add hack-a-round for language specified markdown code blocks

When language is specified in a markdown code block such as:

````
```c
    printf("Hello, world!\n");
```
````

is used, the resulting HTML as produced by `pandoc(1)` combined
with our revised `ioccc.css` is not intent the code block well.

A temporary "_hack-a-round_" is to turn the "markdown code language
specifier" into an HTML comment.  For example:

````
```<!---c-->
    printf("Hello, world!\n");
```
````

This commits turns off existing markdown code language specifiers
for now, while preserving our ability to restore them in the future
when a better  "_hack-a-round_" to the "markdown code language
specifier" problem is found.

The HTML impact if this "_hack-a-round_" is to produce HTML that
looks like this:

```
<pre class="&lt;!---c--&gt;"><code> .. </code></pre>
```

We might be able to create a complex set of classes for each markdown
code language (there won't be very many) in `ioccc.css`, or we might
find a more general CSS (or maybe `pandoc(1)`?) solution.

For now, this hack-a-round will have to do.

2019/mills/README.md

@@ -1,6 +1,6 @@
 ## To build:
 
-```sh
+```<!---sh-->
     make
 ```
 
@@ -18,7 +18,7 @@ For more detailed information see [2019 mills bugs](../../bugs.html#2019_mills).
 
 ## To use:
 
-```sh
+```<!---sh-->
     make cpclean
     # Let this run for about about an hour and then kill it:
     ./prog Shakespeare.txt
@@ -27,7 +27,7 @@ For more detailed information see [2019 mills bugs](../../bugs.html#2019_mills).
 
 ## Try:
 
-```sh
+```<!---sh-->
     ./try.sh
 ```
 
@@ -92,19 +92,19 @@ OMLET has three operating modes:
 For your first OMLET experiment, we will try training a simple single-level
 RNN to write some [Shakespeare][5] plays.  Start by typing
 
-```sh
+```<!---sh-->
     make
 ```
 
 After it builds, train it using
 
-```sh
+```<!---sh-->
     ./prog Shakespere.txt
 ```
 
 This will immediately start outputting gibberish to the output, e.g.
 
-```sh
+```<!---sh-->
     ./prog Shakespere.txt
 ```
 
@@ -259,7 +259,7 @@ doing `Control-C`.
 You can continue training from a previous checkpoint by providing the
 checkpoint file name as the second parameter, for example:
 
-```sh
+```<!---sh-->
     ./prog Shakespeare.txt cp01_1.970
 ```
 
@@ -275,7 +275,7 @@ parameters to generate data.  Inference mode takes the checkpoint file as
 *standard input* (not on the command line) and hence must be run with a
 command like
 
-```sh
+```<!---sh-->
     ./prog < cp55_1.807
 ```
 
@@ -294,7 +294,7 @@ The default network for OMLET (the one you get if you type `make` with the
 executable name `prog`) is the simplest recurrent neural network.  It looks
 like
 
-```c
+```<!---c-->
     h = tanh(Wxh * x + Whh * h' + Bh)
     y = Why * h + By
 ```
@@ -317,26 +317,26 @@ It is the presence of the hidden state vector that allows the RNN to
 "remember" the past.  We can see what would happen if we removed this hidden
 state.  If you type
 
-```sh
+```<!---sh-->
     make lin1
 ```
 
 OMLET will create an [ADALINE][10] network that does
 
-```c
+```<!---c-->
     y = Wxy * x + By
 ```
 
 This is a simple [linear][11] [feed-forward network][12].  You can run it with
 
-```sh
+```<!---sh-->
     ./lin1 Shakespeare.txt
 ```
 
 The linear network won't be able to get past the gibberish stage, because it
 lacks history:
 
-```sh
+```<!---sh-->
     ./lin1 Shakespeare.txt
 ```
 
@@ -388,7 +388,7 @@ the language but `per` with the digit 1).
 We can try to improve the RNN's performance by stacking RNN modules atop each
 other:
 
