From 67cf5a58b42fe347bc41633109494a484f007870 Mon Sep 17 00:00:00 2001
From: Mohamed Hisham Abdelzaher <11410120200483@stud.cu.edu.eg>
Date: Mon, 11 Dec 2023 18:56:50 +0200
Subject: [PATCH] Kaggle Notebook | Facial Emotions Detection | Version 1
---
facial_emotions_detection.ipynb | 420 +-------------------------------
1 file changed, 1 insertion(+), 419 deletions(-)
diff --git a/facial_emotions_detection.ipynb b/facial_emotions_detection.ipynb
index 8e9ad369..8a0b7de5 100644
--- a/facial_emotions_detection.ipynb
+++ b/facial_emotions_detection.ipynb
@@ -1,419 +1 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "outputs": [],
- "source": [
- "import tensorflow as tf\n",
- "\n",
- "tf.__version__"
- ],
- "metadata": {
- "collapsed": false
- }
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:33.813523Z",
- "iopub.status.busy": "2023-07-25T17:32:33.813063Z",
- "iopub.status.idle": "2023-07-25T17:32:33.823764Z",
- "shell.execute_reply": "2023-07-25T17:32:33.822594Z",
- "shell.execute_reply.started": "2023-07-25T17:32:33.813487Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "import os\n",
- "import matplotlib.pyplot as plt\n",
- "import tensorflow as tf\n",
- "import numpy as np\n",
- "from keras.preprocessing.image import ImageDataGenerator, load_img\n",
- "from keras.layers import (\n",
- " Dense,\n",
- " Dropout,\n",
- " Flatten,\n",
- " Conv2D,\n",
- " MaxPooling2D,\n",
- ")\n",
- "from keras.optimizers import Adam"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:33.856871Z",
- "iopub.status.busy": "2023-07-25T17:32:33.856532Z",
- "iopub.status.idle": "2023-07-25T17:32:33.863955Z",
- "shell.execute_reply": "2023-07-25T17:32:33.862902Z",
- "shell.execute_reply.started": "2023-07-25T17:32:33.856841Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "tf.config.experimental.list_physical_devices(\"GPU\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "pathToLocal = \"images/validation/\"\n",
- "pathToKaggle = \"/kaggle/input/face-expression-recognition-dataset/images/validation/\"\n",
- "\n",
- "path = pathToLocal if os.path.exists(pathToLocal) else pathToKaggle"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:33.867117Z",
- "iopub.status.busy": "2023-07-25T17:32:33.866398Z",
- "iopub.status.idle": "2023-07-25T17:32:34.361979Z",
- "shell.execute_reply": "2023-07-25T17:32:34.360993Z",
- "shell.execute_reply.started": "2023-07-25T17:32:33.867084Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "plt.figure(figsize=(8, 8))\n",
- "for i, expression in enumerate(path, start=1):\n",
- " img = load_img(f\"images/validation/{expression}/{os.listdir(f'images/validation/{expression}')[0]}\")\n",
- " plt.subplot(1, 7, i)\n",
- " plt.imshow(img)\n",
- " plt.title(expression)\n",
- " plt.axis(\"off\")\n",
- "plt.show()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Check the amount of data in each folder in training dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:34.364825Z",
- "iopub.status.busy": "2023-07-25T17:32:34.363243Z",
- "iopub.status.idle": "2023-07-25T17:32:34.388176Z",
- "shell.execute_reply": "2023-07-25T17:32:34.387281Z",
- "shell.execute_reply.started": "2023-07-25T17:32:34.364786Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "try:\n",
- " for expression in os.listdir(\"images/train/\"):\n",
- " print(\n",
- " expression,\n",
- " \"folder contains\\t\\t\",\n",
- " len(os.listdir(\"images/train/\" + expression)),\n",
- " \"images\",\n",
- " )\n",
- "except FileNotFoundError:\n",
- " for expression in os.listdir(\n",
- " \"/kaggle/input/face-expression-recognition-dataset/images/train/\"\n",
- " ):\n",
- " print(\n",
