Inicio Explorar Ayuda
Iniciar sesión
cnheider
/
draugr
espejo de https://github.com/cnheider/draugr
Seguir 1
Destacar 1
Fork 0
Archivos Incidencias 0 Wiki
Rama: master
Ramas Etiquetas
draugr
/
docs
/
github
/
html
/
generated
/
draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.html

draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.html 66 KB
Permalink Histórico Raw

123456789101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135136137138139140141142143144145146147148149150151152153154155156157158159160161162163164165166167168169170171172173174175176177178179180181182183184185186187188189190191192193194195196197198199200201202203204205206207208209210211212213214215216217218219220221222223224225226227228229230231232233234235236237238239240241242243244245246247248249250251252253254255256257258259260261262263264265266267268269270271272273274275276277278279280281282283284285286287288289290291292293294295296297298299300301302303304305306307308309310311312313314315316317318319320321322323324325326327328329330331332333334335336337338339340341342343344345346347348349350351352353354355356357358359360361362363364365366367368369370371372373374375376377378379380381382383384385386387388389390391392393394395396397398399400401402403404405406407408409410411412413414415416417418419420421422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451452453454455456457458459460461462463464465466467468469470471472473474475476477478479480481482483484485486487488489490491492493494495496497498499500501502503504505506507508509510511512513514515516517518519520521522523524525526527528529530531532533534535536537538539540541542543544545546547548549550551552553554555556557558559560561562563564565566567568569570571572573574575576577578579580581582583584585586587 
  1. 

  2. <!DOCTYPE html>
    3. 

  3. 

  4. <html lang="en">
    5. 

  5.   <head>
    6. 

  6.     <meta charset="utf-8" />
    7. 

  7.     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.17.1: http://docutils.sourceforge.net/" />
    8. 

  8. 

  9.     <title>draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU &#8212; Draugr 1.0.1 documentation</title>
    10. 

  10.     <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
    11. 

  11.     <link rel="stylesheet" type="text/css" href="../_static/alabaster.css" />
    12. 

  12.     <link rel="stylesheet" type="text/css" href="../_static/graphviz.css" />
    13. 

  13.     <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
    14. 

  14.     <script src="../_static/jquery.js"></script>
    15. 

  15.     <script src="../_static/underscore.js"></script>
    16. 

  16.     <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
    17. 

  17.     <script src="../_static/doctools.js"></script>
    18. 

  18.     <link rel="canonical" href="pything.github.io/draugr/generated/draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.html" />
    19. 

  19.     <link rel="index" title="Index" href="../genindex.html" />
    20. 

  20.     <link rel="search" title="Search" href="../search.html" />
    21. 

  21.     <link rel="next" title="draugr.torch_utilities.optimisation.debugging.layer_fetching" href="draugr.torch_utilities.optimisation.debugging.layer_fetching.html" />
    22. 

  22.     <link rel="prev" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLUModel" href="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLUModel.html" />
    23. 

  23.    
    24. 

  24.   <link rel="stylesheet" href="../_static/custom.css" type="text/css" />
    25. 

  25.   
    26. 

  26.   
    27. 

  27.   <meta name="viewport" content="width=device-width, initial-scale=0.9, maximum-scale=0.9" />
    28. 

  28. 

  29.   </head><body>
    30. 

  30.   
    31. 

  31. 

  32.     <div class="document">
    33. 

  33.       <div class="documentwrapper">
    34. 

  34.         <div class="bodywrapper">
    35. 

  35.           
    36. 

  36. 

  37.           <div class="body" role="main">
    38. 

  38.             
    39. 

  39.   <section id="draugr-torch-utilities-optimisation-debugging-gradients-guided-guidedbackproprelu">
    40. 

  40. <h1>draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU<a class="headerlink" href="#draugr-torch-utilities-optimisation-debugging-gradients-guided-guidedbackproprelu" title="Permalink to this heading">¶</a></h1>
    41. 

  41. <dl class="py class">
    42. 

  42. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU">
    43. 

  43. <em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">draugr.torch_utilities.optimisation.debugging.gradients.guided.</span></span><span class="sig-name descname"><span class="pre">GuidedBackPropReLU</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/draugr/torch_utilities/optimisation/debugging/gradients/guided.html#GuidedBackPropReLU"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU" title="Permalink to this definition">¶</a></dt>
    44. 

  44. <dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Function</span></code></p>
    45. 

  45. <dl class="py method">
    46. 

  46. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.__init__">
    47. 

  47. <span class="sig-name descname"><span class="pre">__init__</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.__init__" title="Permalink to this definition">¶</a></dt>
    48. 

  48. <dd></dd></dl>
    49. 

  49. 

  50. <p class="rubric">Methods</p>
    51. 

  51. <table class="autosummary longtable docutils align-default">
    52. 

  52. <colgroup>
    53. 

  53. <col style="width: 10%" />
    54. 

  54. <col style="width: 90%" />
    55. 

  55. </colgroup>
    56. 

  56. <tbody>
    57. 

  57. <tr class="row-odd"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.__init__" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.__init__"><code class="xref py py-obj docutils literal notranslate"><span class="pre">__init__</span></code></a>(*args, **kwargs)</p></td>
    58. 

  58. <td><p></p></td>
    59. 

  59. </tr>
    60. 

  60. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply</span></code></p></td>
    61. 

  61. <td><p></p></td>
    62. 

  62. </tr>
    63. 

  63. <tr class="row-odd"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">backward</span></code></a>(self, grad_output)</p></td>
    64. 

  64. <td><p><dl class="field-list simple">
    65. 

  65. <dt class="field-odd">param self</dt>
    66. 

