{
"cells": [
{
"cell_type": "markdown",
"id": "52f1271d",
"metadata": {
"id": "52f1271d"
},
"source": [
"\n",
" \n",
" ACCA7025 – Application of Machine Learning\n",
"
\n",
" \"\n",
" Student: MD Mahadi Hasan\n",
"
\n",
" \n",
" ACCA7025 – Application of Machine Learning\n",
"
\n",
"\n"
]
},
{
"cell_type": "markdown",
"id": "042ec0de",
"metadata": {
"id": "042ec0de"
},
"source": [
"\n",
"## 1. Imports & Reproducibility\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a0b8ac34",
"metadata": {
"id": "a0b8ac34"
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.gridspec as gridspec\n",
"import seaborn as sns\n",
"import os\n",
"\n",
"import tensorflow as tf\n",
"from tensorflow.keras import layers, models, regularizers\n",
"from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
"from tensorflow.keras.optimizers import Adam, SGD\n",
"\n",
"from sklearn.metrics import (\n",
" classification_report,\n",
" confusion_matrix,\n",
" precision_score,\n",
" recall_score,\n",
" f1_score,\n",
" accuracy_score\n",
")\n",
"\n",
"# Reproducibility\n",
"\n",
"SEED = 42\n",
"np.random.seed(SEED)\n",
"tf.random.set_seed(SEED)\n",
"\n",
"os.makedirs(\"outputs\", exist_ok=True)"
]
},
{
"cell_type": "markdown",
"id": "108d186f",
"metadata": {
"id": "108d186f"
},
"source": [
"## 2.Dataset Loading & Preprocessing\n",
"\n",
"**CIFAR-10** is loaded directly from Keras. Two preprocessing steps are applied:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f7269695",
"metadata": {
"id": "f7269695"
},
"outputs": [],
"source": [
"# Dataset Loading & Preprocessing\n",
"\n",
"CLASS_NAMES = [\n",
" \"airplane\", \"automobile\", \"bird\", \"cat\", \"deer\",\n",
" \"dog\", \"frog\", \"horse\", \"ship\", \"truck\"\n",
"]\n",
"\n",
"print(\"=\" * 60)\n",
"print(\" CIFAR-10 Lightweight CNN Optimisation\")\n",
"print(\"=\" * 60)\n",
"\n",
"(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
"\n",
"# Normalise pixel values to [0, 1]\n",
"x_train = x_train.astype(\"float32\") / 255.0\n",
"x_test = x_test.astype(\"float32\") / 255.0\n",
"\n",
"# Per-channel mean subtraction (zero-centring)\n",
"channel_mean = x_train.mean(axis=(0, 1, 2), keepdims=True)\n",
"x_train -= channel_mean\n",
"x_test -= channel_mean\n",
"\n",
"# Flatten labels\n",
"y_train_flat = y_train.flatten()\n",
"y_test_flat = y_test.flatten()\n",
"\n",
"print(f\"\\nTraining samples : {x_train.shape[0]}\")\n",
"print(f\"Test samples : {x_test.shape[0]}\")\n",
"print(f\"Image shape : {x_train.shape[1:]}\")\n",
"print(f\"Classes : {CLASS_NAMES}\\n\")"
]
},
{
"cell_type": "markdown",
"id": "db2d091d",
"metadata": {
"id": "db2d091d"
},
"source": [
"## 3. Data Augmentation\n",
"\n",
"| Transform | Purpose |\n",
"|:---|:---|\n",
"| `RandomFlip(\"horizontal\")` | Mirror images left-right; preserves class semantics |\n",
"| `RandomTranslation(±10%)` | Simulate object position variation |\n",
"| `RandomRotation(±10%)` | Handle slight camera angle differences |\n",
"| `RandomZoom(±10%)` | Simulate scale variation |\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0c488832",
"metadata": {
"id": "0c488832"
},
"outputs": [],
"source": [
"# Data Augmentation (applied during training only)\n",
"\n",
"data_augmentation = tf.keras.Sequential([\n",
