0.10.30

netlightregresssion2

Author: bartzbeielstein

notebooks/00_spotPython_tests.ipynb

@@ -2442,6 +2442,47 @@
         "len(data_module.data_predict)"
       ]
     },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def generate_div2_list(n, n_min):\n",
+        "    result = []\n",
+        "    current = n\n",
+        "    while current >= n_min:\n",
+        "        result.extend([current] * (n // current))\n",
+        "        current = current // 2\n",
+        "    return result"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "[400, 200, 200, 100, 100, 100, 100, 50, 50, 50, 50, 50, 50, 50, 50]"
+            ]
+          },
+          "execution_count": 28,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "_L_in = 128\n",
+        "l1 = \n",
+        "\n",
+        "n_low = _L_in // 4\n",
+        "# ensure that n_high is larger than n_low\n",
+        "n_high = max(l1, 2 * n_low)\n",
+        "generate_div2_list(n_high, n_low)"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "spotPython"
-version = "0.10.29"
+version = "0.10.30"
 authors = [
   { name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
 ]

src/spotPython/hyperdict/light_hyper_dict.json

@@ -92,6 +92,103 @@
             "upper": 2
         }
     },
+    "NetLightRegression2": {
+        "l1": {
+            "type": "int",
+            "default": 3,
+            "transform": "transform_power_2_int",
+            "lower": 3,
+            "upper": 8
+        },
+        "epochs": {
+            "type": "int",
+            "default": 4,
+            "transform": "transform_power_2_int",
+            "lower": 4,
+            "upper": 9
+        },
+        "batch_size": {
+            "type": "int",
+            "default": 4,
+            "transform": "transform_power_2_int",
+            "lower": 1,
+            "upper": 4
+        },
+        "act_fn": {
+            "levels": [
+                "Sigmoid",
+                "Tanh",
+                "ReLU",
+                "LeakyReLU",
+                "ELU",
+                "Swish"
+            ],
+            "type": "factor",
+            "default": "ReLU",
+            "transform": "None",
+            "class_name": "spotPython.torch.activation",
+            "core_model_parameter_type": "instance()",
+            "lower": 0,
+            "upper": 5
+        },
+        "optimizer": {
+            "levels": [
+                "Adadelta",
+                "Adagrad",
+                "Adam",
+                "AdamW",
+                "SparseAdam",
+                "Adamax",
+                "ASGD",
+                "NAdam",
+                "RAdam",
+                "RMSprop",
+                "Rprop",
+                "SGD"
+            ],
+            "type": "factor",
+            "default": "SGD",
+            "transform": "None",
+            "class_name": "torch.optim",
+            "core_model_parameter_type": "str",
+            "lower": 0,
+            "upper": 11
+        },
+        "dropout_prob": {
+            "type": "float",
+            "default": 0.01,
+            "transform": "None",
+            "lower": 0.0,
+            "upper": 0.25
+        },
+        "lr_mult": {
+            "type": "float",
+            "default": 1.0,
+            "transform": "None",
+            "lower": 0.1,
+            "upper": 10.0
+        },
+        "patience": {
+            "type": "int",
+            "default": 2,
+            "transform": "transform_power_2_int",
+            "lower": 2,
+            "upper": 6
+        },
+        "initialization": {
+            "levels": [
+                "Default",
+                "Kaiming",
+                "Xavier"
+            ],
+            "type": "factor",
+            "default": "Default",
+            "transform": "None",
+            "core_model_parameter_type": "str",
+            "lower": 0,
+            "upper": 2
+        }
+    },
     "NetLightRegression": {
         "l1": {
             "type": "int",