plot derivative of training accuracy

Author: bjarthur

src/accuracy

@@ -272,10 +272,20 @@ def main():
       ax1.set_ylim(top=100)
       ax1.set_ylabel('Overall recall')
       ax1.set_xlim(0,1+len(train_step[model]))
+
       if imodel==len(keys_to_plot)-1:
         handles1, labels1 = ax1.get_legend_handles_labels()
         handles4, labels4 = ax4.get_legend_handles_labels()
-        ax1.legend(handles1+handles4, labels1+labels4, loc=(1.25, 0.0)) #loc='upper center')
+        ax1.legend(handles1+handles4, labels1+labels4, loc=(1.45, 0.0)) #loc='upper center')
+
+      ax5 = ax4.twinx()
+      train_accuracy_ave_diff = [train_accuracy_ave[i+1] - train_accuracy_ave[i] \
+                                 for i in range(-1+len(train_accuracy_ave))]
+      ax5.plot([(x+y)/2 for (x,y) in validation_intervals[1:]], train_accuracy_ave_diff, 'y', label='Train diff')
+      ax5.spines["right"].set_position(("axes", 1.2))
+      ax5.set_ylabel('Train mean diff', color='y')
+      ax5.tick_params(axis='y', labelcolor='y')
+
       ax2 = ax4.twiny()
       ax2.xaxis.set_ticks_position("bottom")
       ax2.xaxis.set_label_position("bottom")