From 3e98b09f9649a3ba8457f0e8b3e78987a6cf8d94 Mon Sep 17 00:00:00 2001
From: Dominik Jain <djain@gmx.net>
Date: Thu, 28 Sep 2023 00:21:36 +0200
Subject: [PATCH] Adapted to changed evaluation classes

---
 notebooks/neural_networks.ipynb | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/notebooks/neural_networks.ipynb b/notebooks/neural_networks.ipynb
index 02f2fad4..1e2a9bfe 100644
--- a/notebooks/neural_networks.ipynb
+++ b/notebooks/neural_networks.ipynb
@@ -2,16 +2,16 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# Neural Networks\n",
     "\n",
     "Neural networks being a very powerful class of models, especially in cases where the learning of representations from low-level information (such as pixels, audio samples or text) is key, sensAI provides many useful abstractions for dealing with this class of models, facilitating data handling, learning and evaluation.\n",
     "\n",
     "sensAI mainly provides abstractions for PyTorch, but there is also rudimentary support for TensorFlow."
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "code",
@@ -137,8 +137,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "evaluator_params = sensai.evaluation.VectorClassificationModelEvaluatorParams(fractional_split_test_fraction=0.2)\n",
-    "eval_util = sensai.evaluation.ClassificationEvaluationUtil(mnistIoData, evaluator_params=evaluator_params)"
+    "evaluator_params = sensai.evaluation.ClassificationEvaluatorParams(fractional_split_test_fraction=0.2)\n",
+    "eval_util = sensai.evaluation.ClassificationModelEvaluation(mnistIoData, evaluator_params=evaluator_params)"
    ]
   },
   {
@@ -286,7 +286,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this case, we derived the class from ``RuleBasedTensorised`` rather than ``Tensoriser``, because our tensoriser does not require fitting. We additionally took care of the normalisation.\n",
+    "In this case, we derived the class from ``RuleBasedTensoriser`` rather than ``Tensoriser``, because our tensoriser does not require fitting. We additionally took care of the normalisation.\n",
     "\n",
     "Now we have all we need to create a sensAI ``TorchVectorClassificationModel`` that will work on the input/output data we loaded earlier."
    ]
@@ -486,7 +486,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.13"
+   "version": "3.8.18"
   },
   "orig_nbformat": 4
  },