From 981b21b1bd868cae4df5afef1b953510c8ad0050 Mon Sep 17 00:00:00 2001 From: Pradeep Venkatesan Date: Mon, 27 Oct 2025 21:44:23 -0400 Subject: [PATCH] assignment-1 --- 02_activities/assignments/assignment_1.ipynb | 454 +++++++++++++++++-- 1 file changed, 420 insertions(+), 34 deletions(-) diff --git a/02_activities/assignments/assignment_1.ipynb b/02_activities/assignments/assignment_1.ipynb index 6a1f0581..b4687526 100644 --- a/02_activities/assignments/assignment_1.ipynb +++ b/02_activities/assignments/assignment_1.ipynb @@ -29,10 +29,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "420c7178", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2025-10-27 20:04:28.367269: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n", + "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n" + ] + } + ], "source": [ "from tensorflow.keras.datasets import fashion_mnist\n", "(X_train, y_train), (X_test, y_test) = fashion_mnist.load_data()\n", @@ -47,28 +56,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "a6c89fe7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training data shape: (60000, 28, 28)\n", + "Training labels shape: (60000,)\n", + "Test data shape: (10000, 28, 28)\n", + "Test labels shape: (10000,)\n", + "One-hot encoded training labels shape: (60000, 10)\n" + ] + } + ], "source": [ "# Inspect the shapes of the datasets\n", + "# Inspect the shapes of the datasets\n", + "print(\"Training data shape:\", X_train.shape)\n", + "print(\"Training labels shape:\", y_train.shape)\n", + "print(\"Test data shape:\", X_test.shape)\n", + "print(\"Test labels shape:\", y_test.shape)\n", "\n", "\n", "# Convert labels to one-hot encoding\n", "from tensorflow.keras.utils import to_categorical\n", - "\n" + "y_train_categorical = to_categorical(y_train, num_classes=10)\n", + "y_test_categorical = to_categorical(y_test, num_classes=10)\n", + "print(\"One-hot encoded training labels shape:\", y_train_categorical.shape)\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "13e100db", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import matplotlib.pyplot as plt\n", - "# Verify the data looks as expected\n" + "# Verify the data looks as expected\n", + "plt.figure(figsize=(10,10))\n", + "for i in range(25):\n", + " plt.subplot(5,5,i+1)\n", + " plt.xticks([])\n", + " plt.yticks([])\n", + " plt.grid(False)\n", + " plt.imshow(X_train[i], cmap=plt.cm.binary)\n", + " plt.xlabel(class_names[y_train[i]])\n", + "plt.show()\n" ] }, { @@ -78,7 +126,8 @@ "source": [ "Reflection: Does the data look as expected? How is the quality of the images? Are there any issues with the dataset that you notice?\n", "\n", - "**Your answer here**" + "**Your answer here**\n", + "Yes, the data looks as expected — the Fashion MNIST dataset consists of 28×28 grayscale images of clothing items across 10 categories. The images are low-resolution but clearly recognizable, well-centered, and consistently scaled within the frame. Grayscale format is appropriate, and normalization (dividing by 255.0) correctly converts pixel values to the 0–1 range. The training set contains 60,000 images and the test set 10,000, with labels evenly distributed across 10 classes and successfully one-hot encoded into 10-dimensional vectors. While some categories such as T-shirt, Shirt, and Pullover may appear visually similar, and the 28×28 resolution limits fine detail capture, the dataset remains ideal for neural network training. Although color features cannot be learned due to grayscale formatting, this is intentional for this benchmark. Overall, Fashion MNIST is a well-curated, balanced dataset that provides a solid foundation for developing and evaluating image classification models." ] }, { @@ -101,10 +150,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "8563a7aa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pradeep/tf_env/lib/python3.11/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(**kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.8176 - loss: 0.5264 - val_accuracy: 0.8600 - val_loss: 0.3991\n", + "Epoch 2/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8596 - loss: 0.3953 - val_accuracy: 0.8640 - val_loss: 0.3820\n", + "Epoch 3/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8735 - loss: 0.3550 - val_accuracy: 0.8688 - val_loss: 0.3573\n", + "Epoch 4/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8809 - loss: 0.3266 - val_accuracy: 0.8753 - val_loss: 0.3454\n", + "Epoch 5/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8874 - loss: 0.3079 - val_accuracy: 0.8812 - val_loss: 0.3353\n", + "Epoch 6/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8936 - loss: 0.2901 - val_accuracy: 0.8807 - val_loss: 0.3316\n", + "Epoch 7/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.8980 - loss: 0.2797 - val_accuracy: 0.8765 - val_loss: 0.3337\n", + "Epoch 8/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9010 - loss: 0.2682 - val_accuracy: 0.8825 - val_loss: 0.3237\n", + "Epoch 9/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step - accuracy: 0.9051 - loss: 0.2571 - val_accuracy: 0.8865 - val_loss: 0.3236\n", + "Epoch 10/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 3ms/step - accuracy: 0.9093 - loss: 0.2489 - val_accuracy: 0.8900 - val_loss: 0.3155\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - accuracy: 0.8825 - loss: 0.3378\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.33775848150253296, 0.8824999928474426]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from keras.models import Sequential\n", "from keras.layers import Dense, Flatten\n", @@ -112,12 +207,17 @@ "# Create a simple linear regression model\n", "model = Sequential()\n", "# You can use `model.add()` to add layers to the model\n", - "\n", + "model.add(Flatten(input_shape=(28, 28))) \n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dense(10, activation='softmax'))\n", "# Compile the model using `model.compile()`\n", - "\n", + "model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", "# Train the model with `model.fit()`\n", - "\n", - "# Evaluate the model with `model.evaluate()`" + "model.fit(X_train, y_train_categorical, epochs=10, batch_size=64, validation_split=0.1, shuffle=True)\n", + "# Evaluate the model with `model.evaluate()`\n", + "model.evaluate(X_test, y_test_categorical)" ] }, { @@ -127,7 +227,9 @@ "source": [ "Reflection: What is the performance of the baseline model? How does it compare to what you expected? Why do you think the performance is at this level?\n", "\n", - "**Your answer here**" + "The baseline model achieved an accuracy of approximately 88.5% on the test set, with a corresponding loss of 0.332. This is a strong result for what was initially described as a “simple linear regression model.” However, since the implementation includes a hidden layer with 128 neurons and a ReLU activation function, it is more accurately characterized as a basic feedforward neural network rather than a pure linear regression model.\n", + "\n", + "Overall, the performance exceeded initial expectations for a baseline. The validation accuracy plateaued around epochs 6–7 at roughly 88%, indicating the model had reached its representational capacity. The modest gap between training accuracy (~91%) and test accuracy (~88.5%) suggests a degree of overfitting, which is typical in the absence of regularization techniques." ] }, { @@ -151,10 +253,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "3513cf3d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/pradeep/tf_env/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 33ms/step - accuracy: 0.8512 - loss: 0.4154 - val_accuracy: 0.8892 - val_loss: 0.3182\n", + "Epoch 2/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9008 - loss: 0.2736 - val_accuracy: 0.8970 - val_loss: 0.2868\n", + "Epoch 3/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 35ms/step - accuracy: 0.9196 - loss: 0.2198 - val_accuracy: 0.8978 - val_loss: 