  66. <dd class="field-odd"><p></p></dd>
    67. 

  67. </dl>
    68. 

  68. </p></td>
    69. 

  69. </tr>
    70. 

  70. <tr class="row-even"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">forward</span></code></a>(self, input_img)</p></td>
    71. 

  71. <td><p><dl class="field-list simple">
    72. 

  72. <dt class="field-odd">param self</dt>
    73. 

  73. <dd class="field-odd"><p></p></dd>
    74. 

  74. </dl>
    75. 

  75. </p></td>
    76. 

  76. </tr>
    77. 

  77. <tr class="row-odd"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.jvp" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.jvp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">jvp</span></code></a>(ctx, *grad_inputs)</p></td>
    78. 

  78. <td><p>Defines a formula for differentiating the operation with forward mode automatic differentiation.</p></td>
    79. 

  79. </tr>
    80. 

  80. <tr class="row-even"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_dirty" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_dirty"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mark_dirty</span></code></a>(*args)</p></td>
    81. 

  81. <td><p>Marks given tensors as modified in an in-place operation.</p></td>
    82. 

  82. </tr>
    83. 

  83. <tr class="row-odd"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_non_differentiable" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_non_differentiable"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mark_non_differentiable</span></code></a>(*args)</p></td>
    84. 

  84. <td><p>Marks outputs as non-differentiable.</p></td>
    85. 

  85. </tr>
    86. 

  86. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">mark_shared_storage</span></code>(*pairs)</p></td>
    87. 

  87. <td><p></p></td>
    88. 

  88. </tr>
    89. 

  89. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">name</span></code></p></td>
    90. 

  90. <td><p></p></td>
    91. 

  91. </tr>
    92. 

  92. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">register_hook</span></code></p></td>
    93. 

  93. <td><p></p></td>
    94. 

  94. </tr>
    95. 

  95. <tr class="row-odd"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_backward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_backward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_for_backward</span></code></a>(*tensors)</p></td>
    96. 

  96. <td><p>Saves given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p></td>
    97. 

  97. </tr>
    98. 

  98. <tr class="row-even"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_forward"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save_for_forward</span></code></a>(*tensors)</p></td>
    99. 

  99. <td><p>Saves given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p></td>
    100. 

  100. </tr>
    101. 

  101. <tr class="row-odd"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.set_materialize_grads" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.set_materialize_grads"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_materialize_grads</span></code></a>(value)</p></td>
    102. 

  102. <td><p>Sets whether to materialize output grad tensors.</p></td>
    103. 

  103. </tr>
    104. 

  104. <tr class="row-even"><td><p><a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.vjp" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.vjp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vjp</span></code></a>(ctx, *grad_outputs)</p></td>
    105. 

  105. <td><p>Defines a formula for differentiating the operation with backward mode automatic differentiation (alias to the vjp function).</p></td>
    106. 

  106. </tr>
    107. 

  107. </tbody>
    108. 

  108. </table>
    109. 

  109. <p class="rubric">Attributes</p>
    110. 

  110. <table class="autosummary longtable docutils align-default">
    111. 

  111. <colgroup>
    112. 

  112. <col style="width: 10%" />
    113. 

  113. <col style="width: 90%" />
    114. 

  114. </colgroup>
    115. 

  115. <tbody>
    116. 

  116. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">dirty_tensors</span></code></p></td>
    117. 

  117. <td><p></p></td>
    118. 

  118. </tr>
    119. 

  119. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_traceable</span></code></p></td>
    120. 

  120. <td><p></p></td>
    121. 

  121. </tr>
    122. 

  122. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">materialize_grads</span></code></p></td>
    123. 

  123. <td><p></p></td>
    124. 

  124. </tr>
    125. 

  125. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">metadata</span></code></p></td>
    126. 

  126. <td><p></p></td>
    127. 

  127. </tr>
    128. 

  128. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">needs_input_grad</span></code></p></td>
    129. 

  129. <td><p></p></td>
    130. 

  130. </tr>
    131. 

  131. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">next_functions</span></code></p></td>
    132. 

  132. <td><p></p></td>
    133. 

  133. </tr>
    134. 

  134. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">non_differentiable</span></code></p></td>
    135. 

  135. <td><p></p></td>
    136. 

  136. </tr>
    137. 

  137. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">requires_grad</span></code></p></td>
    138. 

  138. <td><p></p></td>
    139. 

  139. </tr>
    140. 

  140. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">saved_for_forward</span></code></p></td>
    141. 

  141. <td><p></p></td>
    142. 

  142. </tr>
    143. 

  143. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">saved_tensors</span></code></p></td>
    144. 

  144. <td><p></p></td>
    145. 

  145. </tr>
    146. 

  146. <tr class="row-odd"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">saved_variables</span></code></p></td>
    147. 

  147. <td><p></p></td>
    148. 

  148. </tr>
    149. 

  149. <tr class="row-even"><td><p><code class="xref py py-obj docutils literal notranslate"><span class="pre">to_save</span></code></p></td>
    150. 

  150. <td><p></p></td>
    151. 

  151. </tr>
    152. 

  152. </tbody>
    153. 

  153. </table>
    154. 

  154. <dl class="py method">
    155. 

  155. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward">
    156. 

  156. <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grad_output</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/draugr/torch_utilities/optimisation/debugging/gradients/guided.html#GuidedBackPropReLU.backward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward" title="Permalink to this definition">¶</a></dt>
    157. 

  157. <dd><dl class="field-list simple">
    158. 

  158. <dt class="field-odd">Parameters</dt>
    159. 

  159. <dd class="field-odd"><ul class="simple">
    160. 

  160. <li><p><strong>self</strong> – </p></li>
    161. 

