 
     num_layers = hp.Param('num_layers', [1, 2, 3], ordered=True)
     ##### Now let us build the model body ###############
@@ -243,8 +243,7 @@ def build_model_storm(hp, *args):
     # example of model-wide unordered categorical parameter
     activation_fn = hp.Param('activation', ['relu', 'selu', 'elu'])
     use_bias = hp.Param('use_bias', [True, False])
-    #weight_decay = hp.Param("weight_decay", np.logspace(-8, -3))
-    weight_decay = trial.suggest_float("weight_decay", 1e-8, 1e-3, log=True)
+    weight_decay = hp.Param("weight_decay", np.logspace(-8, -3, 10))
     #weight_decay = hp.Param("weight_decay", [1e-8, 1e-7,1e-6, 1e-5,1e-4])
 
     batch_norm = hp.Param("batch_norm", [True, False])

setup.py

@@ -20,7 +20,7 @@
 
 setuptools.setup(
     name="deep_autoviml",
-    version="0.0.81",
+    version="0.0.82",
     author="Ram Seshadri",
     # author_email="[email protected]",
     description="Automatically Build Deep Learning Models and Pipelines fast!",