clean variable names

Author: SAAS R7 User1

README.md

@@ -108,7 +108,7 @@ model = ExNN(meta_info=meta_info,
                tuning_epochs=100,
                l1_proj=0.001,
                l1_subnet=0.01,
-               smooth_lambda=10**(-6),
+               l2_smooth=10**(-6),
                verbose=True,
                val_ratio=0.2,
                early_stop_thres=500)

docs/build/doctrees/environment.pickle

@@ -210,8 +210,8 @@ <h1>Source code for exnn.exnn</h1><div class="highlight"><pre>
 <span class="sd">    :type  l1_subnet: float</span>
 <span class="sd">    :param l1_subnet: optional, default=0.001, the strength of L1 penalty for scaling layer.</span>
 
-<span class="sd">    :type  smooth_lambda: float</span>
-<span class="sd">    :param smooth_lambda: optional, default=0.000001, the strength of roughness penalty for subnetworks.</span>
+<span class="sd">    :type  l2_smooth: float</span>
+<span class="sd">    :param l2_smooth: optional, default=0.000001, the strength of roughness penalty for subnetworks.</span>
 
 <span class="sd">    :type  verbose: bool</span>
 <span class="sd">    :param verbose: optional, default=False. If True, detailed messages will be printed.</span>
@@ -233,7 +233,7 @@ <h1>Source code for exnn.exnn</h1><div class="highlight"><pre>
 
     <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">meta_info</span><span class="p">,</span> <span class="n">subnet_num</span><span class="p">,</span> <span class="n">subnet_arch</span><span class="o">=</span><span class="p">[</span><span class="mi">10</span><span class="p">,</span> <span class="mi">6</span><span class="p">],</span> <span class="n">task_type</span><span class="o">=</span><span class="s2">&quot;Regression&quot;</span><span class="p">,</span>
                  <span class="n">activation_func</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">tanh</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">training_epochs</span><span class="o">=</span><span class="mi">10000</span><span class="p">,</span> <span class="n">lr_bp</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">lr_cl</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span>
-                 <span class="n">beta_threshold</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">tuning_epochs</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">l1_proj</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">l1_subnet</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">smooth_lambda</span><span class="o">=</span><span class="mf">0.000001</span><span class="p">,</span>
+                 <span class="n">beta_threshold</span><span class="o">=</span><span class="mf">0.05</span><span class="p">,</span> <span class="n">tuning_epochs</span><span class="o">=</span><span class="mi">500</span><span class="p">,</span> <span class="n">l1_proj</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">l1_subnet</span><span class="o">=</span><span class="mf">0.001</span><span class="p">,</span> <span class="n">l2_smooth</span><span class="o">=</span><span class="mf">0.000001</span><span class="p">,</span>
                  <span class="n">verbose</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">val_ratio</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">early_stop_thres</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
 
         <span class="nb">super</span><span class="p">(</span><span class="n">ExNN</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">meta_info</span><span class="o">=</span><span class="n">meta_info</span><span class="p">,</span>
@@ -246,7 +246,7 @@ <h1>Source code for exnn.exnn</h1><div class="highlight"><pre>
                              <span class="n">lr_bp</span><span class="o">=</span><span class="n">lr_bp</span><span class="p">,</span>
                              <span class="n">l1_proj</span><span class="o">=</span><span class="n">l1_proj</span><span class="p">,</span>
                              <span class="n">l1_subnet</span><span class="o">=</span><span class="n">l1_subnet</span><span class="p">,</span>
-                             <span class="n">smooth_lambda</span><span class="o">=</span><span class="n">smooth_lambda</span><span class="p">,</span>
+                             <span class="n">l2_smooth</span><span class="o">=</span><span class="n">l2_smooth</span><span class="p">,</span>
                              <span class="n">batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
                              <span class="n">training_epochs</span><span class="o">=</span><span class="n">training_epochs</span><span class="p">,</span>
                              <span class="n">tuning_epochs</span><span class="o">=</span><span class="n">tuning_epochs</span><span class="p">,</span>
@@ -261,12 +261,12 @@ <h1>Source code for exnn.exnn</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">train_step_init</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
         <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">GradientTape</span><span class="p">()</span> <span class="k">as</span> <span class="n">tape_cl</span><span class="p">:</span>
             <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">GradientTape</span><span class="p">()</span> <span class="k">as</span> <span class="n">tape_bp</span><span class="p">:</span>
-                <span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+                <span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
                 <span class="n">pred_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
                 <span class="n">regularization_loss</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">add_n</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">proj_layer</span><span class="o">.</span><span class="n">losses</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">output_layer</span><span class="o">.</span><span class="n">losses</span><span class="p">)</span>
                 <span class="n">cl_loss</span> <span class="o">=</span> <span class="n">pred_loss</span> <span class="o">+</span> <span class="n">regularization_loss</span>
                 <span class="n">bp_loss</span> <span class="o">=</span> <span class="n">pred_loss</span> <span class="o">+</span> <span class="n">regularization_loss</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">smooth_lambda</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">l2_smooth</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                     <span class="n">smoothness_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">subnet_blocks</span><span class="o">.</span><span class="n">smooth_loss</span>
                     <span class="n">bp_loss</span> <span class="o">+=</span> <span class="n">smoothness_loss</span>
 
@@ -291,10 +291,10 @@ <h1>Source code for exnn.exnn</h1><div class="highlight"><pre>
     <span class="k">def</span> <span class="nf">train_step_finetune</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">,</span> <span class="n">labels</span><span class="p">):</span>
 
         <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">GradientTape</span><span class="p">()</span> <span class="k">as</span> <span class="n">tape</span><span class="p">:</span>
-            <span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">pred</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">inputs</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
             <span class="n">pred_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">loss_fn</span><span class="p">(</span><span class="n">labels</span><span class="p">,</span> <span class="n">pred</span><span class="p">)</span>
             <span class="n">total_loss</span> <span class="o">=</span> <span class="n">pred_loss</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">smooth_lambda</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">l2_smooth</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
                 <span class="n">smoothness_loss</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">subnet_blocks</span><span class="o">.</span><span class="n">smooth_loss</span>
                 <span class="n">total_loss</span> <span class="o">+=</span> <span class="n">smoothness_loss</span>