  161. <li><p><strong>grad_output</strong> – </p></li>
    162. 

  162. </ul>
    163. 

  163. </dd>
    164. 

  164. <dt class="field-even">Returns</dt>
    165. 

  165. <dd class="field-even"><p></p>
    166. 

  166. </dd>
    167. 

  167. </dl>
    168. 

  168. </dd></dl>
    169. 

  169. 

  170. <dl class="py method">
    171. 

  171. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward">
    172. 

  172. <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">self</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">input_img</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="../_modules/draugr/torch_utilities/optimisation/debugging/gradients/guided.html#GuidedBackPropReLU.forward"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="Permalink to this definition">¶</a></dt>
    173. 

  173. <dd><dl class="field-list simple">
    174. 

  174. <dt class="field-odd">Parameters</dt>
    175. 

  175. <dd class="field-odd"><ul class="simple">
    176. 

  176. <li><p><strong>self</strong> – </p></li>
    177. 

  177. <li><p><strong>input_img</strong> – </p></li>
    178. 

  178. </ul>
    179. 

  179. </dd>
    180. 

  180. <dt class="field-even">Returns</dt>
    181. 

  181. <dd class="field-even"><p></p>
    182. 

  182. </dd>
    183. 

  183. </dl>
    184. 

  184. </dd></dl>
    185. 

  185. 

  186. <dl class="py method">
    187. 

  187. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.jvp">
    188. 

  188. <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">jvp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_inputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span></span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.jvp" title="Permalink to this definition">¶</a></dt>
    189. 

  189. <dd><p>Defines a formula for differentiating the operation with forward mode
    190. 

  190. automatic differentiation.
    191. 

  191. This function is to be overridden by all subclasses.
    192. 

  192. It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
    193. 

  193. as many inputs as the <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> got (None will be passed in
    194. 

  194. for non tensor inputs of the forward function),
    195. 

  195. and it should return as many tensors as there were outputs to
    196. 

  196. <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. Each argument is the gradient w.r.t the given input,
    197. 

  197. and each returned value should be the gradient w.r.t. the
    198. 

  198. corresponding output. If an output is not a Tensor or the function is not
    199. 

  199. differentiable with respect to that output, you can just pass None as a
    200. 

  200. gradient for that input.</p>
    201. 

  201. <p>You can use the <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> object to pass any value from the forward to this
    202. 

  202. functions.</p>
    203. 

  203. </dd></dl>
    204. 

  204. 

  205. <dl class="py method">
    206. 

  206. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_dirty">
    207. 

  207. <span class="sig-name descname"><span class="pre">mark_dirty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_dirty" title="Permalink to this definition">¶</a></dt>
    208. 

  208. <dd><p>Marks given tensors as modified in an in-place operation.</p>
    209. 

  209. <p><strong>This should be called at most once, only from inside the</strong>
    210. 

  210. <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> <strong>method, and all arguments should be inputs.</strong></p>
    211. 

  211. <p>Every tensor that’s been modified in-place in a call to <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>
    212. 

  212. should be given to this function, to ensure correctness of our checks.
    213. 

  213. It doesn’t matter whether the function is called before or after
    214. 

  214. modification.</p>
    215. 

  215. <dl>
    216. 

  216. <dt>Examples::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Inplace</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
    217. 

  217. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    218. 

  218. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    219. 

  219. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x_npy</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span> <span class="c1"># x_npy shares storage with x</span>
    220. 

  220. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x_npy</span> <span class="o">+=</span> <span class="mi">1</span>
    221. 

  221. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">mark_dirty</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    222. 

  222. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">x</span>
    223. 

  223. <span class="go">&gt;&gt;&gt;</span>
    224. 

  224. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    225. 

  225. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@once_differentiable</span>
    226. 

  226. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">):</span>
    227. 

  227. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">grad_output</span>
    228. 

  228. <span class="go">&gt;&gt;&gt;</span>
    229. 

  229. <span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
    230. 

  230. <span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="n">a</span> <span class="o">*</span> <span class="n">a</span>
    231. 

  231. <span class="gp">&gt;&gt;&gt; </span><span class="n">Inplace</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>  <span class="c1"># This would lead to wrong gradients!</span>
    232. 

  232. <span class="gp">&gt;&gt;&gt; </span>                  <span class="c1"># but the engine would not know unless we mark_dirty</span>
    233. 

  233. <span class="gp">&gt;&gt;&gt; </span><span class="n">b</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span> <span class="c1"># RuntimeError: one of the variables needed for gradient</span>
    234. 

  234. <span class="gp">&gt;&gt;&gt; </span>             <span class="c1"># computation has been modified by an inplace operation</span>
    235. 

  235. </pre></div>
    236. 

  236. </div>
    237. 

  237. </dd>
    238. 

  238. </dl>
    239. 

  239. </dd></dl>
    240. 

  240. 

  241. <dl class="py method">
    242. 

  242. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_non_differentiable">
    243. 

  243. <span class="sig-name descname"><span class="pre">mark_non_differentiable</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.mark_non_differentiable" title="Permalink to this definition">¶</a></dt>
    244. 

  244. <dd><p>Marks outputs as non-differentiable.</p>
    245. 

  245. <p><strong>This should be called at most once, only from inside the</strong>
    246. 

  246. <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> <strong>method, and all arguments should be tensor outputs.</strong></p>
    247. 

  247. <p>This will mark outputs as not requiring gradients, increasing the
    248. 

  248. efficiency of backward computation. You still need to accept a gradient
    249. 

  249. for each output in <code class="xref py py-meth docutils literal notranslate"><span class="pre">backward()</span></code>, but it’s always going to
    250. 