- " expression,\n",
- " \"folder contains\\t\\t\",\n",
- " len(\n",
- " os.listdir(\n",
- " \"/kaggle/input/face-expression-recognition-dataset/images/train/\"\n",
- " + expression\n",
- " )\n",
- " ),\n",
- " \"images\",\n",
- " )"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Check the amount of data in each folder in testing dataset"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:34.390505Z",
- "iopub.status.busy": "2023-07-25T17:32:34.390222Z",
- "iopub.status.idle": "2023-07-25T17:32:34.404139Z",
- "shell.execute_reply": "2023-07-25T17:32:34.403223Z",
- "shell.execute_reply.started": "2023-07-25T17:32:34.390480Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "try:\n",
- " for expression in os.listdir(\"images/validation/\"):\n",
- " print(\n",
- " expression,\n",
- " \"folder contains\\t\\t\",\n",
- " len(os.listdir(\"images/validation/\" + expression)),\n",
- " \"images\",\n",
- " )\n",
- "except FileNotFoundError:\n",
- " for expression in os.listdir(\n",
- " \"/kaggle/input/face-expression-recognition-dataset/images/validation/\"\n",
- " ):\n",
- " print(\n",
- " expression,\n",
- " \"folder contains\\t\\t\",\n",
- " len(\n",
- " os.listdir(\n",
- " \"/kaggle/input/face-expression-recognition-dataset/images/validation/\"\n",
- " + expression\n",
- " )\n",
- " ),\n",
- " \"images\",\n",
- " )"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:34.406259Z",
- "iopub.status.busy": "2023-07-25T17:32:34.405822Z",
- "iopub.status.idle": "2023-07-25T17:32:34.534127Z",
- "shell.execute_reply": "2023-07-25T17:32:34.533350Z",
- "shell.execute_reply.started": "2023-07-25T17:32:34.406225Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "model = tf.keras.models.Sequential(\n",
- " [\n",
- " Conv2D(64, (3, 3), padding=\"same\", activation=\"relu\", input_shape=(48, 48, 1)),\n",
- " MaxPooling2D((2, 2)),\n",
- " Conv2D(128, (3, 3), padding=\"same\", activation=\"relu\"),\n",
- " MaxPooling2D((2, 2)),\n",
- " Conv2D(256, (3, 3), padding=\"same\", activation=\"relu\"),\n",
- " MaxPooling2D((2, 2)),\n",
- " Conv2D(512, (3, 3), padding=\"same\", activation=\"relu\"),\n",
- " MaxPooling2D((2, 2)),\n",
- " Flatten(),\n",
- " Dense(512, activation=\"relu\", kernel_regularizer=tf.keras.regularizers.l1()),\n",
- " Dropout(0.5),\n",
- " Dense(7, activation=\"softmax\", kernel_regularizer=tf.keras.regularizers.l1()),\n",
- " ]\n",
- ")\n",
- "\n",
- "model.compile(optimizer=Adam(), loss=\"categorical_crossentropy\", metrics=[\"accuracy\"])\n",
- "\n",
- "model.summary()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Data Augmentation"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:34.535616Z",
- "iopub.status.busy": "2023-07-25T17:32:34.535182Z",
- "iopub.status.idle": "2023-07-25T17:32:40.538703Z",
- "shell.execute_reply": "2023-07-25T17:32:40.537624Z",
- "shell.execute_reply.started": "2023-07-25T17:32:34.535558Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "try:\n",
- " datagen_train = ImageDataGenerator(\n",
- " rescale=1.0 / 255, zoom_range=0.3, horizontal_flip=True\n",
- " )\n",
- "\n",
- " train_generator = datagen_train.flow_from_directory(\n",
- " \"/kaggle/input/face-expression-recognition-dataset/images/train/\",\n",
- " batch_size=64,\n",
- " target_size=(48, 48),\n",
- " shuffle=True,\n",
- " color_mode=\"grayscale\",\n",
- " class_mode=\"categorical\",\n",
- " )\n",
- "\n",
- " datagen_test = ImageDataGenerator(\n",
- " rescale=1.0 / 255, zoom_range=0.3, horizontal_flip=True\n",
- " )\n",
- "\n",
- " test_generator = datagen_test.flow_from_directory(\n",
- " \"/kaggle/input/face-expression-recognition-dataset/images/validation/\",\n",
