[MISC] Introduction on Neural Networks

.github/workflows/ci.yaml

@@ -21,6 +21,8 @@ jobs:
             folder: Chapter02
           - name: chap3
             folder: Chapter03
+          - name: chap-nn
+            folder: ChapterNN
           - name: chap5
             folder: Chapter05
           - name: chap6

ChapterNN/01_ImageClassification_TensorFlow.ipynb

@@ -0,0 +1,237 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8c4e9e09-b0c6-4671-86a0-ca6bc73056b6",
+   "metadata": {},
+   "source": [
+    "# Image Classification with TensorFlow"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "012237ce-0396-4c88-8b13-994c7a830421",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from tensorflow.keras.datasets import fashion_mnist\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6080305f-eaac-49d2-a4cb-658a907186ac",
+   "metadata": {},
+   "source": [
+    "### Load and re-scale input data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1135c9e8-1765-48cc-beb8-25fd6ff363d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ee2989f4-9b32-48f6-b669-8255aa9e9c79",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = x_train.astype('float32') / 255.\n",
+    "x_test = x_test.astype('float32') / 255.\n",
+    "\n",
+    "print (x_train.shape)\n",
+    "print (x_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0525097f-4b57-4e9c-b850-966540589a30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classes = {\n",
+    "    0: \"T-shirt\",\n",
+    "    1: \"Trouser\",\n",
+    "    2: \"Pullover\",\n",
+    "    3: \"Dress\",\n",
+    "    4: \"Coat\",\n",
+    "    5: \"Sandal\",\n",
+    "    6: \"Shirt\",\n",
+    "    7: \"Sneaker\",\n",
+    "    8: \"Bag\",\n",
+    "    9: \"Ankle boot\", \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a0e2bc95-ee33-4024-99d2-4ba7cb4fd0c6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 6\n",
+    "plt.figure(figsize=(20, 4))\n",
+    "for i in range(n):\n",
+    "    # display original\n",
+    "    ax = plt.subplot(1, n, i + 1)\n",
+    "    plt.imshow(x_test[i])\n",
+    "    plt.title(classes[y_test[i]])\n",
+    "    plt.gray()\n",
+    "    ax.get_xaxis().set_visible(False)\n",
+    "    ax.get_yaxis().set_visible(False)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7ace701-0cd8-4173-982c-8682a860dd26",
+   "metadata": {},
+   "source": [
+    "### Build model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a2d549c8-a410-4caa-95a4-b0bb20a05236",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = tf.keras.models.Sequential([\n",
+    "  tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
+    "  tf.keras.layers.Dense(128, activation='relu'),\n",
+    "  tf.keras.layers.Dropout(0.2),\n",
+    "  tf.keras.layers.Dense(10)\n",
+    "])\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f20ef704-3435-4f12-b41b-db42fcfb3b43",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9af979aa-9ccb-4b92-b0fd-ef39fcf6f317",
+   "metadata": {},
+   "source": [
+    "### Train the network"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53f02c48-3d8f-4e41-a9d4-703016a0dc19",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
+    "optimizer = tf.keras.optimizers.Adam()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6427e500-41d2-43f1-b235-622d1d59572e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.compile(optimizer=optimizer,\n",
+    "              loss=loss_fn,\n",
+    "              metrics=['accuracy'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ef1fea96-97cc-4c84-bb0c-78417a571575",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.fit(\n",
+    "    x_train, \n",
+    "    y_train, \n",
+    "    validation_data=(x_test, y_test), \n",
+    "    epochs=20, \n",
+    "    batch_size=128,\n",
+    "    shuffle=True\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8c64c99-cbab-4503-8a1d-84f80c6f2af7",
+   "metadata": {},
+   "source": [
+    "### More advanced model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c4649c36-1157-438b-bff0-01e980aa7da3",
+   "metadata": {},
+   "source": [
+    "For a slightly more complex and deeper network, try to train the model below"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "e9af3bfc-7b19-4441-9dc4-46df1b3739cc",
+   "metadata": {},
+   "source": [
+    "input_img = tf.keras.layers.Input(shape=(28, 28, 1))\n",
+    "\n",
+    "x = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', padding='same')(input_img)\n",
+    "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = tf.keras.layers.Conv2D(8, (3, 3), activation='relu', padding='same')(x)\n",
+    "x = tf.keras.layers.MaxPooling2D((2, 2), padding='same')(x)\n",
+    "x = tf.keras.layers.Flatten(input_shape=(26, 26))(x)\n",
+    "x = tf.keras.layers.Dense(128, activation='relu')(x)\n",
+    "x = tf.keras.layers.Dropout(0.2)(x)\n",
+    "x = tf.keras.layers.Dense(10)(x)\n",
+    "\n",
+    "model = tf.keras.Model(input_img, x)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "chap-nn",
+   "language": "python",
+   "name": "chap-nn"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.18"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}