  250. be a zero tensor with the same shape as the shape of a corresponding
    251. 

  251. output.</p>
    252. 

  252. <dl>
    253. 

  253. <dt>This is used e.g. for indices returned from a sort. See example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Func</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
    254. 

  254. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    255. 

  255. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    256. 

  256. <span class="gp">&gt;&gt;&gt; </span>        <span class="nb">sorted</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">sort</span><span class="p">()</span>
    257. 

  257. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">mark_non_differentiable</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
    258. 

  258. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">idx</span><span class="p">)</span>
    259. 

  259. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="nb">sorted</span><span class="p">,</span> <span class="n">idx</span>
    260. 

  260. <span class="go">&gt;&gt;&gt;</span>
    261. 

  261. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    262. 

  262. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@once_differentiable</span>
    263. 

  263. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">g1</span><span class="p">,</span> <span class="n">g2</span><span class="p">):</span>  <span class="c1"># still need to accept g2</span>
    264. 

  264. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x</span><span class="p">,</span> <span class="n">idx</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
    265. 

  265. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">grad_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    266. 

  266. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">grad_input</span><span class="o">.</span><span class="n">index_add_</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">idx</span><span class="p">,</span> <span class="n">g1</span><span class="p">)</span>
    267. 

  267. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">grad_input</span>
    268. 

  268. </pre></div>
    269. 

  269. </div>
    270. 

  270. </dd>
    271. 

  271. </dl>
    272. 

  272. </dd></dl>
    273. 

  273. 

  274. <dl class="py method">
    275. 

  275. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_backward">
    276. 

  276. <span class="sig-name descname"><span class="pre">save_for_backward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_backward" title="Permalink to this definition">¶</a></dt>
    277. 

  277. <dd><p>Saves given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code>.</p>
    278. 

  278. <p><code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> should be called at most once, only from inside the
    279. 

  279. <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> method, and only with tensors.</p>
    280. 

  280. <p>All tensors intended to be used in the backward pass should be saved
    281. 

  281. with <code class="docutils literal notranslate"><span class="pre">save_for_backward</span></code> (as opposed to directly on <code class="docutils literal notranslate"><span class="pre">ctx</span></code>) to prevent
    282. 

  282. incorrect gradients and memory leaks, and enable the application of saved
    283. 

  283. tensor hooks. See <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.autograd.graph.saved_tensors_hooks</span></code>.</p>
    284. 

  284. <p>In <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a>, saved tensors can be accessed through the <code class="xref py py-attr docutils literal notranslate"><span class="pre">saved_tensors</span></code>
    285. 

  285. attribute. Before returning them to the user, a check is made to ensure
    286. 

  286. they weren’t used in any in-place operation that modified their content.</p>
    287. 

  287. <p>Arguments can also be <code class="docutils literal notranslate"><span class="pre">None</span></code>. This is a no-op.</p>
    288. 

  288. <p>See <span class="xref std std-ref">extending-autograd</span> for more details on how to use this method.</p>
    289. 

  289. <dl>
    290. 

  290. <dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Func</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
    291. 

  291. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    292. 

  292. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">z</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
    293. 

  293. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">w</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">z</span>
    294. 

  294. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">out</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">z</span> <span class="o">+</span> <span class="n">w</span>
    295. 

  295. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span><span class="p">)</span>
    296. 

  296. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">z</span>  <span class="c1"># z is not a tensor</span>
    297. 

  297. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">out</span>
    298. 

  298. <span class="go">&gt;&gt;&gt;</span>
    299. 

  299. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    300. 

  300. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_out</span><span class="p">):</span>
    301. 

  301. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">w</span><span class="p">,</span> <span class="n">out</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
    302. 

  302. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">z</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">z</span>
    303. 

  303. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">gx</span> <span class="o">=</span> <span class="n">grad_out</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span> <span class="o">+</span> <span class="n">y</span> <span class="o">*</span> <span class="n">z</span><span class="p">)</span>
    304. 

  304. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">gy</span> <span class="o">=</span> <span class="n">grad_out</span> <span class="o">*</span> <span class="p">(</span><span class="n">x</span> <span class="o">+</span> <span class="n">z</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">z</span><span class="p">)</span>
    305. 

  305. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">gz</span> <span class="o">=</span> <span class="kc">None</span>
    306. 

  306. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">gx</span><span class="p">,</span> <span class="n">gy</span><span class="p">,</span> <span class="n">gz</span>
    307. 

  307. <span class="go">&gt;&gt;&gt;</span>
    308. 

  308. <span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
    309. 

  309. <span class="gp">&gt;&gt;&gt; </span><span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
    310. 

  310. <span class="gp">&gt;&gt;&gt; </span><span class="n">c</span> <span class="o">=</span> <span class="mi">4</span>
    311. 

  311. <span class="gp">&gt;&gt;&gt; </span><span class="n">d</span> <span class="o">=</span> <span class="n">Func</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
    312. 

  312. </pre></div>
    313. 

  313. </div>
    314. 

  314. </dd>
    315. 

  315. </dl>
    316. 

  316. </dd></dl>
    317. 

  317. 

  318. <dl class="py method">
    319. 

  319. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_forward">
    320. 

  320. <span class="sig-name descname"><span class="pre">save_for_forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">tensors</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><span class="pre">Tensor</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.save_for_forward" title="Permalink to this definition">¶</a></dt>
    321. 

  321. <dd><p>Saves given tensors for a future call to <code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code>.</p>
    322. 

  322. <p><code class="docutils literal notranslate"><span class="pre">save_for_forward</span></code> should be only called once, from inside the <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>
    323. 

  323. method, and only be called with tensors.</p>
    324. 