- " batch_size=64,\n",
- " target_size=(48, 48),\n",
- " shuffle=True,\n",
- " color_mode=\"grayscale\",\n",
- " class_mode=\"categorical\",\n",
- " )\n",
- "except FileNotFoundError:\n",
- " datagen_train = ImageDataGenerator(\n",
- " rescale=1.0 / 255, zoom_range=0.3, horizontal_flip=True\n",
- " )\n",
- "\n",
- " train_generator = datagen_train.flow_from_directory(\n",
- " \"images/train/\",\n",
- " batch_size=64,\n",
- " target_size=(48, 48),\n",
- " shuffle=True,\n",
- " color_mode=\"grayscale\",\n",
- " class_mode=\"categorical\",\n",
- " )\n",
- "\n",
- " datagen_test = ImageDataGenerator(\n",
- " rescale=1.0 / 255, zoom_range=0.3, horizontal_flip=True\n",
- " )\n",
- "\n",
- " test_generator = datagen_test.flow_from_directory(\n",
- " \"images/validation/\",\n",
- " batch_size=64,\n",
- " target_size=(48, 48),\n",
- " shuffle=True,\n",
- " color_mode=\"grayscale\",\n",
- " class_mode=\"categorical\",\n",
- " )\n",
- "\n",
- "steps_per_epoch = train_generator.n / train_generator.batch_size\n",
- "testing_steps = test_generator.n / test_generator.batch_size"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "train_generator = np.array(train_generator)\n",
- "train_generator"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T17:32:40.540723Z",
- "iopub.status.busy": "2023-07-25T17:32:40.540240Z",
- "iopub.status.idle": "2023-07-25T19:16:49.938685Z",
- "shell.execute_reply": "2023-07-25T19:16:49.937680Z",
- "shell.execute_reply.started": "2023-07-25T17:32:40.540685Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "history = model.fit(x=train_generator, epochs=100, validation_split=0.2, batch_size=10)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false
- },
- "outputs": [],
- "source": [
- "model.evaluate(x=test_generator, steps=testing_steps)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Plot the training and validation accuracy and loss at each epoch"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "collapsed": false,
- "execution": {
- "iopub.execute_input": "2023-07-25T19:16:49.940946Z",
- "iopub.status.busy": "2023-07-25T19:16:49.940262Z",
- "iopub.status.idle": "2023-07-25T19:16:50.297499Z",
- "shell.execute_reply": "2023-07-25T19:16:50.296490Z",
- "shell.execute_reply.started": "2023-07-25T19:16:49.940908Z"
- },
- "trusted": true
- },
- "outputs": [],
- "source": [
- "loss = history.history[\"loss\"]\n",
- "val_loss = history.history[\"val_loss\"]\n",
- "epochs = range(1, len(loss) + 1)\n",
- "plt.plot(epochs, loss, \"y\", label=\"Training loss\")\n",
- "plt.plot(epochs, val_loss, \"r\", label=\"Validation loss\")\n",
- "acc = history.history[\"accuracy\"]\n",
- "val_acc = history.history[\"val_accuracy\"]\n",
- "plt.plot(epochs, acc, label=\"Training acc\")\n",
- "plt.plot(epochs, val_acc, \"r\", label=\"Validation acc\")\n",
- "plt.title(\"Training and Validation Accuracy\")\n",
- "plt.xlabel(\"Epochs\")\n",
- "plt.ylabel(\"Loss\")\n",
- "plt.legend()\n",
- "plt.show()"
- ]
- }
- ],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "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": "3.10.12"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+{"cells":[{"source":"![\"Kaggle\"](\"https://kaggle.com/static/images/open-in-kaggle.svg\" "\\"Open")