-```c
+```<!---c-->
     h1 = RNN(h1', x)
     h2 = RNN(h2', h1)
     y = Why * h2 + By
@@ -414,13 +414,13 @@ the hard limit with `limit -h stacksize` and set it with
 
 You can try the deeper network by doing
 
-```sh
+```<!---sh-->
     make rnn2
 ```
 
 (or even `make rnn3` if you want a three-layer RNN) and train it with
 
-```sh
+```<!---sh-->
     ./rnn2 Shakespeare.txt
 ```
 
@@ -444,13 +444,13 @@ gradient, worsening the exploding and vanishing gradient problem.
 solve this problem.  Christopher Olah gives a good description of them
 at his [blog posting][20].  You can build a two-level LSTM by doing
 
-```sh
+```<!---sh-->
     make lstm2
 ```
 
 and train with it with
 
-```sh
+```<!---sh-->
     ./lstm2 Shakespeare.txt
 ```
 
@@ -464,7 +464,7 @@ its hidden state via "gates".  These gates are called
 
 The basic LSTM equations are
 
-```c
+```<!---c-->
     f = sigmoid(Wxf * x + Whf * h' + Bf)
     i = sigmoid(Wxi * x + Whi * h' + Bi)
     o = sigmoid(Wxo * x + Who * h' + Bo)
@@ -496,7 +496,7 @@ simplified versions of an LSTM which combine the gates together, meaning they
 require fewer learned parameters.  This allows them to train faster than a
 generic LSTM.  You can build a two-layer GRU with
 