  324. <p>In <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.jvp" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.jvp"><code class="xref py py-func docutils literal notranslate"><span class="pre">jvp()</span></code></a>, saved objects can be accessed through the <code class="xref py py-attr docutils literal notranslate"><span class="pre">saved_tensors</span></code>
    325. 

  325. attribute.</p>
    326. 

  326. <p>Arguments can also be <code class="docutils literal notranslate"><span class="pre">None</span></code>. This is a no-op.</p>
    327. 

  327. <p>See <span class="xref std std-ref">extending-autograd</span> for more details on how to use this method.</p>
    328. 

  328. <dl>
    329. 

  329. <dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Func</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">autograd</span><span class="o">.</span><span class="n">Function</span><span class="p">):</span>
    330. 

  330. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    331. 

  331. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">y</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">z</span><span class="p">:</span> <span class="nb">int</span><span class="p">):</span>
    332. 

  332. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    333. 

  333. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_forward</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    334. 

  334. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">z</span> <span class="o">=</span> <span class="n">z</span>
    335. 

  335. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y</span> <span class="o">*</span> <span class="n">z</span>
    336. 

  336. <span class="go">&gt;&gt;&gt;</span>
    337. 

  337. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    338. 

  338. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">jvp</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x_t</span><span class="p">,</span> <span class="n">y_t</span><span class="p">,</span> <span class="n">_</span><span class="p">):</span>
    339. 

  339. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
    340. 

  340. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">z</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">z</span>
    341. 

  341. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">z</span> <span class="o">*</span> <span class="p">(</span><span class="n">y</span> <span class="o">*</span> <span class="n">x_t</span> <span class="o">+</span> <span class="n">x</span> <span class="o">*</span> <span class="n">y_t</span><span class="p">)</span>
    342. 

  342. <span class="go">&gt;&gt;&gt;</span>
    343. 

  343. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    344. 

  344. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">vjp</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">grad_out</span><span class="p">):</span>
    345. 

  345. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
    346. 

  346. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">z</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">z</span>
    347. 

  347. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">z</span> <span class="o">*</span> <span class="n">grad_out</span> <span class="o">*</span> <span class="n">y</span><span class="p">,</span> <span class="n">z</span> <span class="o">*</span> <span class="n">grad_out</span> <span class="o">*</span> <span class="n">x</span><span class="p">,</span> <span class="kc">None</span>
    348. 

  348. <span class="go">&gt;&gt;&gt;</span>
    349. 

  349. <span class="gp">&gt;&gt;&gt; </span>    <span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
    350. 

  350. <span class="gp">&gt;&gt;&gt; </span>    <span class="n">t</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
    351. 

  351. <span class="gp">&gt;&gt;&gt; </span>    <span class="n">b</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">2.</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
    352. 

  352. <span class="gp">&gt;&gt;&gt; </span>    <span class="n">c</span> <span class="o">=</span> <span class="mi">4</span>
    353. 

  353. <span class="go">&gt;&gt;&gt;</span>
    354. 

  354. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">with</span> <span class="n">fwAD</span><span class="o">.</span><span class="n">dual_level</span><span class="p">():</span>
    355. 

  355. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">a_dual</span> <span class="o">=</span> <span class="n">fwAD</span><span class="o">.</span><span class="n">make_dual</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">t</span><span class="p">)</span>
    356. 

  356. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">d</span> <span class="o">=</span> <span class="n">Func</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">a_dual</span><span class="p">,</span> <span class="n">b</span><span class="p">,</span> <span class="n">c</span><span class="p">)</span>
    357. 

  357. </pre></div>
    358. 

  358. </div>
    359. 

  359. </dd>
    360. 

  360. </dl>
    361. 

  361. </dd></dl>
    362. 

  362. 

  363. <dl class="py method">
    364. 

  364. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.set_materialize_grads">
    365. 

  365. <span class="sig-name descname"><span class="pre">set_materialize_grads</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">value</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.10)"><span class="pre">bool</span></a></span></em><span class="sig-paren">)</span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.set_materialize_grads" title="Permalink to this definition">¶</a></dt>
    366. 

  366. <dd><p>Sets whether to materialize output grad tensors. Default is <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p>
    367. 

  367. <p><strong>This should be called only from inside the</strong> <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> <strong>method</strong></p>
    368. 

  368. <p>If <code class="docutils literal notranslate"><span class="pre">True</span></code>, undefined output grad tensors will be expanded to tensors full
    369. 

  369. of zeros prior to calling the <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> method.</p>
    370. 

  370. <dl>
    371. 

  371. <dt>Example::</dt><dd><div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">SimpleFunc</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
    372. 

  372. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    373. 

  373. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    374. 

  374. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
    375. 

  375. <span class="go">&gt;&gt;&gt;</span>
    376. 

  376. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    377. 

  377. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@once_differentiable</span>
    378. 

  378. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">g1</span><span class="p">,</span> <span class="n">g2</span><span class="p">):</span>
    379. 

  379. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">g1</span> <span class="o">+</span> <span class="n">g2</span>  <span class="c1"># No check for None necessary</span>
    380. 

  380. <span class="go">&gt;&gt;&gt;</span>
    381. 

  381. <span class="gp">&gt;&gt;&gt; </span><span class="c1"># We modify SimpleFunc to handle non-materialized grad outputs</span>
    382. 

  382. <span class="gp">&gt;&gt;&gt; </span><span class="k">class</span> <span class="nc">Func</span><span class="p">(</span><span class="n">Function</span><span class="p">):</span>
    383. 

  383. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    384. 

  384. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">forward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
    385. 

  385. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">set_materialize_grads</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
    386. 

  386. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">ctx</span><span class="o">.</span><span class="n">save_for_backward</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    387. 