","metadata":{},"cell_type":"markdown"},{"cell_type":"code","execution_count":1,"id":"cd579715","metadata":{"ExecuteTime":{"end_time":"2023-07-24T18:55:40.362049600Z","start_time":"2023-07-24T18:55:40.324052Z"},"execution":{"iopub.execute_input":"2023-12-11T16:13:01.900277Z","iopub.status.busy":"2023-12-11T16:13:01.899913Z","iopub.status.idle":"2023-12-11T16:13:11.089966Z","shell.execute_reply":"2023-12-11T16:13:11.089163Z"},"papermill":{"duration":9.198647,"end_time":"2023-12-11T16:13:11.092286","exception":false,"start_time":"2023-12-11T16:13:01.893639","status":"completed"},"tags":[]},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n"," warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:98: UserWarning: unable to load libtensorflow_io_plugins.so: unable to open file: libtensorflow_io_plugins.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so']\n","caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io_plugins.so: undefined symbol: _ZN3tsl6StatusC1EN10tensorflow5error4CodeESt17basic_string_viewIcSt11char_traitsIcEENS_14SourceLocationE']\n"," warnings.warn(f\"unable to load libtensorflow_io_plugins.so: {e}\")\n","/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/__init__.py:104: UserWarning: file system plugins are not loaded: unable to open file: libtensorflow_io.so, from paths: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so']\n","caused by: ['/opt/conda/lib/python3.10/site-packages/tensorflow_io/python/ops/libtensorflow_io.so: undefined symbol: _ZTVN10tensorflow13GcsFileSystemE']\n"," warnings.warn(f\"file system plugins are not loaded: {e}\")\n"]}],"source":["import os\n","import matplotlib.pyplot as plt\n","import tensorflow as tf\n","import numpy as np\n","from tensorflow.keras.preprocessing.image import ImageDataGenerator, load_img\n","from tensorflow.keras.layers import Dense, Input, Dropout, Flatten, Conv2D\n","from tensorflow.keras.layers import BatchNormalization, Activation, MaxPooling2D\n","from tensorflow.keras.optimizers import Adam\n","from tensorflow.keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\n","from tensorflow.keras.utils import plot_model"]},{"cell_type":"code","execution_count":2,"id":"88c5da6e","metadata":{"execution":{"iopub.execute_input":"2023-12-11T16:13:11.1036Z","iopub.status.busy":"2023-12-11T16:13:11.103017Z","iopub.status.idle":"2023-12-11T16:13:11.419261Z","shell.execute_reply":"2023-12-11T16:13:11.418308Z"},"papermill":{"duration":0.323779,"end_time":"2023-12-11T16:13:11.421319","exception":false,"start_time":"2023-12-11T16:13:11.09754","status":"completed"},"tags":[]},"outputs":[{"data":{"text/plain":["[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU'),\n"," PhysicalDevice(name='/physical_device:GPU:1', device_type='GPU')]"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["tf.config.experimental.list_physical_devices('GPU')"]},{"cell_type":"code","execution_count":3,"id":"0b686d23","metadata":{"ExecuteTime":{"end_time":"2023-07-24T18:55:41.148001500Z","start_time":"2023-07-24T18:55:40.341069600Z"},"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:13:11.432308Z","iopub.status.busy":"2023-12-11T16:13:11.431998Z","iopub.status.idle":"2023-12-11T16:13:13.391915Z","shell.execute_reply":"2023-12-11T16:13:13.391002Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":1.967625,"end_time":"2023-12-11T16:13:13.393996","exception":false,"start_time":"2023-12-11T16:13:11.426371","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["i = 1\n","plt.figure(figsize=(8, 8))\n","for expression in os.listdir('/kaggle/input/face-expression-recognition-dataset/images/validation/'):\n"," img = load_img(('/kaggle/input/face-expression-recognition-dataset/images/validation/' + expression + '/' +\n"," os.listdir('/kaggle/input/face-expression-recognition-dataset/images/validation/' + expression)[0]))\n"," plt.subplot(1, 7, i)\n"," plt.imshow(img)\n"," plt.title(expression)\n"," plt.axis('off')\n"," i += 