-```sh
+```<!---sh-->
     make gru2
 ```
 
@@ -510,7 +510,7 @@ compiler's command-line by using `-D` directives.  The network is defined by
 a `-DNW='...'` command which consists of a series of comma-separated
 assignments.  For example, the simple one-layer RNN could be defined like
 
-```make
+```<!---make-->
     -DNW=' x  = I(n), hp = I(128),                  \
 	   h  = C(hp, T(A(L(128, x), L(128, hp)))), \
 	   y  = L(n, h)'
@@ -555,7 +555,7 @@ training mode, this is used to generate the loss which is backpropagated.
 As an example of a more complicated network, we can look at a two-layer
 GRU network:
 
-```make
+```<!---make-->
     -DHS=128,                                 \
     -DNW=' x  = I(n),                         \
 	   y  = L(n, MD(MD(x)))'              \

2019/mills/index.html

@@ -141,17 +141,17 @@ Location: <a href="../../location.html#US">US</a> - <em>United States of America
 <!-- START: this line starts content for HTML phase 21 by: bin/pandoc-wrapper.sh via bin/md2html.sh -->
 
 <h2 id="to-build">To build:</h2>
-<div class="sourceCode" id="cb1"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span></span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make</code></pre>
 <h3 id="bugs-and-misfeatures">Bugs and (Mis)features:</h3>
 <p>The current status of this entry is:</p>
 <pre><code>    STATUS: INABIAF - please **DO NOT** fix</code></pre>
 <p>For more detailed information see <a href="../../bugs.html#2019_mills">2019 mills bugs</a>.</p>
 <h2 id="to-use">To use:</h2>
-<div class="sourceCode" id="cb3"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span> cpclean</span>
-<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a>    <span class="co"># Let this run for about about an hour and then kill it:</span></span>
-<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./prog</span> Shakespeare.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make cpclean
+    # Let this run for about about an hour and then kill it:
+    ./prog Shakespeare.txt</code></pre>
 <h2 id="try">Try:</h2>
-<div class="sourceCode" id="cb4"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./try.sh</span></span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./try.sh</code></pre>
 <p>However, as the binary model files used to produce the output are in an
 implementation-specific format, your mileage may vary.</p>
 <h2 id="judges-remarks">Judges’ remarks:</h2>
@@ -200,11 +200,11 @@ training, but you can have even more fun by downloading some larger datasets:</p
 </ul>
 <p>For your first OMLET experiment, we will try training a simple single-level
 RNN to write some <a href="https://en.wikipedia.org/wiki/William_Shakespeare">Shakespeare</a> plays. Start by typing</p>
-<div class="sourceCode" id="cb5"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span></span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make</code></pre>
 <p>After it builds, train it using</p>
-<div class="sourceCode" id="cb6"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./prog</span> Shakespere.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./prog Shakespere.txt</code></pre>
 <p>This will immediately start outputting gibberish to the output, e.g.</p>
-<div class="sourceCode" id="cb7"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./prog</span> Shakespere.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./prog Shakespere.txt</code></pre>
 <p>produces:</p>
 <pre><code>    sins ohennAu
     T-teooclelp tiThoWy
@@ -331,7 +331,7 @@ helpful if you need to stop and restart training. You can stop training by
 doing <code>Control-C</code>.</p>
 <p>You can continue training from a previous checkpoint by providing the
 checkpoint file name as the second parameter, for example:</p>
-<div class="sourceCode" id="cb13"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./prog</span> Shakespeare.txt cp01_1.970</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./prog Shakespeare.txt cp01_1.970</code></pre>
 <p>After the validation cycle finishes, OMLET begins the next epoch by restarting
 training at the beginning of the training set. Training continues forever,
 until you quit it with <code>Control-C</code>. You should monitor the checkpoints to
@@ -342,7 +342,7 @@ OMLET to run the network in inference mode which uses the frozen checkpoint
 parameters to generate data. Inference mode takes the checkpoint file as
 <em>standard input</em> (not on the command line) and hence must be run with a
 command like</p>
-<div class="sourceCode" id="cb14"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb14-1"><a href="#cb14-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./prog</span> <span class="op">&lt;</span> cp55_1.807</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./prog &lt; cp55_1.807</code></pre>
 <p>Running it produces an infinite amount of generated output, until you hit
 <code>Control-C</code> to stop it.</p>
 <p>Note that if you decide to change networks or use a different input file, you
@@ -353,8 +353,8 @@ cause a crash.</p>