" layers.RandomFlip(\"horizontal\"),\n",
" layers.RandomTranslation(height_factor=0.1, width_factor=0.1),\n",
" layers.RandomRotation(0.1),\n",
" layers.RandomZoom(0.1),\n",
"], name=\"data_augmentation\")"
]
},
{
"cell_type": "markdown",
"id": "061d275e",
"metadata": {
"id": "061d275e"
},
"source": [
"## 4.Lightweight CNN Architecture\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "65196884",
"metadata": {
"id": "65196884"
},
"outputs": [],
"source": [
"\n",
"# Lightweight CNN Architecture Builder\n",
"def build_cnn(\n",
" dropout_rate=0.4,\n",
" optimizer=\"adam\",\n",
" learning_rate=0.001,\n",
" filter_size=(3, 3),\n",
" use_augmentation=True\n",
"):\n",
"\n",
" inputs = layers.Input(shape=(32, 32, 3))\n",
" x = inputs\n",
"\n",
" if use_augmentation:\n",
" x = data_augmentation(x)\n",
"\n",
" # Block 1\n",
" x = layers.Conv2D(32, filter_size, padding=\"same\", activation=\"relu\",\n",
" kernel_initializer=\"he_normal\")(x)\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.Conv2D(32, filter_size, padding=\"same\", activation=\"relu\",\n",
" kernel_initializer=\"he_normal\")(x)\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
" x = layers.Dropout(dropout_rate * 0.5)(x) # lighter dropout after pooling\n",
"\n",
" # Block 2\n",
" x = layers.Conv2D(64, filter_size, padding=\"same\", activation=\"relu\",\n",
" kernel_initializer=\"he_normal\")(x)\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.Conv2D(64, filter_size, padding=\"same\", activation=\"relu\",\n",
" kernel_initializer=\"he_normal\")(x)\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
" x = layers.Dropout(dropout_rate * 0.5)(x)\n",
"\n",
" # Block 3\n",
" x = layers.Conv2D(128, filter_size, padding=\"same\", activation=\"relu\",\n",
" kernel_initializer=\"he_normal\")(x)\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.MaxPooling2D(pool_size=(2, 2))(x)\n",
" x = layers.Dropout(dropout_rate)(x)\n",
"\n",
" # Classification Head\n",
" x = layers.Flatten()(x)\n",
" x = layers.Dense(256, activation=\"relu\", kernel_initializer=\"he_normal\")(x)\n",
" x = layers.BatchNormalization()(x)\n",
" x = layers.Dropout(dropout_rate)(x)\n",
" outputs = layers.Dense(10, activation=\"softmax\")(x)\n",
"\n",
" model = models.Model(inputs, outputs)\n",
"\n",
" # Optimizer selection\n",
" if optimizer == \"adam\":\n",
" opt = Adam(learning_rate=learning_rate)\n",
" else:\n",
" opt = SGD(learning_rate=learning_rate, momentum=0.9, nesterov=True)\n",
"\n",
" model.compile(\n",
" optimizer=opt,\n",
" loss=\"sparse_categorical_crossentropy\",\n",
" metrics=[\"accuracy\"]\n",
" )\n",
" return model"
]
},
{
"cell_type": "markdown",
"id": "4bcc8794",
"metadata": {
"id": "4bcc8794"
},
"source": [
"## 5. Training & Evaluation Helpers\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9fde2135",
"metadata": {
"id": "9fde2135"
},
"outputs": [],
"source": [
"# Training Helper\n",
"\n",
"def train_model(model, epochs=20, batch_size=64, label=\"model\"):\n",
" print(f\"\\n{'─'*10}\")\n",
" print(f\" Training: {label}\")\n",
" print(f\"{'─'*10}\")\n",
"\n",
" callbacks = [\n",
" EarlyStopping(monitor=\"val_loss\", patience=10, restore_best_weights=True),\n",
" ReduceLROnPlateau(monitor=\"val_loss\", factor=0.5, patience=5, min_lr=1e-6, verbose=1)\n",
