diff --git a/cnn.ipynb b/cnn.ipynb new file mode 100644 index 0000000..c67525c --- /dev/null +++ b/cnn.ipynb @@ -0,0 +1,393 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "Mhp4GfwTjOee" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/andremicci/Generative_Deep_Learning_2nd_Edition.git" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "KOnmreJbjLIz", + "outputId": "a3a49f71-3b29-41cb-fc11-a031442b32a9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Cloning into 'Generative_Deep_Learning_2nd_Edition'...\n", + "remote: Enumerating objects: 653, done.\u001b[K\n", + "remote: Counting objects: 100% (195/195), done.\u001b[K\n", + "remote: Compressing objects: 100% (70/70), done.\u001b[K\n", + "remote: Total 653 (delta 142), reused 125 (delta 125), pack-reused 458 (from 1)\u001b[K\n", + "Receiving objects: 100% (653/653), 37.09 MiB | 8.67 MiB/s, done.\n", + "Resolving deltas: 100% (377/377), done.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TFy-fuvQjJJp" + }, + "source": [ + "# ๐Ÿž Convolutional Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wEIaaUnijJJr" + }, + "source": [ + "In this notebook, we'll walk through the steps required to train your own convolutional neural network (CNN) on the CIFAR dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350 + }, + "id": "Ewl7DskxjJJs", + "outputId": "72f7b930-03ff-4bab-97c7-8f337c0da519" + }, + "outputs": [ + { + "output_type": "error", + "ename": "ModuleNotFoundError", + "evalue": "No module named 'notebooks'", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeras\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mlayers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutils\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mnotebooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'notebooks'", + "", + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n" + ], + "errorDetails": { + "actions": [ + { + "action": "open_url", + "actionText": "Open Examples", + "url": "/notebooks/snippets/importing_libraries.ipynb" + } + ] + } + } + ], + "source": [ + "import numpy as np\n", + "\n", + "from tensorflow.keras import layers, models, optimizers, utils, datasets\n", + "from notebooks.utils import display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "5xHzrj8jjJJu" + }, + "source": [ + "## 0. Parameters " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4dTmq7jjjJJv" + }, + "outputs": [], + "source": [ + "NUM_CLASSES = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MTLZ1IG6jJJw" + }, + "source": [ + "## 1. Prepare the Data " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DR22w6jsjJJx" + }, + "outputs": [], + "source": [ + "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gkQBN7pojJJx" + }, + "outputs": [], + "source": [ + "x_train = x_train.astype(\"float32\") / 255.0\n", + "x_test = x_test.astype(\"float32\") / 255.0\n", + "\n", + "y_train = utils.to_categorical(y_train, NUM_CLASSES)\n", + "y_test = utils.to_categorical(y_test, NUM_CLASSES)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "p6D1z7qPjJJz" + }, + "outputs": [], + "source": [ + "display(x_train[:10])\n", + "print(y_train[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uqly1nGYjJJ0" + }, + "source": [ + "## 2. Build the model " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "rvLwhVf7jJJ1" + }, + "outputs": [], + "source": [ + "input_layer = layers.Input((32, 32, 3))\n", + "\n", + "x = layers.Conv2D(filters=32, kernel_size=3, strides=1, padding=\"same\")(\n", + " input_layer\n", + ")\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.LeakyReLU()(x)\n", + "\n", + "x = layers.Conv2D(filters=32, kernel_size=3, strides=2, padding=\"same\")(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.LeakyReLU()(x)\n", + "\n", + "x = layers.Conv2D(filters=64, kernel_size=3, strides=1, padding=\"same\")(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.LeakyReLU()(x)\n", + "\n", + "x = layers.Conv2D(filters=64, kernel_size=3, strides=2, padding=\"same\")(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.LeakyReLU()(x)\n", + "\n", + "x = layers.Flatten()(x)\n", + "\n", + "x = layers.Dense(128)(x)\n", + "x = layers.BatchNormalization()(x)\n", + "x = layers.LeakyReLU()(x)\n", + "x = layers.Dropout(rate=0.5)(x)\n", + "\n", + "x = layers.Dense(NUM_CLASSES)(x)\n", + "output_layer = layers.Activation(\"softmax\")(x)\n", + "\n", + "model = models.Model(input_layer, output_layer)\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "_0JEpfyWjJJ3" + }, + "source": [ + "## 3. Train the model " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "RQWD-6i6jJJ4" + }, + "outputs": [], + "source": [ + "opt = optimizers.Adam(learning_rate=0.0005)\n", + "model.compile(\n", + " loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [], + "id": "pajMd0T0jJJ5" + }, + "outputs": [], + "source": [ + "model.fit(\n", + " x_train,\n", + " y_train,\n", + " batch_size=32,\n", + " epochs=10,\n", + " shuffle=True,\n", + " validation_data=(x_test, y_test),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "M2Jl9j7ljJJ5" + }, + "source": [ + "## 4. Evaluation " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "t7GXyVvYjJJ6" + }, + "outputs": [], + "source": [ + "model.evaluate(x_test, y_test, batch_size=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Ttvkm2g_jJJ6" + }, + "outputs": [], + "source": [ + "CLASSES = np.array(\n", + " [\n", + " \"airplane\",\n", + " \"automobile\",\n", + " \"bird\",\n", + " \"cat\",\n", + " \"deer\",\n", + " \"dog\",\n", + " \"frog\",\n", + " \"horse\",\n", + " \"ship\",\n", + " \"truck\",\n", + " ]\n", + ")\n", + "\n", + "preds = model.predict(x_test)\n", + "preds_single = CLASSES[np.argmax(preds, axis=-1)]\n", + "actual_single = CLASSES[np.argmax(y_test, axis=-1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5SkFQ0pHjJJ7" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "n_to_show = 10\n", + "indices = np.random.choice(range(len(x_test)), n_to_show)\n", + "\n", + "fig = plt.figure(figsize=(15, 3))\n", + "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n", + "\n", + "for i, idx in enumerate(indices):\n", + " img = x_test[idx]\n", + " ax = fig.add_subplot(1, n_to_show, i + 1)\n", + " ax.axis(\"off\")\n", + " ax.text(\n", + " 0.5,\n", + " -0.35,\n", + " \"pred = \" + str(preds_single[idx]),\n", + " fontsize=10,\n", + " ha=\"center\",\n", + " transform=ax.transAxes,\n", + " )\n", + " ax.text(\n", + " 0.5,\n", + " -0.7,\n", + " \"act = \" + str(actual_single[idx]),\n", + " fontsize=10,\n", + " ha=\"center\",\n", + " transform=ax.transAxes,\n", + " )\n", + " ax.imshow(img)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + }, + "colab": { + "provenance": [], + "include_colab_link": true + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/notebooks/02_deeplearning/01_mlp/mlp.ipynb b/notebooks/02_deeplearning/01_mlp/mlp.ipynb index 565a3ab..401044c 100644 --- a/notebooks/02_deeplearning/01_mlp/mlp.ipynb +++ b/notebooks/02_deeplearning/01_mlp/mlp.ipynb @@ -1,251 +1,463 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ๐Ÿ‘€ Multilayer perceptron (MLP)" - ] + "cells": [ + { + "cell_type": "code", + "source": [ + "!git clone https://github.com/andremicci/Generative_Deep_Learning_2nd_Edition.git" + ], + "metadata": { + "id": "NQO_FnPJrntz", + "outputId": "c90bfe61-fab4-4041-936b-28eaa6d76ac0", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "fatal: destination path 'Generative_Deep_Learning_2nd_Edition' already exists and is not an empty directory.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xygBEiUNrm_d" + }, + "source": [ + "# ๐Ÿ‘€ Multilayer perceptron (MLP)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hRT6yWgtrm_h" + }, + "source": [ + "In this notebook, we'll walk through the steps required to train your own multilayer perceptron on the CIFAR dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "f5PzrICOrm_i" + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import sys\n", + "sys.path.append(\"/content/Generative_Deep_Learning_2nd_Edition\")\n", + "from tensorflow.keras import layers, models, optimizers, utils, datasets\n", + "from notebooks.utils import display" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "3aJHxoZvrm_j" + }, + "source": [ + "## 0. Parameters " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "6Hu7cWzvrm_k" + }, + "outputs": [], + "source": [ + "NUM_CLASSES = 10" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9af8U9QZrm_l" + }, + "source": [ + "## 1. Prepare the Data " + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "u9XozgvWrm_m", + "outputId": "f4aa2262-0bf0-4a9b-a87d-65aee918d193", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n", + "\u001b[1m170498071/170498071\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 0us/step\n" + ] + } + ], + "source": [ + "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "1kzNwzMMrm_m" + }, + "outputs": [], + "source": [ + "x_train = x_train.astype(\"float32\") / 255.0\n", + "x_test = x_test.astype(\"float32\") / 255.0\n", + "\n", + "y_train = utils.to_categorical(y_train, NUM_CLASSES)\n", + "y_test = utils.to_categorical(y_test, NUM_CLASSES)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "7xJaekznrm_n", + "outputId": "e4e44106-b63b-486e-8fe1-b3bc8d02e16a", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 310 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n", + " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n", + " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "display(x_train[:10])\n", + "print(y_train[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IUrp0BqVrm_o" + }, + "source": [ + "## 2. Build the model " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "riBxz6Rtrm_o", + "outputId": "8bfebedf-f7b7-40c1-fff8-e65963831c3e", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1mModel: \"functional\"\u001b[0m\n" + ], + "text/html": [ + "