 <p>The default network for OMLET (the one you get if you type <code>make</code> with the
 executable name <code>prog</code>) is the simplest recurrent neural network. It looks
 like</p>
-<div class="sourceCode" id="cb15"><pre class="sourceCode c"><code class="sourceCode c"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>    h <span class="op">=</span> tanh<span class="op">(</span>Wxh <span class="op">*</span> x <span class="op">+</span> Whh <span class="op">*</span> h<span class="ch">&#39; </span><span class="er">+ Bh)</span></span>
-<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> Why <span class="op">*</span> h <span class="op">+</span> By</span></code></pre></div>
+<pre class="&lt;!---c--&gt;"><code>    h = tanh(Wxh * x + Whh * h&#39; + Bh)
+    y = Why * h + By</code></pre>
 <p>where</p>
 <ul>
 <li><code>x</code> is the <em>input vector</em></li>
@@ -371,14 +371,14 @@ the loss of the network across the training set.</p>
 <p>It is the presence of the hidden state vector that allows the RNN to
 “remember” the past. We can see what would happen if we removed this hidden
 state. If you type</p>
-<div class="sourceCode" id="cb16"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb16-1"><a href="#cb16-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span> lin1</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make lin1</code></pre>
 <p>OMLET will create an <a href="https://en.wikipedia.org/wiki/ADALINE">ADALINE</a> network that does</p>
-<div class="sourceCode" id="cb17"><pre class="sourceCode c"><code class="sourceCode c"><span id="cb17-1"><a href="#cb17-1" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> Wxy <span class="op">*</span> x <span class="op">+</span> By</span></code></pre></div>
+<pre class="&lt;!---c--&gt;"><code>    y = Wxy * x + By</code></pre>
 <p>This is a simple <a href="https://en.wikipedia.org/wiki/Linear_map">linear</a> <a href="https://en.wikipedia.org/wiki/Feedforward_neural_network">feed-forward network</a>. You can run it with</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb18-1"><a href="#cb18-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./lin1</span> Shakespeare.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./lin1 Shakespeare.txt</code></pre>
 <p>The linear network won’t be able to get past the gibberish stage, because it
 lacks history:</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb19-1"><a href="#cb19-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./lin1</span> Shakespeare.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./lin1 Shakespeare.txt</code></pre>
 <p>produces:</p>
 <pre><code>    UERond w,
 
@@ -418,9 +418,9 @@ the language but <code>per</code> with the digit 1).</p>
 <h3 id="going-deeper">Going deeper:</h3>
 <p>We can try to improve the RNN’s performance by stacking RNN modules atop each
 other:</p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode c"><code class="sourceCode c"><span id="cb21-1"><a href="#cb21-1" aria-hidden="true" tabindex="-1"></a>    h1 <span class="op">=</span> RNN<span class="op">(</span>h1<span class="ch">&#39;,</span><span class="er"> x)</span></span>
-<span id="cb21-2"><a href="#cb21-2" aria-hidden="true" tabindex="-1"></a>    h2 <span class="op">=</span> RNN<span class="op">(</span>h2<span class="ch">&#39;,</span><span class="er"> h1)</span></span>
-<span id="cb21-3"><a href="#cb21-3" aria-hidden="true" tabindex="-1"></a>    y <span class="op">=</span> Why <span class="op">*</span> h2 <span class="op">+</span> By</span></code></pre></div>
+<pre class="&lt;!---c--&gt;"><code>    h1 = RNN(h1&#39;, x)
+    h2 = RNN(h2&#39;, h1)
+    y = Why * h2 + By</code></pre>
 <p>with <code>RNN(h, x)</code> defined as above. Each RNN module has its own set of
 parameters and its own hidden state vector. This will improve the network’s
 performance, at the cost of a much larger parameter space.</p>
@@ -436,9 +436,9 @@ the hard limit with <code>limit -h stacksize</code> and set it with
 <code>limit stacksize 65532</code> (replacing <code>65532</code> with the actual hard limit).</p>
 <hr />
 <p>You can try the deeper network by doing</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb22-1"><a href="#cb22-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span> rnn2</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make rnn2</code></pre>
 <p>(or even <code>make rnn3</code> if you want a three-layer RNN) and train it with</p>
-<div class="sourceCode" id="cb23"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb23-1"><a href="#cb23-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./rnn2</span> Shakespeare.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./rnn2 Shakespeare.txt</code></pre>
 <p>The additional depth should allow the network to make better predictions (it
 can represent more complicated history), but it may take a long time to train
 -- both because the network (being larger) now requires more time to train and
@@ -454,9 +454,9 @@ gradient, worsening the exploding and vanishing gradient problem.</p>
 <p><a href="https://en.wikipedia.org/wiki/Long_short-term_memory">Long Short Term Memory</a> networks (also called LSTMs) were developed to