" ]\n",
"\n",
" history = model.fit(\n",
" x_train, y_train_flat,\n",
" epochs=epochs,\n",
" batch_size=batch_size,\n",
" validation_split=0.1,\n",
" callbacks=callbacks,\n",
" verbose=1\n",
" )\n",
" return history\n",
"\n",
"# Evaluation Helper\n",
"\n",
"def evaluate_model(model, label=\"Model\"):\n",
" y_pred_probs = model.predict(x_test, verbose=0)\n",
" y_pred = np.argmax(y_pred_probs, axis=1)\n",
"\n",
" acc = accuracy_score(y_test_flat, y_pred)\n",
" prec = precision_score(y_test_flat, y_pred, average=\"weighted\")\n",
" rec = recall_score(y_test_flat, y_pred, average=\"weighted\")\n",
" f1 = f1_score(y_test_flat, y_pred, average=\"weighted\")\n",
"\n",
" print(f\"\\n{'═'*20}\")\n",
" print(f\" Results: {label}\")\n",
" print(f\"{'═'*20}\")\n",
" print(f\" Accuracy : {acc:.4f}\")\n",
" print(f\" Precision : {prec:.4f}\")\n",
" print(f\" Recall : {rec:.4f}\")\n",
" print(f\" F1-Score : {f1:.4f}\")\n",
" print(f\"\\n Per-Class Report:\")\n",
" print(classification_report(y_test_flat, y_pred, target_names=CLASS_NAMES))\n",
"\n",
" return {\n",
" \"label\": label,\n",
" \"accuracy\": acc,\n",
" \"precision\": prec,\n",
" \"recall\": rec,\n",
" \"f1\": f1,\n",
" \"y_pred\": y_pred,\n",
" \"y_pred_probs\": y_pred_probs\n",
" }"
]
},
{
"cell_type": "markdown",
"id": "a8216b07",
"metadata": {
"id": "a8216b07"
},
"source": [
"## 6. Experiment A - Baseline\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8b5e8af4",
"metadata": {
"id": "8b5e8af4"
},
"outputs": [],
"source": [
"# EXPERIMENT A – Baseline (Adam, dropout=0.4, 3×3 filters)\n",
"model_A = build_cnn(dropout_rate=0.4, optimizer=\"adam\", filter_size=(3, 3))\n",
"model_A.summary()\n",
"hist_A = train_model(model_A, epochs=20, label=\"Exp-A: Adam | Dropout=0.4 | 3×3\")\n",
"res_A = evaluate_model(model_A, label=\"Exp-A: Adam | Dropout=0.4 | 3×3\")"
]
},
{
"cell_type": "markdown",
"id": "cf7c5e04",
"metadata": {
"id": "cf7c5e04"
},
"source": [
"## 7. Experiment B - SGD Optimiser\n",
"**Configuration:** SGD (lr=0.01, momentum=0.9, Nesterov=True) | Dropout = 0.4 | Filter = 3×3\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23af86cc",
"metadata": {
"id": "23af86cc"
},
"outputs": [],
"source": [
"# 7. EXPERIMENT B – SGD optimizer\n",
"model_B = build_cnn(dropout_rate=0.4, optimizer=\"sgd\",\n",
" learning_rate=0.01, filter_size=(3, 3))\n",
"hist_B = train_model(model_B, epochs=20, label=\"Exp-B: SGD | Dropout=0.4 | 3×3\")\n",
"res_B = evaluate_model(model_B, label=\"Exp-B: SGD | Dropout=0.4 | 3×3\")"
]
},
{
"cell_type": "markdown",
"id": "0c203335",
"metadata": {
"id": "0c203335"
},
"source": [
"## 8. Experiment C - Higher Dropout Rate\n",
"**Configuration:** Adam (lr=0.001) | Dropout = **0.5** | Filter = 3×3\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "69554a53",
"metadata": {
"id": "69554a53"
},
"outputs": [],
"source": [
"# 8. EXPERIMENT C – Higher dropout (0.5)\n",
"\n",
"model_C = build_cnn(dropout_rate=0.5, optimizer=\"adam\", filter_size=(3, 3))\n",
"hist_C = train_model(model_C, epochs=20, label=\"Exp-C: Adam | Dropout=0.5 | 3×3\")\n",
"res_C = evaluate_model(model_C, label=\"Exp-C: Adam | Dropout=0.5 | 3×3\")"
]
},
{
"cell_type": "markdown",
"id": "3b64618e",
