enhance(tutorial6): net definition and link

docs/tutorials/tutorial_6.rst

@@ -265,22 +265,16 @@ The convolutional network architecture to be used is:
             mem2 = self.lif2.init_leaky() 
             mem3 = self.lif3.init_leaky()
 
-            # Record the final layer
-            spk3_rec = []
-            mem3_rec = []
-
-            for step in range(num_steps):
-                cur1 = F.max_pool2d(self.conv1(x), 2)
-                spk1, mem1 = self.lif1(cur1, mem1)
-                cur2 = F.max_pool2d(self.conv2(spk1), 2)
-                spk2, mem2 = self.lif2(cur2, mem2)
-                cur3 = self.fc1(spk2.view(batch_size, -1))
-                spk3, mem3 = self.lif3(cur3, mem3)
-
-                spk3_rec.append(spk3)
-                mem3_rec.append(mem3)
+            cur1 = F.max_pool2d(self.conv1(x), 2)
+            spk1, mem1 = self.lif1(cur1, mem1)
+
+            cur2 = F.max_pool2d(self.conv2(spk1), 2)
+            spk2, mem2 = self.lif2(cur2, mem2)
+
+            cur3 = self.fc1(spk2.view(batch_size, -1))
+            spk3, mem3 = self.lif3(cur3, mem3)
 
-            return torch.stack(spk3_rec), torch.stack(mem3_rec)
+            return spk3, mem3
 
 In the previous tutorial, the network was wrapped inside of a class, as shown above. 
 With increasing network complexity, this adds a
@@ -559,6 +553,8 @@ be able to start running your own experiments. `In the next
 tutorial <https://snntorch.readthedocs.io/en/latest/tutorials/index.html>`__,
 we will train a network using a neuromorphic dataset.
 
+A special thanks to `Gianfresco Angelini <https://github.com/gianfa>`__ for providing valuable feedback on the tutorial.
+
 If you like this project, please consider starring ⭐ the repo on GitHub as it is the easiest and best way to support it.
 
 Additional Resources 

examples/tutorial_6_CNN.ipynb

@@ -349,25 +349,18 @@
         "\n",
         "        # Initialize hidden states and outputs at t=0\n",
         "        mem1 = self.lif1.init_leaky()\n",
-        "        mem2 = self.lif2.init_leaky() \n",
+        "        mem2 = self.lif2.init_leaky()\n",
         "        mem3 = self.lif3.init_leaky()\n",
         "\n",
-        "        # Record the final layer\n",
-        "        spk3_rec = []\n",
-        "        mem3_rec = []\n",
+        "        cur1 = F.max_pool2d(self.conv1(x), 2)\n",
+        "        spk1, mem1 = self.lif1(cur1, mem1)\n",
         "\n",
-        "        for step in range(num_steps):\n",
-        "            cur1 = F.max_pool2d(self.conv1(x), 2)\n",
-        "            spk1, mem1 = self.lif1(cur1, mem1)\n",
-        "            cur2 = F.max_pool2d(self.conv2(spk1), 2)\n",
-        "            spk2, mem2 = self.lif2(cur2, mem2)\n",
-        "            cur3 = self.fc1(spk2.view(batch_size, -1))\n",
-        "            spk3, mem3 = self.lif3(cur3, mem3)\n",
+        "        cur2 = F.max_pool2d(self.conv2(spk1), 2)\n",
+        "        spk2, mem2 = self.lif2(cur2, mem2)\n",
         "\n",
-        "            spk3_rec.append(spk3)\n",
-        "            mem3_rec.append(mem3)\n",
-        "\n",
-        "        return torch.stack(spk3_rec), torch.stack(mem3_rec)"
+        "        cur3 = self.fc1(spk2.view(batch_size, -1))\n",
+        "        spk3, mem3 = self.lif3(cur3, mem3)\n",
+        "        return spk3, mem3"
       ]
     },
     {
@@ -418,6 +411,11 @@
         "This enables the final layer to return both the spike and membrane potential response of the neuron."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -786,6 +784,7 @@
       ]
     },
     {
+      "attachments": {},
       "cell_type": "markdown",
       "metadata": {
         "id": "s0dAgWUt2o6E"
@@ -794,6 +793,8 @@
         "# Conclusion\n",
         "You should now have a grasp of the basic features of snnTorch and be able to start running your own experiments. [In the next tutorial](https://snntorch.readthedocs.io/en/latest/tutorials/index.html), we will train a network using a neuromorphic dataset.\n",
         "\n",
+        "A special thanks to [Gianfresco Angelini](https://github.com/gianfa) for providing valuable feedback on the tutorial.\n",
+        "\n",
         "If you like this project, please consider starring ⭐ the repo on GitHub as it is the easiest and best way to support it."
       ]
     },
@@ -822,7 +823,7 @@
     "language_info": {
       "codemirror_mode": {
         "name": "ipython",
-        "version": 2
+        "version": 3
       },
       "file_extension": ".py",
       "mimetype": "text/x-python",