update chapter 12

doc/BookChapters/chapter12.dlog

@@ -4,3 +4,7 @@ Translating doconce text in chapter12.do.txt to ipynb
 Failed to remove ans_at_end environment
 Failed to remove sol_at_end environment
 output in chapter12.ipynb
+Translating doconce text in chapter12.do.txt to ipynb
+Failed to remove ans_at_end environment
+Failed to remove sol_at_end environment
+output in chapter12.ipynb

doc/BookChapters/chapter12.do.txt

@@ -1,4 +1,4 @@
-======= Convolutional Neural Networks (recognizing images) =======
+======= Convolutional Neural Networks  =======
 
 
 Convolutional neural networks (CNNs) were developed during the last
@@ -24,12 +24,6 @@ into the architecture. These then make the forward function more
 efficient to implement and vastly reduce the amount of parameters in
 the network._
 
-Here we provide only a superficial overview, for the more interested, we recommend highly the course
-"IN5400 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html"
-and the slides of "CS231":"http://cs231n.github.io/convolutional-networks/".
-
-Another good read is the article here URL:"https://arxiv.org/pdf/1603.07285.pdf". 
-
 
 As an example, consider
 an image of size $32\times 32\times 3$ (32 wide, 32 high, 3 color channels), so a
@@ -119,12 +113,6 @@ In summary:
 * Each Layer may or may not have parameters (e.g. CONV/FC do, RELU/POOL don’t)
 * Each Layer may or may not have additional hyperparameters (e.g. CONV/FC/POOL do, RELU doesn’t)
 
-For more material on convolutional networks, we strongly recommend
-the course
-"IN5400 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html"
-and the slides of "CS231":"http://cs231n.github.io/convolutional-networks/" which is taught at Stanford University (consistently ranked as one of the top computer science programs in the world). "Michael Nielsen's book is a must read, in particular chapter 6 which deals with CNNs":"http://neuralnetworksanddeeplearning.com/chap6.html".
-
-
 
 
 ===== CNNs in more detail, building convolutional neural networks in Tensorflow and Keras =====

doc/LectureNotes/_build/jupyter_execute/chapter1.ipynb

@@ -441,9 +441,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.59542395e+00 -1.76635856e-01  6.09517402e-01  7.37482326e-01\n",
-      "  1.23310910e+00  8.39779531e-04 -1.09487746e+00  8.32655571e-01\n",
-      " -8.22808167e-01  9.71128130e-01]\n"
+      "[ 0.11326843  1.70742862 -0.9607646   0.92342746 -0.89435001 -1.23982628\n",
+      " -0.44141207  0.2720779  -0.57697981 -0.24974585]\n"
      ]
     }
    ],

doc/LectureNotes/_build/jupyter_execute/chapter10.ipynb

@@ -2322,52 +2322,48 @@
       "Learning rate  =  0.1\n",
       "Lambda =  1.0\n",
       "Accuracy score on test set:  0.9055555555555556\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  0.1\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.8666666666666667\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.09166666666666666\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.11944444444444445\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.001\n",
       "Accuracy score on test set:  0.1361111111111111\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  1.0\n",
       "Lambda =  0.01\n",
       "Accuracy score on test set:  0.1527777777777778\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  0.1\n",
       "Accuracy score on test set:  0.16666666666666666\n",
-      "\n",
-      "Learning rate  =  1.0\n",
-      "Lambda =  1.0\n",
-      "Accuracy score on test set:  0.1111111111111111\n",
       "\n"
      ]
     },
@@ -2375,10 +2371,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "Learning rate  =  1.0\n",
+      "Lambda =  1.0\n",
+      "Accuracy score on test set:  0.1111111111111111\n",
+      "\n",
       "Learning rate  =  1.0\n",
       "Lambda =  10.0\n",
       "Accuracy score on test set:  0.05\n",
-      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Learning rate  =  10.0\n",
       "Lambda =  1e-05\n",
       "Accuracy score on test set:  0.08888888888888889\n",
@@ -2386,19 +2392,19 @@
       "Learning rate  =  10.0\n",
       "Lambda =  0.0001\n",
       "Accuracy score on test set:  0.08611111111111111\n",
-      "\n"
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.001\n",
+      "Accuracy score on test set:  0.08888888888888889\n",
+      "\n",
+      "Learning rate  =  10.0\n",
+      "Lambda =  0.01\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.001\n",
-      "Accuracy score on test set:  0.08888888888888889\n",
-      "\n",
-      "Learning rate  =  10.0\n",
-      "Lambda =  0.01\n",
       "Accuracy score on test set:  0.08888888888888889\n",
       "\n",
       "Learning rate  =  10.0\n",

doc/LectureNotes/_toc.yml

@@ -32,3 +32,4 @@
   - file: chapter9.ipynb
   - file: chapter10.ipynb
   - file: chapter11.ipynb
+  - file: chapter12.ipynb