 solve this problem. Christopher Olah gives a good description of them
 at his <a href="http://colah.github.io/posts/2015-08-Understanding-LSTMs/">blog posting</a>. You can build a two-level LSTM by doing</p>
-<div class="sourceCode" id="cb24"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb24-1"><a href="#cb24-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span> lstm2</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make lstm2</code></pre>
 <p>and train with it with</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb25-1"><a href="#cb25-1" aria-hidden="true" tabindex="-1"></a>    <span class="ex">./lstm2</span> Shakespeare.txt</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    ./lstm2 Shakespeare.txt</code></pre>
 <p>The LSTM is much easier to train because it explicitly decides how to update
 its hidden state via “gates”. These gates are called</p>
 <ul>
@@ -466,11 +466,11 @@ its hidden state via “gates”. These gates are called</p>
 produce the output</li>
 </ul>
 <p>The basic LSTM equations are</p>
-<div class="sourceCode" id="cb26"><pre class="sourceCode c"><code class="sourceCode c"><span id="cb26-1"><a href="#cb26-1" aria-hidden="true" tabindex="-1"></a>    f <span class="op">=</span> sigmoid<span class="op">(</span>Wxf <span class="op">*</span> x <span class="op">+</span> Whf <span class="op">*</span> h<span class="ch">&#39; </span><span class="er">+ Bf)</span></span>
-<span id="cb26-2"><a href="#cb26-2" aria-hidden="true" tabindex="-1"></a>    i <span class="op">=</span> sigmoid<span class="op">(</span>Wxi <span class="op">*</span> x <span class="op">+</span> Whi <span class="op">*</span> h<span class="ch">&#39; </span><span class="er">+ Bi)</span></span>
-<span id="cb26-3"><a href="#cb26-3" aria-hidden="true" tabindex="-1"></a>    o <span class="op">=</span> sigmoid<span class="op">(</span>Wxo <span class="op">*</span> x <span class="op">+</span> Who <span class="op">*</span> h<span class="ch">&#39; </span><span class="er">+ Bo)</span></span>
-<span id="cb26-4"><a href="#cb26-4" aria-hidden="true" tabindex="-1"></a>    c <span class="op">=</span> f <span class="op">*</span> c<span class="ch">&#39; </span><span class="er">+ i * tanh(Wxc * x + Whc * h</span><span class="ch">&#39;</span> <span class="op">+</span> Bc<span class="op">)</span></span>
-<span id="cb26-5"><a href="#cb26-5" aria-hidden="true" tabindex="-1"></a>    h <span class="op">=</span> o <span class="op">*</span> tanh<span class="op">(</span>c<span class="op">)</span></span></code></pre></div>
+<pre class="&lt;!---c--&gt;"><code>    f = sigmoid(Wxf * x + Whf * h&#39; + Bf)
+    i = sigmoid(Wxi * x + Whi * h&#39; + Bi)
+    o = sigmoid(Wxo * x + Who * h&#39; + Bo)
+    c = f * c&#39; + i * tanh(Wxc * x + Whc * h&#39; + Bc)
+    h = o * tanh(c)</code></pre>
 <p>Where</p>
 <ul>
 <li><code>x</code> is the input vector</li>
@@ -494,7 +494,7 @@ examples). One important one is the <a href="https://en.wikipedia.org/wiki/Gated
 simplified versions of an LSTM which combine the gates together, meaning they
 require fewer learned parameters. This allows them to train faster than a
 generic LSTM. You can build a two-layer GRU with</p>
-<div class="sourceCode" id="cb27"><pre class="sourceCode sh"><code class="sourceCode bash"><span id="cb27-1"><a href="#cb27-1" aria-hidden="true" tabindex="-1"></a>    <span class="fu">make</span> gru2</span></code></pre></div>
+<pre class="&lt;!---sh--&gt;"><code>    make gru2</code></pre>
 <h3 id="building-your-own-networks">Building your own networks</h3>
 <p>The OMLET <code>Makefile</code> comes with one-, two- and three-layer RNNs, LSTMs and
 GRUs, along with simpler feed-forward networks like multi-layer perceptrons and
@@ -503,7 +503,7 @@ own networks by modifying the <code>Makefile</code>. Networks are passed in on t
 compiler’s command-line by using <code>-D</code> directives. The network is defined by
 a <code>-DNW='...'</code> command which consists of a series of comma-separated
 assignments. For example, the simple one-layer RNN could be defined like</p>
-<pre class="make"><code>    -DNW=&#39; x  = I(n), hp = I(128),                  \
+<pre class="&lt;!---make--&gt;"><code>    -DNW=&#39; x  = I(n), hp = I(128),                  \
            h  = C(hp, T(A(L(128, x), L(128, hp)))), \
            y  = L(n, h)&#39;</code></pre>
 <p>The network declares <code>x</code> as an input vector (there must be a declaration for
@@ -538,7 +538,7 @@ inference mode, this is used to select the next character to emit. In
 training mode, this is used to generate the loss which is backpropagated.</p>
 <p>As an example of a more complicated network, we can look at a two-layer
 GRU network:</p>
-<pre class="make"><code>    -DHS=128,                                 \
+<pre class="&lt;!---make--&gt;"><code>    -DHS=128,                                 \
     -DNW=&#39; x  = I(n),                         \
            y  = L(n, MD(MD(x)))&#39;              \
     -DBK=&#39; hp = I(HS),                        \