"metadata": {
"id": "3b64618e"
},
"source": [
"\n",
"## 9. Experiment D - Larger Filter Size (5×5)\n",
"**Configuration:** Adam (lr=0.001) | Dropout = 0.4 | Filter = **5×5**\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0213d8d7",
"metadata": {
"id": "0213d8d7"
},
"outputs": [],
"source": [
"# 9. EXPERIMENT D – Larger filter size (5×5)\n",
"model_D = build_cnn(dropout_rate=0.4, optimizer=\"adam\", filter_size=(5, 5))\n",
"hist_D = train_model(model_D, epochs=20, label=\"Exp-D: Adam | Dropout=0.4 | 5×5\")\n",
"res_D = evaluate_model(model_D, label=\"Exp-D: Adam | Dropout=0.4 | 5×5\")"
]
},
{
"cell_type": "markdown",
"id": "1f1a273e",
"metadata": {
"id": "1f1a273e"
},
"source": [
"## 10. Training & Validation Curves\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25f66111",
"metadata": {
"id": "25f66111"
},
"outputs": [],
"source": [
"# PLOT: Training & Validation Accuracy / Loss per Experiment\n",
"def plot_history(hist, title, filename):\n",
" fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
" fig.suptitle(title, fontsize=14, fontweight=\"bold\")\n",
"\n",
" # Accuracy\n",
" axes[0].plot(hist.history[\"accuracy\"], label=\"Train Accuracy\", color=\"#2196F3\")\n",
" axes[0].plot(hist.history[\"val_accuracy\"], label=\"Validation Accuracy\", color=\"#FF5722\", linestyle=\"--\")\n",
" axes[0].set_title(\"Accuracy\")\n",
" axes[0].set_xlabel(\"Epoch\")\n",
" axes[0].set_ylabel(\"Accuracy\")\n",
" axes[0].legend()\n",
" axes[0].grid(True, alpha=0.3)\n",
"\n",
" # Loss\n",
" axes[1].plot(hist.history[\"loss\"], label=\"Train Loss\", color=\"#2196F3\")\n",
" axes[1].plot(hist.history[\"val_loss\"], label=\"Validation Loss\", color=\"#FF5722\", linestyle=\"--\")\n",
" axes[1].set_title(\"Loss\")\n",
" axes[1].set_xlabel(\"Epoch\")\n",
" axes[1].set_ylabel(\"Loss\")\n",
" axes[1].legend()\n",
" axes[1].grid(True, alpha=0.3)\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(f\"outputs/{filename}\", dpi=150, bbox_inches=\"tight\")\n",
" plt.show()\n",
" print(f\" -> Saved: outputs/{filename}\")\n",
"\n",
"plot_history(hist_A, \"Exp-A: Adam | Dropout=0.4 | 3x3\", \"expA_history.png\")\n",
"plot_history(hist_B, \"Exp-B: SGD | Dropout=0.4 | 3x3\", \"expB_history.png\")\n",
"plot_history(hist_C, \"Exp-C: Adam | Dropout=0.5 | 3x3\", \"expC_history.png\")\n",
"plot_history(hist_D, \"Exp-D: Adam | Dropout=0.4 | 5x5\", \"expD_history.png\")"
]
},
{
"cell_type": "markdown",
"id": "5d1395c8",
"metadata": {
"id": "5d1395c8"
},
"source": [
"## 11.Confusion Matrix\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ebf25469",
"metadata": {
"id": "ebf25469"
},
"outputs": [],
"source": [
"# 11. PLOT: Confusion Matrix for Best Model (Exp-A assumed)\n",
"def plot_confusion_matrix(res, filename):\n",
" cm = confusion_matrix(y_test_flat, res[\"y_pred\"])\n",
" cm_norm = cm.astype(float) / cm.sum(axis=1, keepdims=True)\n",
"\n",
" fig, axes = plt.subplots(1, 2, figsize=(18, 7))\n",
" fig.suptitle(f\"Confusion Matrix — {res['label']}\", fontsize=13, fontweight=\"bold\")\n",
"\n",
" # Raw counts\n",
" sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\",\n",
" xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES, ax=axes[0])\n",
" axes[0].set_title(\"Raw Counts\")\n",
