Add inference mode and more often earlystop check.

Author: marekowa

.gitignore

@@ -2,8 +2,5 @@
 **/__pycache__
 _archive
 
-results/classical_DQL_sim_quantum_full.7z
 /scripts/_external_sv_save.py
 /scripts/3z_entropy-calculation.ipynb
-/results/classical_DQL_sim_quantum_sv
-/results/classical_DQL_sim_quantum_sv.7z

scripts/3._Classical_DQL_sim_quant.py

@@ -171,9 +171,10 @@ def train(self, epoch):
                 # update state
                 s = s1
                 if(self.compute_entropy):
-                    self.entropies.append(entanglement_entropy(self.calc_statevector(s))) 
-                    self.cl_entropies.append(classical_entropy(self.calc_statevector(s))) 
-                    self.entropies_episodes[i] += 1
+                    with torch.inference_mode():
+                        self.entropies.append(entanglement_entropy(self.calc_statevector(s))) 
+                        self.cl_entropies.append(classical_entropy(self.calc_statevector(s))) 
+                        self.entropies_episodes[i] += 1
 
                 if d == True: break
 
@@ -190,7 +191,7 @@ def train(self, epoch):
                 self.epsilon *= self.epsilon_growth_rate
             self.epsilon_list.append(self.epsilon)
 
-            if i%10==0 and i>100:
+            if i>100:
                 if sum(self.success[-window:])/window>target_win_ratio:
                     print("Network trained before epoch limit on {i} epoch".format(i=i))
                     break

scripts/3b._Classical_DQL_sim_quant_grid_search.py

@@ -181,9 +181,10 @@ def train(self, epoch):
                     # update state
                     s = s1
                     if(self.compute_entropy):
-                        self.entropies.append(entanglement_entropy(self.calc_statevector(s))) 
-                        self.cl_entropies.append(classical_entropy(self.calc_statevector(s))) 
-                        self.entropies_episodes[i] += 1
+                        with torch.inference_mode():
+                            self.entropies.append(entanglement_entropy(self.calc_statevector(s))) 
+                            self.cl_entropies.append(classical_entropy(self.calc_statevector(s))) 
+                            self.entropies_episodes[i] += 1
 
                     if d == True: break
 
@@ -200,7 +201,7 @@ def train(self, epoch):
                     self.epsilon *= self.epsilon_growth_rate
                 self.epsilon_list.append(self.epsilon)
 
-                if i%10==0 and i>100:
+                if i>100:
                     if sum(self.success[-window:])/window>target_win_ratio:
                         print("Network trained before epoch limit on {i} epoch".format(i=i))
                         break

scripts/3c._Classical_DQL_sim_quant_finetuning.ipynb

@@ -336,9 +336,10 @@
         "                # update state\n",
         "                s = s1\n",
         "                if(self.compute_entropy):\n",
-        "                    self.entropies.append(entanglement_entropy(self.calc_statevector(s))) \n",
-        "                    self.cl_entropies.append(classical_entropy(self.calc_statevector(s))) \n",
-        "                    self.entropies_episodes[i] += 1\n",
+        "                    with torch.inference_mode():\n",
+        "                        self.entropies.append(entanglement_entropy(self.calc_statevector(s))) \n",
+        "                        self.cl_entropies.append(classical_entropy(self.calc_statevector(s))) \n",
+        "                        self.entropies_episodes[i] += 1\n",
         "                \n",
         "                if d == True: break\n",
         "            \n",
@@ -362,7 +363,7 @@
         "                steps = len(self.success)\n",
         "                )\n",
         "\n",
-        "            if i%10==0 and i>100:\n",
+        "            if i>100:\n",
         "                if sum(self.success[-window:])/window>target_win_ratio:\n",
         "                    #print(\"Network trained before epoch limit on {i} epoch\".format(i=i))\n",
         "                    break\n",