hw2: done download switch

as2_Maggioni_Claudio/download.sh

@@ -0,0 +1,7 @@
+#!/bin/sh
+
+cd deliverable
+/usr/bin/curl -o models.tar.gz "https://drive.switch.ch/index.php/s/F0ubFgS6PBy8UM5/download"
+/usr/bin/tar xzvf models.tar.gz
+rm models.tar.gz
+cd ..

as2_Maggioni_Claudio/report_Maggioni_Claudio.tex

@@ -86,6 +86,23 @@
 
 \begin{document}
 
+\section*{Note on model download from SWITCHdrive}
+
+Due to space constraints, all the neural network models in this assigment were saved and uploaded on 
+SWITCHdrive. To download them, make sure you have \texttt{curl}, \texttt{gzip}, and \texttt{tar} 
+(all installed by default on MacOS) and then run:
+
+\begin{verbatim}
+./download.sh
+\end{verbatim}
+
+having as current working directory (pwd) the root assigment directory \\ \texttt{as2\_Maggioni\_Claudio}.
+
+The script will download automatically the models. 
+
+Then, in order to run the models with the \texttt{run\_task*.py}, make sure you first \texttt{cd} in the 
+\texttt{deliverable} directory.
+
 \maketitle
 
 In this assignment you are asked to:
@@ -148,11 +165,11 @@ Implement a multi-class classifier to identify the subject of the images from \h
 
 The network model was built and trained according to the given specification.
 
-The performance on the given test set is of $0.3649$ loss and $86.4\%$ accuracy. In order to assess performance on new and unseen images
+The performance on the given test set is of $0.3879$ loss and $85.63\%$ accuracy. In order to assess performance on new and unseen images
 a statistical confidence interval is necessary. Since the accuracy is by construction a binomial measure (since an image can either be correctly
 classified or not, and we repeat this Bernoulli process for each test set datapoint), we perform a binomial distribution confidence interval computation
 for 95\% confidence. The code use to do this is found in the notebook \texttt{src/Assignment 2.ipynb} under the section \textit{Statistical tests on CIFAR classifier}.
-We conclude stating that with 95\% confidence the accuracy for new and unseen images will fall between $\approx 85.12\%$ and $\approx 87.59\%$.
+We conclude stating that with 95\% confidence the accuracy for new and unseen images will fall between $\approx 84.29\%$ and $\approx 86.82\%$.
 
 The training and validation accuracy curves for the network is shown below:
 

as2_Maggioni_Claudio/src/Assignment 2.ipynb

@@ -646,7 +646,7 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "0pNBVGIh5NKd",
-        "outputId": "1fcd7c60-f848-4890-cf6f-56936a352286"
+        "outputId": "2f973b0b-5d41-4b04-e1ec-e55cd349f732"
       },
       "source": [
         "# Compute confidence interval for accuracy using binomial distribution\n",
@@ -654,24 +654,24 @@
         "import scipy.stats as st\n",
         "from statsmodels.stats.proportion import proportion_confint \n",
         "\n",
-        "test_accuracy = 0.864\n",
+        "test_accuracy = 0.856333315372467\n",
-        "n = len(y_test)\n",
+        "n = 3000\n",
         "\n",
         "proportion_confint(test_accuracy * n, n, method='binom_test', alpha=0.05)"
       ],
-      "execution_count": 16,
+      "execution_count": 2,
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "(0.8512018385349547, 0.8758694331900033)"
+              "(0.8428686580662224, 0.8682027279962743)"
             ]
           },
           "metadata": {
             "tags": []
           },
-          "execution_count": 16
+          "execution_count": 2
         }
       ]
     },