" axes[0].set_xlabel(\"Predicted Label\")\n",
" axes[0].set_ylabel(\"True Label\")\n",
" axes[0].tick_params(axis=\"x\", rotation=45)\n",
"\n",
" # Normalised\n",
" sns.heatmap(cm_norm, annot=True, fmt=\".2f\", cmap=\"Greens\",\n",
" xticklabels=CLASS_NAMES, yticklabels=CLASS_NAMES, ax=axes[1])\n",
" axes[1].set_title(\"Normalised (Row %)\")\n",
" axes[1].set_xlabel(\"Predicted Label\")\n",
" axes[1].set_ylabel(\"True Label\")\n",
" axes[1].tick_params(axis=\"x\", rotation=45)\n",
"\n",
" plt.tight_layout()\n",
" plt.savefig(f\"outputs/{filename}\", dpi=150, bbox_inches=\"tight\")\n",
" plt.show()\n",
" print(f\" -> Saved: outputs/{filename}\")\n",
"\n",
"plot_confusion_matrix(res_A, \"confmat_expA.png\")\n",
"plot_confusion_matrix(res_B, \"confmat_expB.png\")"
]
},
{
"cell_type": "markdown",
"id": "5143e26b",
"metadata": {
"id": "5143e26b"
},
"source": [
"## 12. Comparative Performance All Experiments\n",
"\n",
"A grouped bar chart compares **Accuracy, Precision, Recall, and weighted F1-score** side-by-side across all four experiments. This visualisation makes it straightforward to identify the best-performing configuration across all metrics simultaneously and to observe whether any experiment trades recall for precision or vice versa.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c2a9d67d",
"metadata": {
"id": "c2a9d67d"
},
"outputs": [],
"source": [
"# 12. PLOT: Comparative Performance Table (Bar Chart)\n",
"\n",
"results_list = [res_A, res_B, res_C, res_D]\n",
"labels = [r[\"label\"].split(\":\")[1].strip() for r in results_list]\n",
"metrics = [\"accuracy\", \"precision\", \"recall\", \"f1\"]\n",
"colors = [\"#2196F3\", \"#4CAF50\", \"#FF9800\", \"#E91E63\"]\n",
"\n",
"x = np.arange(len(labels))\n",
"width = 0.2\n",
"\n",
"fig, ax = plt.subplots(figsize=(14, 6))\n",
"for i, (metric, color) in enumerate(zip(metrics, colors)):\n",
" vals = [r[metric] for r in results_list]\n",
" bars = ax.bar(x + i * width, vals, width, label=metric.capitalize(), color=color, alpha=0.85)\n",
" for bar, val in zip(bars, vals):\n",
" ax.text(bar.get_x() + bar.get_width() / 2, bar.get_height() + 0.002,\n",
" f\"{val:.3f}\", ha=\"center\", va=\"bottom\", fontsize=8)\n",
"\n",
"ax.set_xticks(x + width * 1.5)\n",
"ax.set_xticklabels(labels, rotation=20, ha=\"right\", fontsize=9)\n",
"ax.set_ylabel(\"Score\")\n",
"ax.set_ylim(0, 1.05)\n",
"ax.set_title(\"Comparative Performance Across All Experiments\", fontsize=13, fontweight=\"bold\")\n",
"ax.legend(loc=\"lower right\")\n",
"ax.grid(axis=\"y\", alpha=0.3)\n",
"plt.tight_layout()\n",
"plt.savefig(\"outputs/comparative_bar.png\", dpi=150, bbox_inches=\"tight\")\n",
"plt.show()\n",
"print(\" -> Saved: outputs/comparative_bar.png\")"
]
},
{
"cell_type": "markdown",
"id": "5ad034af",
"metadata": {
"id": "5ad034af"
},
"source": [
"## 13. Adam vs SGD - Validation Overlay"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a8206e5",
"metadata": {
"id": "5a8206e5"
},
"outputs": [],
"source": [
"# 13. PLOT: Overlay – Adam vs SGD Validation Accuracy\n",
"\n",
"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
"fig.suptitle(\"Adam vs SGD: Validation Accuracy & Loss Overlay\", fontsize=13, fontweight=\"bold\")\n",
"\n",
"# Accuracy overlay\n",
