From 10aa2d099ded0f480e8005ea9d6a327aed53fdf3 Mon Sep 17 00:00:00 2001
From: rasbt <mail@sebastianraschka.com>
Date: Sat, 10 Feb 2024 10:10:14 -0600
Subject: [PATCH] add print statements for illustration purposes

---
 ch04/01_main-chapter-code/ch04.ipynb | 26 ++++++++++++++------------
 1 file changed, 14 insertions(+), 12 deletions(-)

diff --git a/ch04/01_main-chapter-code/ch04.ipynb b/ch04/01_main-chapter-code/ch04.ipynb
index 27eda6b..1ef4aa2 100644
--- a/ch04/01_main-chapter-code/ch04.ipynb
+++ b/ch04/01_main-chapter-code/ch04.ipynb
@@ -538,7 +538,7 @@
    "id": "11190e7d-8c29-4115-824a-e03702f9dd54",
    "metadata": {},
    "source": [
-    "## 4.3 Implementing a feed forward network and GELU activations"
+    "## 4.3 Implementing a feed forward network with GELU activations"
    ]
   },
   {
@@ -741,19 +741,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 38,
    "id": "05473938-799c-49fd-86d4-8ed65f94fee6",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "tensor([[0.2427]], grad_fn=<AddmmBackward0>)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Before shortcut: tensor([[0.0950, 0.0634, 0.3361]], grad_fn=<MulBackward0>)\n",
+      "After shortcut: tensor([[-0.9050,  1.0634,  2.3361]], grad_fn=<AddBackward0>)\n",
+      "Final network output: tensor([[0.2427]], grad_fn=<AddmmBackward0>)\n"
+     ]
     }
    ],
    "source": [
@@ -768,14 +767,17 @@
     "    def forward(self, x):\n",
     "        shortcut = x\n",
     "        x = self.gelu(self.fc1(x))\n",
-    "        x = self.gelu(self.fc2(x)) + shortcut\n",
+    "        x = self.gelu(self.fc2(x))\n",
+    "        print(\"Before shortcut:\", x)\n",
+    "        x = x + shortcut\n",
+    "        print(\"After shortcut:\", x)\n",
     "        x = self.fc3(x)\n",
     "        return x\n",
     "\n",
     "torch.manual_seed(123)\n",
     "ex_short = ExampleWithShortcut()\n",
     "inputs = torch.tensor([[-1., 1., 2.]])\n",
-    "print(ex_short(inputs))"
+    "print(\"Final network output:\", ex_short(inputs))"
    ]
   },
   {