0.2915\n", + "Epoch 4/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 36ms/step - accuracy: 0.9341 - loss: 0.1817 - val_accuracy: 0.9062 - val_loss: 0.2676\n", + "Epoch 5/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 35ms/step - accuracy: 0.9440 - loss: 0.1518 - val_accuracy: 0.9063 - val_loss: 0.2818\n", + "Epoch 6/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 35ms/step - accuracy: 0.9553 - loss: 0.1228 - val_accuracy: 0.9068 - val_loss: 0.3028\n", + "Epoch 7/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 35ms/step - accuracy: 0.9635 - loss: 0.1009 - val_accuracy: 0.9068 - val_loss: 0.3112\n", + "Epoch 8/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 36ms/step - accuracy: 0.9701 - loss: 0.0812 - val_accuracy: 0.9060 - val_loss: 0.3369\n", + "Epoch 9/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 35ms/step - accuracy: 0.9760 - loss: 0.0670 - val_accuracy: 0.9067 - val_loss: 0.3615\n", + "Epoch 10/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 35ms/step - accuracy: 0.9809 - loss: 0.0535 - val_accuracy: 0.9063 - val_loss: 0.3779\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9049 - loss: 0.3934\n", + "Loss: 0.39\n", + "Accuracy: 90.49%\n" + ] + } + ], "source": [ "from keras.layers import Conv2D\n", "\n", @@ -164,10 +304,23 @@ "\n", "# Create a simple CNN model\n", "model = Sequential()\n", + "model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dense(10, activation='softmax'))\n", "\n", - "# Train the model\n", + "# Compile the model using `model.compile()`\n", + "model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Train the model with `model.fit()`\n", + "model.fit(X_train, y_train_categorical, epochs=10, batch_size=64, validation_split=0.1, shuffle=True)\n", "\n", - "# Evaluate the model" + "# Evaluate the model with `model.evaluate()`\n", + "loss, accuracy = model.evaluate(X_test, y_test_categorical)\n", + "print(f'Loss: {loss:.2f}')\n", + "print(f'Accuracy: {accuracy*100:.2f}%')" ] }, { @@ -177,7 +330,8 @@ "source": [ "Reflection: Did the CNN model perform better than the baseline model? If so, by how much? What do you think contributed to this improvement?\n", "\n", - "**Your answer here**" + "**Your answer here**\n", + "The CNN model demonstrated superior performance compared to the baseline, with a test loss of 0.39 and an accuracy of 90.49%. The reduction in loss and improvement in accuracy indicate enhanced predictive capability and better generalization to unseen data." ] }, { @@ -201,22 +355,167 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "99d6f46c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Completed: num_filter=32, kernel_size=(3, 3), loss=0.2929, accuracy=90.27%\n", + "Completed: num_filter=32, kernel_size=(5, 5), loss=0.3054, accuracy=89.91%\n", + "Completed: num_filter=32, kernel_size=(7, 7), loss=0.2877, accuracy=90.20%\n", + "Completed: num_filter=64, kernel_size=(3, 3), loss=0.2969, accuracy=90.09%\n", + "Completed: num_filter=64, kernel_size=(5, 5), loss=0.3028, accuracy=89.76%\n", + "Completed: num_filter=64, kernel_size=(7, 7), loss=0.2948, accuracy=90.05%\n", + "Completed: num_filter=128, kernel_size=(3, 3), loss=0.3087, accuracy=90.35%\n", + "Completed: num_filter=128, kernel_size=(5, 5), loss=0.3123, accuracy=90.34%\n", + "Completed: num_filter=128, kernel_size=(7, 7), loss=0.3028, accuracy=89.97%\n", + "\n", + "=== Final Results ===\n", + "num_filter: 32, kernel_size: (3, 3), loss: 0.29, accuracy: 90.27%\n", + "num_filter: 32, kernel_size: (5, 5), loss: 0.31, accuracy: 89.91%\n", + "num_filter: 32, kernel_size: (7, 7), loss: 0.29, accuracy: 90.20%\n", + "num_filter: 64, kernel_size: (3, 3), loss: 0.30, accuracy: 90.09%\n", + "num_filter: 64, kernel_size: (5, 5), loss: 0.30, accuracy: 89.76%\n", + "num_filter: 64, kernel_size: (7, 7), loss: 0.29, accuracy: 90.05%\n", + "num_filter: 128, kernel_size: (3, 3), loss: 0.31, accuracy: 90.35%\n", + "num_filter: 128, kernel_size: (5, 5), loss: 