Adding updated custom encode_dim loss decorator

Author: DiogenesAnalytics

notebooks/tuning/minndae.ipynb

@@ -113,11 +113,11 @@
     "    # create model\n",
     "    autoencoder = MinNDAE(config)\n",
     "    \n",
-    "    # get custom loss func\n",
-    "    loss_function = build_encode_dim_loss_function(encode_dim, regularization_factor=REG_FACTOR)\n",
+    "    # get custom loss func using decorator factory\n",
+    "    custom_loss = build_encode_dim_loss_function(encode_dim, regularization_factor=REG_FACTOR)()\n",
     "    \n",
     "    # select loss function\n",
-    "    autoencoder.compile(optimizer=\"adam\", loss=loss_function)\n",
+    "    autoencoder.compile(optimizer=\"adam\", loss=custom_loss)\n",
     "\n",
     "    # now return keras model\n",
     "    return autoencoder.model"
@@ -143,15 +143,15 @@
    "source": [
     "# get hyperparam tools\n",
     "from keras.callbacks import EarlyStopping\n",
-    "from keras_tuner import GridSearch\n",
+    "from keras_tuner import RandomSearch\n",
     "\n",
     "# setup tuner\n",
-    "tuner = GridSearch(\n",
+    "tuner = RandomSearch(\n",
     "    build_autoencoder,\n",
     "    objective=\"val_loss\",\n",
-    "    max_trials=50,\n",
+    "    max_trials=100,\n",
     "    directory=\"autoencoder_tuning/minndae\",\n",
-    "    project_name=f\"grid_search_encode_dim_{REG_FACTOR}_reg\",\n",
+    "    project_name=f\"random_search_encode_dim_{REG_FACTOR}_reg\",\n",
     "    seed=42,\n",
     ")\n",
     "\n",
@@ -162,7 +162,7 @@
     "tuner.search_space_summary()\n",
     "\n",
     "# run the hyperparameter search\n",
-    "tuner.search(train_ds, epochs=10, validation_data=test_ds, callbacks=[stop_early])"
+    "tuner.search(train_ds, epochs=3, validation_data=test_ds, callbacks=[stop_early])"
    ]
   },
   {