"axes[0].plot(hist_A.history[\"val_accuracy\"], label=\"Adam (val_acc)\",\n",
" color=\"#2196F3\", linewidth=2)\n",
"axes[0].plot(hist_B.history[\"val_accuracy\"], label=\"SGD (val_acc)\",\n",
" color=\"#FF5722\", linewidth=2, linestyle=\"--\")\n",
"axes[0].set_title(\"Validation Accuracy\")\n",
"axes[0].set_xlabel(\"Epoch\")\n",
"axes[0].set_ylabel(\"Accuracy\")\n",
"axes[0].legend()\n",
"axes[0].grid(True, alpha=0.3)\n",
"\n",
"# Loss overlay\n",
"axes[1].plot(hist_A.history[\"val_loss\"], label=\"Adam (val_loss)\",\n",
" color=\"#2196F3\", linewidth=2)\n",
"axes[1].plot(hist_B.history[\"val_loss\"], label=\"SGD (val_loss)\",\n",
" color=\"#FF5722\", linewidth=2, linestyle=\"--\")\n",
"axes[1].set_title(\"Validation Loss\")\n",
"axes[1].set_xlabel(\"Epoch\")\n",
"axes[1].set_ylabel(\"Loss\")\n",
"axes[1].legend()\n",
"axes[1].grid(True, alpha=0.3)\n",
"\n",
"plt.tight_layout()\n",
"plt.savefig(\"outputs/adam_vs_sgd_overlay.png\", dpi=150, bbox_inches=\"tight\")\n",
"plt.show()\n",
"print(\" -> Saved: outputs/adam_vs_sgd_overlay.png\")"
]
},
{
"cell_type": "markdown",
"id": "e3b27850",
"metadata": {
"id": "e3b27850"
},
"source": [
"## 14. Sample Predictions Best Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "25b7411d",
"metadata": {
"id": "25b7411d"
},
"outputs": [],
"source": [
"# 14. PLOT: Sample Predictions (Best Model)\n",
"\n",
"# Reload original (non-normalised) for display only\n",
"(_, _), (x_test_display, _) = tf.keras.datasets.cifar10.load_data()\n",
"x_test_display = x_test_display[:25]\n",
"\n",
"best_model = model_A\n",
"y_pred_sample = np.argmax(best_model.predict(x_test[:25], verbose=0), axis=1)\n",
"\n",
"fig, axes = plt.subplots(5, 5, figsize=(12, 12))\n",
"fig.suptitle(\"Sample Predictions — Exp-A (Best Model)\", fontsize=13, fontweight=\"bold\")\n",
"for i, ax in enumerate(axes.flat):\n",
" ax.imshow(x_test_display[i])\n",
" true_label = CLASS_NAMES[y_test_flat[i]]\n",
" pred_label = CLASS_NAMES[y_pred_sample[i]]\n",
" color = \"green\" if true_label == pred_label else \"red\"\n",
" ax.set_title(f\"T:{true_label}\\nP:{pred_label}\", fontsize=7, color=color)\n",
" ax.axis(\"off\")\n",
"plt.tight_layout()\n",
"plt.savefig(\"outputs/sample_predictions.png\", dpi=150, bbox_inches=\"tight\")\n",
"plt.show()\n",
"print(\" -> Saved: outputs/sample_predictions.png\")"
]
},
{
"cell_type": "markdown",
"id": "cc0ed547",
"metadata": {
"id": "cc0ed547"
},
"source": [
"## 15. Results Summary Table"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bb8d4ee0",
"metadata": {
"id": "bb8d4ee0"
},
"outputs": [],
"source": [
"# 15. Summary Table\n",
"\n",
"print(\"\\n\" + \"=\" * 70)\n",
"print(f\" {'Experiment':<35} {'Acc':>7} {'Prec':>7} {'Rec':>7} {'F1':>7}\")\n",
"print(\"=\" * 70)\n",
"for r in results_list:\n",
" print(f\" {r['label']:<35} {r['accuracy']:>7.4f} {r['precision']:>7.4f} \"\n",
" f\"{r['recall']:>7.4f} {r['f1']:>7.4f}\")\n",
"print(\"=\" * 70)\n",
"\n",
"print(\"\\n All experiments complete. Outputs saved to /outputs/\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:base] *",
"language": "python",
"name": "conda-base-py"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.9"
},
"colab": {
"provenance": []
}
},
"nbformat": 4,
"nbformat_minor": 5
}