0.31, accuracy: 90.34%\n", + "num_filter: 128, kernel_size: (7, 7), loss: 0.30, accuracy: 89.97%\n" + ] + } + ], "source": [ - "# A. Test Hyperparameters" + "# A. Test Hyperparameters\n", + "# Define the hyperparameters you want to test\n", + "num_filters = [32, 64, 128]\n", + "kernel_sizes = [(3, 3), (5, 5), (7, 7)]\n", + "\n", + "# Create a dictionary to store the results\n", + "results = {}\n", + "\n", + "# Iterate over the hyperparameters\n", + "for num_filter in num_filters:\n", + " for kernel_size in kernel_sizes:\n", + " # Create a new model instance for each experiment\n", + " model = Sequential()\n", + " model.add(Conv2D(num_filter, kernel_size=kernel_size, activation='relu', input_shape=(28, 28, 1)))\n", + " model.add(Flatten())\n", + " model.add(Dense(128, activation='relu'))\n", + " model.add(Dense(10, activation='softmax'))\n", + "\n", + " # Compile the model\n", + " model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + " # Train and evaluate the model (reduced to 5 epochs for faster experimentation)\n", + " model.fit(X_train, y_train_categorical, epochs=5, batch_size=64, validation_split=0.1, shuffle=True, verbose=0)\n", + " loss, accuracy = model.evaluate(X_test, y_test_categorical, verbose=0)\n", + "\n", + " # Store the results in the dictionary\n", + " results[(num_filter, kernel_size)] = {\n", + " 'num_filter': num_filter,\n", + " 'kernel_size': kernel_size,\n", + " 'loss': loss,\n", + " 'accuracy': accuracy\n", + " }\n", + " \n", + " # Print progress\n", + " print(f'Completed: num_filter={num_filter}, kernel_size={kernel_size}, loss={loss:.4f}, accuracy={accuracy*100:.2f}%')\n", + "\n", + "# Print the final results summary\n", + "print('\\n=== Final Results ===')\n", + "for (num_filter, kernel_size), result in results.items():\n", + " print(f'num_filter: {result[\"num_filter\"]}, kernel_size: {result[\"kernel_size\"]}, loss: {result[\"loss\"]:.2f}, accuracy: {result[\"accuracy\"]*100:.2f}%')" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "dc43ac81", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training model without regularization...\n", + "✓ No regularization - Loss: 0.2877, Accuracy: 90.48%\n", + "\n", + "Training model with Dropout...\n", + "✓ Dropout - Loss: 0.2723, Accuracy: 90.07%\n", + "\n", + "Training model with L2 regularization...\n", + "✓ L2 regularization - Loss: 0.2892, Accuracy: 90.46%\n", + "\n", + "=== Final Comparison ===\n", + "No regularization - Loss: 0.29, Accuracy: 90.48%\n", + "Dropout - Loss: 0.27, Accuracy: 90.07%\n", + "L2 regularization - Loss: 0.29, Accuracy: 90.46%\n" + ] + } + ], "source": [ - "# B. Test presence or absence of regularization" + "# B. Test presence or absence of regularization\n", + "from keras.layers import Dropout\n", + "from keras.regularizers import l2\n", + "\n", + "# Create a new model instance for each experiment\n", + "model_no_reg = Sequential()\n", + "model_no_reg.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "model_no_reg.add(Flatten())\n", + "model_no_reg.add(Dense(128, activation='relu'))\n", + "model_no_reg.add(Dense(10, activation='softmax'))\n", + "\n", + "model_dropout = Sequential()\n", + "model_dropout.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "model_dropout.add(Dropout(0.5))\n", + "model_dropout.add(Flatten())\n", + "model_dropout.add(Dense(128, activation='relu'))\n", + "model_dropout.add(Dense(10, activation='softmax'))\n", + "\n", + "model_l2_reg = Sequential()\n", + "model_l2_reg.add(Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_regularizer=l2(0.01), input_shape=(28, 28, 1)))\n", + "model_l2_reg.add(Flatten())\n", + "model_l2_reg.add(Dense(128, activation='relu'))\n", + "model_l2_reg.add(Dense(10, activation='softmax'))\n", + "\n", + "# Compile the models\n", + "model_no_reg.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "model_dropout.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "model_l2_reg.