  387. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">(),</span> <span class="n">x</span><span class="o">.</span><span class="n">clone</span><span class="p">()</span>
    388. 

  388. <span class="go">&gt;&gt;&gt;</span>
    389. 

  389. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@staticmethod</span>
    390. 

  390. <span class="gp">&gt;&gt;&gt; </span>    <span class="nd">@once_differentiable</span>
    391. 

  391. <span class="gp">&gt;&gt;&gt; </span>    <span class="k">def</span> <span class="nf">backward</span><span class="p">(</span><span class="n">ctx</span><span class="p">,</span> <span class="n">g1</span><span class="p">,</span> <span class="n">g2</span><span class="p">):</span>
    392. 

  392. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">x</span><span class="p">,</span> <span class="o">=</span> <span class="n">ctx</span><span class="o">.</span><span class="n">saved_tensors</span>
    393. 

  393. <span class="gp">&gt;&gt;&gt; </span>        <span class="n">grad_input</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
    394. 

  394. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">if</span> <span class="n">g1</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>  <span class="c1"># We must check for None now</span>
    395. 

  395. <span class="gp">&gt;&gt;&gt; </span>            <span class="n">grad_input</span> <span class="o">+=</span> <span class="n">g1</span>
    396. 

  396. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">if</span> <span class="n">g2</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
    397. 

  397. <span class="gp">&gt;&gt;&gt; </span>            <span class="n">grad_input</span> <span class="o">+=</span> <span class="n">g2</span>
    398. 

  398. <span class="gp">&gt;&gt;&gt; </span>        <span class="k">return</span> <span class="n">grad_input</span>
    399. 

  399. <span class="go">&gt;&gt;&gt;</span>
    400. 

  400. <span class="gp">&gt;&gt;&gt; </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
    401. 

  401. <span class="gp">&gt;&gt;&gt; </span><span class="n">b</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">Func</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>  <span class="c1"># induces g2 to be undefined</span>
    402. 

  402. </pre></div>
    403. 

  403. </div>
    404. 

  404. </dd>
    405. 

  405. </dl>
    406. 

  406. </dd></dl>
    407. 

  407. 

  408. <dl class="py method">
    409. 

  409. <dt class="sig sig-object py" id="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.vjp">
    410. 

  410. <em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">vjp</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ctx</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">grad_outputs</span></span><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="n"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span></em><span class="sig-paren">)</span> <span class="sig-return"><span class="sig-return-icon">&#x2192;</span> <span class="sig-return-typehint"><a class="reference external" href="https://docs.python.org/3/library/typing.html#typing.Any" title="(in Python v3.10)"><span class="pre">Any</span></a></span></span><a class="headerlink" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.vjp" title="Permalink to this definition">¶</a></dt>
    411. 

  411. <dd><p>Defines a formula for differentiating the operation with backward mode
    412. 

  412. automatic differentiation (alias to the vjp function).</p>
    413. 

  413. <p>This function is to be overridden by all subclasses.</p>
    414. 

  414. <p>It must accept a context <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx</span></code> as the first argument, followed by
    415. 

  415. as many outputs as the <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> returned (None will be passed in
    416. 

  416. for non tensor outputs of the forward function),
    417. 

  417. and it should return as many tensors, as there were inputs to
    418. 

  418. <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a>. Each argument is the gradient w.r.t the given output,
    419. 

  419. and each returned value should be the gradient w.r.t. the
    420. 

  420. corresponding input. If an input is not a Tensor or is a Tensor not
    421. 

  421. requiring grads, you can just pass None as a gradient for that input.</p>
    422. 

  422. <p>The context can be used to retrieve tensors saved during the forward
    423. 

  423. pass. It also has an attribute <code class="xref py py-attr docutils literal notranslate"><span class="pre">ctx.needs_input_grad</span></code> as a tuple
    424. 

  424. of booleans representing whether each input needs gradient. E.g.,
    425. 

  425. <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> will have <code class="docutils literal notranslate"><span class="pre">ctx.needs_input_grad[0]</span> <span class="pre">=</span> <span class="pre">True</span></code> if the
    426. 

  426. first input to <a class="reference internal" href="#draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward" title="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> needs gradient computated w.r.t. the
    427. 

  427. output.</p>
    428. 

  428. </dd></dl>
    429. 

  429. 

  430. </dd></dl>
    431. 

  431. 

  432. </section>
    433. 

  433. 

  434. 

  435.           </div>
    436. 

  436.           
    437. 

  437.         </div>
    438. 

  438.       </div>
    439. 

  439.       <div class="sphinxsidebar" role="navigation" aria-label="main navigation">
    440. 

  440.         <div class="sphinxsidebarwrapper">
    441. 

  441.             <p class="logo"><a href="../index.html">
    442. 

  442.               <img class="logo" src="../_static/valknut.svg" alt="Logo"/>
    443. 

  443.             </a></p>
    444. 

  444. <h1 class="logo"><a href="../index.html">Draugr</a></h1>
    445. 

  445. 

  446. 

  447. 

  448. 

  449. 

  450. 

  451. 

  452. 

  453. <h3>Navigation</h3>
    454. 

  454. <ul class="current">
    455. 

  455. <li class="toctree-l1 current"><a class="reference internal" href="draugr.html">draugr</a><ul class="current">
    456. 

  456. <li class="toctree-l2"><a class="reference internal" href="draugr.dist_is_editable.html">draugr.dist_is_editable</a></li>
    457. 

  457. <li class="toctree-l2"><a class="reference internal" href="draugr.get_version.html">draugr.get_version</a></li>
    458. 

