v0.0.68 New torch classes

Author: bartzbeielstein

notebooks/11_spot_hpt_torch_fashion_mnist.ipynb

@@ -16,8 +16,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "MAX_TIME = 600\n",
-    "INIT_SIZE = 20"
+    "MAX_TIME = 1\n",
+    "INIT_SIZE = 5\n",
+    "DEVICE = \"cpu\" # \"cuda:0\""
    ]
   },
   {
@@ -43,7 +44,7 @@
    "metadata": {},
    "source": [
     "# Chapter 11: Sequential Parameter Optimization\n",
-    "## Hyperparameter Tuning: pytorch with fashionMNIST Data "
+    "## Hyperparameter Tuning: pytorch with fashionMNIST Data Using Hold-out Data Sets"
    ]
   },
   {
@@ -133,6 +134,7 @@
     "    iterate_dict_values,\n",
     ")\n",
     "\n",
+    "from spotPython.torch.traintest import evaluate_cv, evaluate_hold_out\n",
     "from spotPython.utils.convert import class_for_name\n",
     "from spotPython.utils.eda import (\n",
     "    get_stars,\n",
@@ -149,7 +151,7 @@
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "# Neural Net specific imports:\n",
-    "from spotPython.torch.netcvfashionMNIST import Net_CV_fashionMNIST"
+    "from spotPython.torch.netfashionMNIST import Net_fashionMNIST"
    ]
   },
   {
@@ -281,13 +283,29 @@
     "## 3. Select `algorithm` and `core_model_hyper_dict`"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`spotPython` implements a class which is similar to the class described in the PyTorch tutorial. The class is called `Net_fashionMNIST`  and is implemented in the file `netcifar10.py`. The class is imported here.\n",
+    "\n",
+    "Note: In addition to the class Net from the PyTorch tutorial, the class Net_CIFAR10 has additional attributes, namely:\n",
+    "\n",
+    "* learning rate (`lr`),\n",
+    "* batchsize (`batch_size`),\n",
+    "* epochs (`epochs`), and\n",
+    "* k_folds (`k_folds`).\n",
+    "\n",
+    "Further attributes can be easily added to the class, e.g., `optimizer` or `loss_function`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# core_model  = RidgeCV\n",
     "core_model = Net_CV_fashionMNIST\n",
     "fun_control = add_core_model_to_fun_control(core_model=core_model,\n",
     "                              fun_control=fun_control,\n",
@@ -381,6 +399,7 @@
     "weights = 1.0\n",
     "shuffle = True\n",
     "eval = \"train_hold_out\"\n",
+    "device = DEVICE\n",
     "\n",
     "fun_control.update({\n",
     "               \"data_dir\": None,\n",
@@ -690,7 +709,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_default.evaluate_hold_out(dataset = testset, shuffle=False)"
+    "evaluate_hold_out(model_default, dataset = testset, shuffle=False)"
    ]
   },
   {
@@ -699,7 +718,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "model_spot.evaluate_hold_out(dataset = testset, shuffle=False)"
+    "evaluate_hold_out(model_spot, dataset = testset, shuffle=False)"
    ]
   },
   {