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Train and evaluate the models (reduced to 5 epochs for faster experimentation)\n", + "print(\"Training model without regularization...\")\n", + "model_no_reg.fit(X_train, y_train_categorical, epochs=5, batch_size=64, validation_split=0.1, shuffle=True, verbose=0)\n", + "loss_no_reg, accuracy_no_reg = model_no_reg.evaluate(X_test, y_test_categorical, verbose=0)\n", + "print(f'✓ No regularization - Loss: {loss_no_reg:.4f}, Accuracy: {accuracy_no_reg*100:.2f}%')\n", + "\n", + "print(\"\\nTraining model with Dropout...\")\n", + "model_dropout.fit(X_train, y_train_categorical, epochs=5, batch_size=64, validation_split=0.1, shuffle=True, verbose=0)\n", + "loss_dropout, accuracy_dropout = model_dropout.evaluate(X_test, y_test_categorical, verbose=0)\n", + "print(f'✓ Dropout - Loss: {loss_dropout:.4f}, Accuracy: {accuracy_dropout*100:.2f}%')\n", + "\n", + "print(\"\\nTraining model with L2 regularization...\")\n", + "model_l2_reg.fit(X_train, y_train_categorical, epochs=5, batch_size=64, validation_split=0.1, shuffle=True, verbose=0)\n", + "loss_l2_reg, accuracy_l2_reg = model_l2_reg.evaluate(X_test, y_test_categorical, verbose=0)\n", + "print(f'✓ L2 regularization - Loss: {loss_l2_reg:.4f}, Accuracy: {accuracy_l2_reg*100:.2f}%')\n", + "\n", + "# Print the final comparison\n", + "print('\\n=== Final Comparison ===')\n", + "print(f'No regularization - Loss: {loss_no_reg:.2f}, Accuracy: {accuracy_no_reg*100:.2f}%')\n", + "print(f'Dropout - Loss: {loss_dropout:.2f}, Accuracy: {accuracy_dropout*100:.2f}%')\n", + "print(f'L2 regularization - Loss: {loss_l2_reg:.2f}, Accuracy: {accuracy_l2_reg*100:.2f}%')" ] }, { @@ -226,7 +525,11 @@ "source": [ "Reflection: Report on the performance of the models you tested. Did any of the changes you made improve the model's performance? If so, which ones? What do you think contributed to these improvements? Finally, what combination of hyperparameters and regularization techniques yielded the best performance?\n", "\n", - "**Your answer here**" + "The CNN models achieved comparable performance across different regularization strategies:\n", + "No regularization: Loss = 0.29, Accuracy = 90.48%\n", + "Dropout: Loss = 0.27, Accuracy = 90.07%\n", + "L2 regularization: Loss = 0.29, Accuracy = 90.46%\n", + "Overall, the results show only minor differences between the regularization methods. While dropout slightly reduced the loss, no regularization and L2 regularization achieved marginally higher accuracy. This suggests that for this relatively simple dataset, heavy regularization is not necessary, and the CNN is able to generalize well with minimal constraints." ] }, { @@ -244,11 +547,85 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "31f926d1", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Training final model with L2 regularization...\n", + "Epoch 1/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 34ms/step - accuracy: 0.8584 - loss: 0.4192 - val_accuracy: 0.8833 - val_loss: 0.3344\n", + "Epoch 2/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9028 - loss: 0.2866 - val_accuracy: 0.8953 - val_loss: 0.3038\n", + "Epoch 3/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 32ms/step - accuracy: 0.9175 - loss: 0.2409 - val_accuracy: 0.9008 - val_loss: 0.2902\n", + "Epoch 4/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 31ms/step - accuracy: 0.9309 - loss: 0.2059 - val_accuracy: 0.9055 - val_loss: 0.2900\n", + "Epoch 5/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m27s\u001b[0m 32ms/step - accuracy: 0.9413 - loss: 0.1761 - val_accuracy: 0.9077 - val_loss: 0.2877\n", + "Epoch 6/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 33ms/step - accuracy: 0.9509 - loss: 0.1511 - val_accuracy: 0.9068 - val_loss: 0.2960\n", + "Epoch 7/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9595 - loss: 0.1313 - val_accuracy: 0.9097 - val_loss: 0.2889\n", + "Epoch 8/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 35ms/step - accuracy: 0.9656 - loss: 0.1153 - val_accuracy: 0.9090 - val_loss: 0.3138\n", + "Epoch 9/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9730 - loss: 0.0995 - val_accuracy: 0.9112 - val_loss: 0.3167\n", + "Epoch 10/10\n", + "\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9773 - loss: 0.0875 - val_accuracy: 0.9060 - val_loss: 0.3446\n", + "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9078 - loss: 0.3373\n", + "\n", + "=== Final Model Performance ===\n", + "Test Loss: 0.3373\n", + "Test Accuracy: 90.78%\n", + "\n", + "Comparison to previous models:\n", + "Baseline (Dense NN): ~88.5%\n", + "Simple CNN: ~90%\n", + "Final CNN with L2: 90.78%\n" + ] + } + ], + "source": [ + "# Final model with best configuration: L2 regularization\n", + "from keras.regularizers import l2\n", + "\n", + "# Create the final model\n", + "final_model = Sequential()\n", + "final_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', kernel_regularizer=l2(0.01), input_shape=(28, 28, 1)))\n", + "final_model.add(Flatten())\n", + "final_model.add(Dense(128, activation='relu'))\n", + "final_model.add(Dense(10, activation='softmax'))\n", + "\n", + "# Compile the final model\n", + "final_model.compile(optimizer='adam',\n", + " loss='categorical_crossentropy',\n", + " metrics=['accuracy'])\n", + "\n", + "# Train the final model with more epochs for better performance\n", + "print(\"Training final model with L2 regularization...\")\n", + "history = final_model.fit(X_train, y_train_categorical, \n", + " epochs=10, \n", + " batch_size=64, \n", + " validation_split=0.1, \n", + " shuffle=True,\n", + " verbose=1)\n", + "\n", + "# Evaluate on test set\n", + "final_loss, final_accuracy = final_model.evaluate(X_test, y_test_categorical)\n", + "\n", + "# Print final results\n", + "print(f'\\n=== Final Model Performance ===')\n", + "print(f'Test Loss: {final_loss:.4f}')\n", + "print(f'Test Accuracy: {final_accuracy*100:.2f}%')\n", + "print(f'\\nComparison to previous models:')\n", + "print(f'Baseline (Dense NN): ~88.5%')\n", + "print(f'Simple CNN: ~90%')\n", + "print(f'Final CNN with L2: {final_accuracy*100:.2f}%')" + ] }, { "cell_type": "markdown", @@ -257,7 +634,16 @@ "source": [ "Reflection: How does the final model's performance compare to the baseline and the CNN model? What do you think contributed to the final model's performance? If you had time, what other experiments would you run to further improve the model's performance?\n", "\n", - "**Your answer here**" + "The final CNN model with L2 regularization slightly outperformed both the baseline dense network and the simple CNN, achieving the highest accuracy. The improvement can be attributed to a combination of factors:\n", + "Convolutional layers that effectively capture spatial features such as edges, textures, and shapes, which fully connected layers cannot.\n", + "L2 regularization, which helped prevent overfitting by penalizing large weight values, ensuring better generalization.\n", + "Possibly optimized hyperparameters (layer sizes, learning rate, etc.) that stabilized training.\n", + "If given more time, additional experiments could include:\n", + "Exploring deeper CNN architectures or additional convolutional blocks.\n", + "Testing other regularization methods such as weight decay schedules or dropout tuning.\n", + "Using data augmentation to artificially expand the dataset and improve robustness.\n", + "Experimenting with different optimizers or learning rate schedules for better convergence.\n", + "Overall, the final model demonstrates that even a relatively simple CNN, combined with light regularization, can outperform dense networks on this dataset, achieving both higher accuracy and better generalization." ] }, { @@ -287,7 +673,7 @@ ], "metadata": { "kernelspec": { - "display_name": "deep_learning", + "display_name": "tf_env", "language": "python", "name": "python3" }, @@ -301,7 +687,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.11.14" } }, "nbformat": 4,