  458. <li class="toctree-l2"><a class="reference internal" href="draugr.dlib_utilities.html">draugr.dlib_utilities</a></li>
    459. 

  459. <li class="toctree-l2"><a class="reference internal" href="draugr.drawers.html">draugr.drawers</a></li>
    460. 

  460. <li class="toctree-l2"><a class="reference internal" href="draugr.entry_points.html">draugr.entry_points</a></li>
    461. 

  461. <li class="toctree-l2"><a class="reference internal" href="draugr.extensions.html">draugr.extensions</a></li>
    462. 

  462. <li class="toctree-l2"><a class="reference internal" href="draugr.ffmpeg_utilities.html">draugr.ffmpeg_utilities</a></li>
    463. 

  463. <li class="toctree-l2"><a class="reference internal" href="draugr.jax_utilities.html">draugr.jax_utilities</a></li>
    464. 

  464. <li class="toctree-l2"><a class="reference internal" href="draugr.metrics.html">draugr.metrics</a></li>
    465. 

  465. <li class="toctree-l2"><a class="reference internal" href="draugr.multiprocessing_utilities.html">draugr.multiprocessing_utilities</a></li>
    466. 

  466. <li class="toctree-l2"><a class="reference internal" href="draugr.numpy_utilities.html">draugr.numpy_utilities</a></li>
    467. 

  467. <li class="toctree-l2"><a class="reference internal" href="draugr.opencv_utilities.html">draugr.opencv_utilities</a></li>
    468. 

  468. <li class="toctree-l2"><a class="reference internal" href="draugr.os_utilities.html">draugr.os_utilities</a></li>
    469. 

  469. <li class="toctree-l2"><a class="reference internal" href="draugr.pandas_utilities.html">draugr.pandas_utilities</a></li>
    470. 

  470. <li class="toctree-l2"><a class="reference internal" href="draugr.pygame_utilities.html">draugr.pygame_utilities</a></li>
    471. 

  471. <li class="toctree-l2"><a class="reference internal" href="draugr.python_utilities.html">draugr.python_utilities</a></li>
    472. 

  472. <li class="toctree-l2"><a class="reference internal" href="draugr.random_utilities.html">draugr.random_utilities</a></li>
    473. 

  473. <li class="toctree-l2"><a class="reference internal" href="draugr.scipy_utilities.html">draugr.scipy_utilities</a></li>
    474. 

  474. <li class="toctree-l2"><a class="reference internal" href="draugr.stopping.html">draugr.stopping</a></li>
    475. 

  475. <li class="toctree-l2"><a class="reference internal" href="draugr.tensorboard_utilities.html">draugr.tensorboard_utilities</a></li>
    476. 

  476. <li class="toctree-l2"><a class="reference internal" href="draugr.threading_utilities.html">draugr.threading_utilities</a></li>
    477. 

  477. <li class="toctree-l2 current"><a class="reference internal" href="draugr.torch_utilities.html">draugr.torch_utilities</a><ul class="current">
    478. 

  478. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.architectures.html">draugr.torch_utilities.architectures</a></li>
    479. 

  479. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.datasets.html">draugr.torch_utilities.datasets</a></li>
    480. 

  480. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.distributions.html">draugr.torch_utilities.distributions</a></li>
    481. 

  481. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.evaluation.html">draugr.torch_utilities.evaluation</a></li>
    482. 

  482. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.exporting.html">draugr.torch_utilities.exporting</a></li>
    483. 

  483. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.generators.html">draugr.torch_utilities.generators</a></li>
    484. 

  484. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.images.html">draugr.torch_utilities.images</a></li>
    485. 

  485. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.opencv.html">draugr.torch_utilities.opencv</a></li>
    486. 

  486. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.operations.html">draugr.torch_utilities.operations</a></li>
    487. 

  487. <li class="toctree-l3 current"><a class="reference internal" href="draugr.torch_utilities.optimisation.html">draugr.torch_utilities.optimisation</a><ul class="current">
    488. 

  488. <li class="toctree-l4 current"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.html">draugr.torch_utilities.optimisation.debugging</a><ul class="current">
    489. 

  489. <li class="toctree-l5 current"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.gradients.html">draugr.torch_utilities.optimisation.debugging.gradients</a><ul class="current">
    490. 

  490. <li class="toctree-l6"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.gradients.checking.html">draugr.torch_utilities.optimisation.debugging.gradients.checking</a></li>
    491. 

  491. <li class="toctree-l6"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.gradients.flow.html">draugr.torch_utilities.optimisation.debugging.gradients.flow</a></li>
    492. 

  492. <li class="toctree-l6"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.gradients.grad_trace.html">draugr.torch_utilities.optimisation.debugging.gradients.grad_trace</a></li>
    493. 

  493. <li class="toctree-l6 current"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.gradients.guided.html">draugr.torch_utilities.optimisation.debugging.gradients.guided</a><ul class="current">
    494. 

  494. <li class="toctree-l7"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLUModel.html">draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLUModel</a></li>
    495. 

  495. <li class="toctree-l7 current"><a class="current reference internal" href="#">draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU</a></li>
    496. 

  496. </ul>
    497. 

  497. </li>
    498. 

  498. </ul>
    499. 

  499. </li>
    500. 

  500. <li class="toctree-l5"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.layer_fetching.html">draugr.torch_utilities.optimisation.debugging.layer_fetching</a></li>
    501. 

  501. <li class="toctree-l5"><a class="reference internal" href="draugr.torch_utilities.optimisation.debugging.opt_verification.html">draugr.torch_utilities.optimisation.debugging.opt_verification</a></li>
    502. 

  502. </ul>
    503. 

  503. </li>
    504. 

