From 0fb1f69b1f6f0813120e6fd0d2a8086c1bb70f78 Mon Sep 17 00:00:00 2001
From: Murtadha <mnisyif@gmail.com>
Date: Fri, 27 Sep 2024 11:02:06 -0400
Subject: [PATCH] Add debugging print statements

---
 bel_NN_dynamic.ipynb | 69 +++++++++++++++++++++++++++++++++++---------
 1 file changed, 56 insertions(+), 13 deletions(-)

diff --git a/bel_NN_dynamic.ipynb b/bel_NN_dynamic.ipynb
index ba9358c..f17e505 100644
--- a/bel_NN_dynamic.ipynb
+++ b/bel_NN_dynamic.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -61,6 +61,25 @@
     "X_train, X_val = X_train.T, X_val.T"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"Data shapes:\")\n",
+    "print(f\"X_train shape: {X_train.shape}\")\n",
+    "print(f\"Y_train shape: {Y_train.shape}\")\n",
+    "print(f\"X_test shape: {X_test.shape}\")\n",
+    "print(f\"Y_test shape: {Y_test.shape}\")\n",
+    "\n",
+    "print(\"\\nData statistics:\")\n",
+    "print(f\"X_train mean: {xp.mean(X_train)}, std: {xp.std(X_train)}\")\n",
+    "print(f\"X_test mean: {xp.mean(X_test)}, std: {xp.std(X_test)}\")\n",
+    "print(f\"Unique Y_train values: {xp.unique(Y_train)}\")\n",
+    "print(f\"Unique Y_test values: {xp.unique(Y_test)}\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -77,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -105,7 +124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,8 +132,8 @@
     "    return xp.maximum(Z, 0)\n",
     "\n",
     "def softmax(Z):\n",
-    "    A = xp.exp(Z) / sum(xp.exp(Z))\n",
-    "    return A\n",
+    "    exp_Z = xp.exp(Z - xp.max(Z, axis=0, keepdims=True))\n",
+    "    return exp_Z / xp.sum(exp_Z, axis=0, keepdims=True)\n",
     "\n",
     "def forward_prop(X, params):\n",
     "    caches = []\n",
@@ -190,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,6 +232,10 @@
     "            val_accuracy = get_accuracy(val_predictions, Y_val)\n",
     "            \n",
     "            print(f\"Iteration {i}: Train Accuracy: {train_accuracy:.4f}, Validation Accuracy: {val_accuracy:.4f}\")\n",
+    "            print(f\"Sample predictions: {train_predictions[:10]}\")\n",
+    "            print(f\"Sample true labels: {Y_train[:10]}\")\n",
+    "            \n",
+    "            print(f\"Iteration {i}: Train Accuracy: {train_accuracy:.4f}, Validation Accuracy: {val_accuracy:.4f}\")\n",
     "            acc_store.append((train_accuracy, val_accuracy))\n",
     "            \n",
     "            if val_accuracy > best_val_accuracy:\n",
@@ -229,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -248,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,15 +307,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
-    "hidden_layers = [1, 2, 3, 4]\n",
-    "neurons_per_layer = [64, 128, 256]\n",
+    "hidden_layers = [1, 2]\n",
+    "neurons_per_layer = [64, 128, 256, 512]\n",
     "layer_configs = list(product(*[neurons_per_layer] * max(hidden_layers)))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(layer_configs)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -315,6 +347,17 @@
     "print(f\"Best validation accuracy: {best_accuracy:.4f}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"\\nModel Architecture:\")\n",
+    "for i in range(1, len(best_params)//2 + 1):\n",
+    "    print(f\"Layer {i}: {best_params[f'W{i}'].shape}\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,