Model: \"functional\"\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n", + "โ”ƒ\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0mโ”ƒ\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0mโ”ƒ\n", + "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n", + "โ”‚ input_layer (\u001b[38;5;33mInputLayer\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m3\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ flatten (\u001b[38;5;33mFlatten\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3072\u001b[0m) โ”‚ \u001b[38;5;34m0\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m200\u001b[0m) โ”‚ \u001b[38;5;34m614,600\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_1 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m150\u001b[0m) โ”‚ \u001b[38;5;34m30,150\u001b[0m โ”‚\n", + "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n", + "โ”‚ dense_2 (\u001b[38;5;33mDense\u001b[0m) โ”‚ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) โ”‚ \u001b[38;5;34m1,510\u001b[0m โ”‚\n", + "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n" + ], + "text/html": [ + "
โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ณโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”“\n",
+              "โ”ƒ Layer (type)                         โ”ƒ Output Shape                โ”ƒ         Param # โ”ƒ\n",
+              "โ”กโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ•‡โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”ฉ\n",
+              "โ”‚ input_layer (InputLayer)             โ”‚ (None, 32, 32, 3)           โ”‚               0 โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ flatten (Flatten)                    โ”‚ (None, 3072)                โ”‚               0 โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ dense (Dense)                        โ”‚ (None, 200)                 โ”‚         614,600 โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ dense_1 (Dense)                      โ”‚ (None, 150)                 โ”‚          30,150 โ”‚\n",
+              "โ”œโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ผโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ค\n",
+              "โ”‚ dense_2 (Dense)                      โ”‚ (None, 10)                  โ”‚           1,510 โ”‚\n",
+              "โ””โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”ดโ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”€โ”˜\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m646,260\u001b[0m (2.47 MB)\n" + ], + "text/html": [ + "
 Total params: 646,260 (2.47 MB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m646,260\u001b[0m (2.47 MB)\n" + ], + "text/html": [ + "
 Trainable params: 646,260 (2.47 MB)\n",
+              "
\n" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n" + ], + "text/html": [ + "
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+              "
\n" + ] + }, + "metadata": {} + } + ], + "source": [ + "input_layer = layers.Input((32, 32, 3))\n", + "\n", + "x = layers.Flatten()(input_layer)\n", + "x = layers.Dense(200, activation=\"relu\")(x)\n", + "x = layers.Dense(150, activation=\"relu\")(x)\n", + "\n", + "output_layer = layers.Dense(NUM_CLASSES, activation=\"softmax\")(x)\n", + "\n", + "model = models.Model(input_layer, output_layer)\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "6ajw_QwRrm_p" + }, + "source": [ + "## 3. Train the model " + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "id": "pTtLug7erm_q" + }, + "outputs": [], + "source": [ + "opt = optimizers.Adam(learning_rate=0.0005)\n", + "model.compile(\n", + " loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "uv1z7aJlrm_q", + "outputId": "e15fd92f-ba04-465f-e000-cb7f54b03cf9", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Epoch 1/10\n", + "\u001b[1m 576/1563\u001b[0m \u001b[32mโ”โ”โ”โ”โ”โ”โ”\u001b[0m\u001b[37mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[1m13s\u001b[0m 14ms/step - accuracy: 0.2452 - loss: 2.0665" + ] + } + ], + "source": [ + "model.fit(x_train, y_train, batch_size=32, epochs=10, shuffle=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "tags": [], + "id": "uuX-0QB4rm_r" + }, + "source": [ + "## 4. Evaluation " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ruBp11Derm_r" + }, + "outputs": [], + "source": [ + "model.evaluate(x_test, y_test)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DE_FObkerm_r" + }, + "outputs": [], + "source": [ + "CLASSES = np.array(\n", + " [\n", + " \"airplane\",\n", + " \"automobile\",\n", + " \"bird\",\n", + " \"cat\",\n", + " \"deer\",\n", + " \"dog\",\n", + " \"frog\",\n", + " \"horse\",\n", + " \"ship\",\n", + " \"truck\",\n", + " ]\n", + ")\n", + "\n", + "preds = model.predict(x_test)\n", + "preds_single = CLASSES[np.argmax(preds, axis=-1)]\n", + "actual_single = CLASSES[np.argmax(y_test, axis=-1)]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true, + "id": "I0qoBXixrm_s" + }, + "outputs": [], + "source": [ + "n_to_show = 10\n", + "indices = np.random.choice(range(len(x_test)), n_to_show)\n", + "\n", + "fig = plt.figure(figsize=(15, 3))\n", + "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n", + "\n", + "for i, idx in enumerate(indices):\n", + " img = x_test[idx]\n", + " ax = fig.add_subplot(1, n_to_show, i + 1)\n", + " ax.axis(\"off\")\n", + " ax.text(\n", + " 0.5,\n", + " -0.35,\n", + " \"pred = \" + str(preds_single[idx]),\n", + " fontsize=10,\n", + " ha=\"center\",\n", + " transform=ax.transAxes,\n", + " )\n", + " ax.text(\n", + " 0.5,\n", + " -0.7,\n", + " \"act = \" + str(actual_single[idx]),\n", + " fontsize=10,\n", + " ha=\"center\",\n", + " transform=ax.transAxes,\n", + " )\n", + " ax.imshow(img)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "vscode": { + "interpreter": { + "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" + } + }, + "colab": { + "provenance": [] + } }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this notebook, we'll walk through the steps required to train your own multilayer perceptron on the CIFAR dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from tensorflow.keras import layers, models, optimizers, utils, datasets\n", - "from notebooks.utils import display" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## 0. Parameters " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NUM_CLASSES = 10" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Prepare the Data " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(x_train, y_train), (x_test, y_test) = datasets.cifar10.load_data()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x_train = x_train.astype(\"float32\") / 255.0\n", - "x_test = x_test.astype(\"float32\") / 255.0\n", - "\n", - "y_train = utils.to_categorical(y_train, NUM_CLASSES)\n", - "y_test = utils.to_categorical(y_test, NUM_CLASSES)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "display(x_train[:10])\n", - "print(y_train[:10])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Build the model " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "input_layer = layers.Input((32, 32, 3))\n", - "\n", - "x = layers.Flatten()(input_layer)\n", - "x = layers.Dense(200, activation=\"relu\")(x)\n", - "x = layers.Dense(150, activation=\"relu\")(x)\n", - "\n", - "output_layer = layers.Dense(NUM_CLASSES, activation=\"softmax\")(x)\n", - "\n", - "model = models.Model(input_layer, output_layer)\n", - "\n", - "model.summary()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## 3. Train the model " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "opt = optimizers.Adam(learning_rate=0.0005)\n", - "model.compile(\n", - " loss=\"categorical_crossentropy\", optimizer=opt, metrics=[\"accuracy\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.fit(x_train, y_train, batch_size=32, epochs=10, shuffle=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## 4. Evaluation " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "model.evaluate(x_test, y_test)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "CLASSES = np.array(\n", - " [\n", - " \"airplane\",\n", - " \"automobile\",\n", - " \"bird\",\n", - " \"cat\",\n", - " \"deer\",\n", - " \"dog\",\n", - " \"frog\",\n", - " \"horse\",\n", - " \"ship\",\n", - " \"truck\",\n", - " ]\n", - ")\n", - "\n", - "preds = model.predict(x_test)\n", - "preds_single = CLASSES[np.argmax(preds, axis=-1)]\n", - "actual_single = CLASSES[np.argmax(y_test, axis=-1)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "n_to_show = 10\n", - "indices = np.random.choice(range(len(x_test)), n_to_show)\n", - "\n", - "fig = plt.figure(figsize=(15, 3))\n", - "fig.subplots_adjust(hspace=0.4, wspace=0.4)\n", - "\n", - "for i, idx in enumerate(indices):\n", - " img = x_test[idx]\n", - " ax = fig.add_subplot(1, n_to_show, i + 1)\n", - " ax.axis(\"off\")\n", - " ax.text(\n", - " 0.5,\n", - " -0.35,\n", - " \"pred = \" + str(preds_single[idx]),\n", - " fontsize=10,\n", - " ha=\"center\",\n", - " transform=ax.transAxes,\n", - " )\n", - " ax.text(\n", - " 0.5,\n", - " -0.7,\n", - " \"act = \" + str(actual_single[idx]),\n", - " fontsize=10,\n", - " ha=\"center\",\n", - " transform=ax.transAxes,\n", - " )\n", - " ax.imshow(img)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "vscode": { - "interpreter": { - "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file