  504. <li class="toctree-l4"><a class="reference internal" href="draugr.torch_utilities.optimisation.parameters.html">draugr.torch_utilities.optimisation.parameters</a></li>
    505. 

  505. <li class="toctree-l4"><a class="reference internal" href="draugr.torch_utilities.optimisation.scheduling.html">draugr.torch_utilities.optimisation.scheduling</a></li>
    506. 

  506. <li class="toctree-l4"><a class="reference internal" href="draugr.torch_utilities.optimisation.stopping.html">draugr.torch_utilities.optimisation.stopping</a></li>
    507. 

  507. <li class="toctree-l4"><a class="reference internal" href="draugr.torch_utilities.optimisation.updates.html">draugr.torch_utilities.optimisation.updates</a></li>
    508. 

  508. </ul>
    509. 

  509. </li>
    510. 

  510. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.persistence.html">draugr.torch_utilities.persistence</a></li>
    511. 

  511. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.sessions.html">draugr.torch_utilities.sessions</a></li>
    512. 

  512. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.system.html">draugr.torch_utilities.system</a></li>
    513. 

  513. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.tensors.html">draugr.torch_utilities.tensors</a></li>
    514. 

  514. <li class="toctree-l3"><a class="reference internal" href="draugr.torch_utilities.writers.html">draugr.torch_utilities.writers</a></li>
    515. 

  515. </ul>
    516. 

  516. </li>
    517. 

  517. <li class="toctree-l2"><a class="reference internal" href="draugr.tqdm_utilities.html">draugr.tqdm_utilities</a></li>
    518. 

  518. <li class="toctree-l2"><a class="reference internal" href="draugr.visualisation.html">draugr.visualisation</a></li>
    519. 

  519. <li class="toctree-l2"><a class="reference internal" href="draugr.writers.html">draugr.writers</a></li>
    520. 

  520. </ul>
    521. 

  521. </li>
    522. 

  522. </ul>
    523. 

  523. <p class="caption" role="heading"><span class="caption-text">Notes</span></p>
    524. 

  524. <ul>
    525. 

  525. <li class="toctree-l1"><a class="reference internal" href="../getting_started.html">Getting Started</a></li>
    526. 

  526. </ul>
    527. 

  527. 

  528. <div class="relations">
    529. 

  529. <h3>Related Topics</h3>
    530. 

  530. <ul>
    531. 

  531.   <li><a href="../index.html">Documentation overview</a><ul>
    532. 

  532.   <li><a href="draugr.html">draugr</a><ul>
    533. 

  533.   <li><a href="draugr.torch_utilities.html">draugr.torch_utilities</a><ul>
    534. 

  534.   <li><a href="draugr.torch_utilities.optimisation.html">draugr.torch_utilities.optimisation</a><ul>
    535. 

  535.   <li><a href="draugr.torch_utilities.optimisation.debugging.html">draugr.torch_utilities.optimisation.debugging</a><ul>
    536. 

  536.   <li><a href="draugr.torch_utilities.optimisation.debugging.gradients.html">draugr.torch_utilities.optimisation.debugging.gradients</a><ul>
    537. 

  537.   <li><a href="draugr.torch_utilities.optimisation.debugging.gradients.guided.html">draugr.torch_utilities.optimisation.debugging.gradients.guided</a><ul>
    538. 

  538.       <li>Previous: <a href="draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLUModel.html" title="previous chapter">draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLUModel</a></li>
    539. 

  539.       <li>Next: <a href="draugr.torch_utilities.optimisation.debugging.layer_fetching.html" title="next chapter">draugr.torch_utilities.optimisation.debugging.layer_fetching</a></li>
    540. 

  540.   </ul></li>
    541. 

  541.   </ul></li>
    542. 

  542.   </ul></li>
    543. 

  543.   </ul></li>
    544. 

  544.   </ul></li>
    545. 

  545.   </ul></li>
    546. 

  546.   </ul></li>
    547. 

  547. </ul>
    548. 

  548. </div>
    549. 

  549. <div id="searchbox" style="display: none" role="search">
    550. 

  550.   <h3 id="searchlabel">Quick search</h3>
    551. 

  551.     <div class="searchformwrapper">
    552. 

  552.     <form class="search" action="../search.html" method="get">
    553. 

  553.       <input type="text" name="q" aria-labelledby="searchlabel" autocomplete="off" autocorrect="off" autocapitalize="off" spellcheck="false"/>
    554. 

  554.       <input type="submit" value="Go" />
    555. 

  555.     </form>
    556. 

  556.     </div>
    557. 

  557. </div>
    558. 

  558. <script>document.getElementById('searchbox').style.display = "block"</script>
    559. 

  559. 

  560. 

  561. 

  562. 

  563. 

  564. 

  565. 

  566. 

  567.         </div>
    568. 

  568.       </div>
    569. 

  569.       <div class="clearer"></div>
    570. 

  570.     </div>
    571. 

  571.     <div class="footer">
    572. 

  572.       &copy;.
    573. 

  573.       
    574. 

  574.       |
    575. 

  575.       Powered by <a href="http://sphinx-doc.org/">Sphinx 5.0.2</a>
    576. 

  576.       &amp; <a href="https://github.com/bitprophet/alabaster">Alabaster 0.7.12</a>
    577. 

  577.       
    578. 

  578.       |
    579. 

  579.       <a href="../_sources/generated/draugr.torch_utilities.optimisation.debugging.gradients.guided.GuidedBackPropReLU.rst.txt"
    580. 

  580.           rel="nofollow">Page source</a>
    581. 

  581.     </div>
    582. 

  582. 

  583.     
    584. 

  584. 

  585.     
    586. 

  586.   </body>
    587. 

  587. </html>
    588.