1\n","\n","plt.show()"]},{"cell_type":"markdown","id":"4b9f1a81","metadata":{"papermill":{"duration":0.005154,"end_time":"2023-12-11T16:13:13.404638","exception":false,"start_time":"2023-12-11T16:13:13.399484","status":"completed"},"tags":[]},"source":["# Check the amount of data in each folder in training dataset"]},{"cell_type":"code","execution_count":4,"id":"a7fff831","metadata":{"ExecuteTime":{"end_time":"2023-07-24T18:55:41.149003200Z","start_time":"2023-07-24T18:55:41.148001500Z"},"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:13:13.416783Z","iopub.status.busy":"2023-12-11T16:13:13.416484Z","iopub.status.idle":"2023-12-11T16:13:16.774024Z","shell.execute_reply":"2023-12-11T16:13:16.772883Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":3.366118,"end_time":"2023-12-11T16:13:16.776214","exception":false,"start_time":"2023-12-11T16:13:13.410096","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["surprise folder contains\t\t 3205 images\n","fear folder contains\t\t 4103 images\n","angry folder contains\t\t 3993 images\n","neutral folder contains\t\t 4982 images\n","sad folder contains\t\t 4938 images\n","disgust folder contains\t\t 436 images\n","happy folder contains\t\t 7164 images\n"]}],"source":["for expression in os.listdir('/kaggle/input/face-expression-recognition-dataset/images/train/'):\n"," print(expression, \"folder contains\\t\\t\",\n"," len(os.listdir('/kaggle/input/face-expression-recognition-dataset/images/train/' + expression)), \"images\")"]},{"cell_type":"markdown","id":"586a3a32","metadata":{"papermill":{"duration":0.005937,"end_time":"2023-12-11T16:13:16.788421","exception":false,"start_time":"2023-12-11T16:13:16.782484","status":"completed"},"tags":[]},"source":["# Check the amount of data in each folder in testing dataset"]},{"cell_type":"code","execution_count":5,"id":"eb128f5c","metadata":{"ExecuteTime":{"end_time":"2023-07-24T18:55:41.270710900Z","start_time":"2023-07-24T18:55:41.153001300Z"},"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:13:16.80105Z","iopub.status.busy":"2023-12-11T16:13:16.800718Z","iopub.status.idle":"2023-12-11T16:13:16.811547Z","shell.execute_reply":"2023-12-11T16:13:16.810724Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":0.019211,"end_time":"2023-12-11T16:13:16.813362","exception":false,"start_time":"2023-12-11T16:13:16.794151","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["surprise folder contains\t\t 797 images\n","fear folder contains\t\t 1018 images\n","angry folder contains\t\t 960 images\n","neutral folder contains\t\t 1216 images\n","sad folder contains\t\t 1139 images\n","disgust folder contains\t\t 111 images\n","happy folder contains\t\t 1825 images\n"]}],"source":["for expression in os.listdir('/kaggle/input/face-expression-recognition-dataset/images/validation/'):\n"," print(expression, \"folder contains\\t\\t\",\n"," len(os.listdir('/kaggle/input/face-expression-recognition-dataset/images/validation/' + expression)), \"images\")"]},{"cell_type":"code","execution_count":6,"id":"1c787496","metadata":{"ExecuteTime":{"end_time":"2023-07-24T18:55:41.523710900Z","start_time":"2023-07-24T18:55:41.270710900Z"},"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:13:16.825972Z","iopub.status.busy":"2023-12-11T16:13:16.825683Z","iopub.status.idle":"2023-12-11T16:13:21.215998Z","shell.execute_reply":"2023-12-11T16:13:21.214747Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":4.40643,"end_time":"2023-12-11T16:13:21.225526","exception":false,"start_time":"2023-12-11T16:13:16.819096","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Model: \"sequential\"\n","_________________________________________________________________\n"," Layer (type) Output Shape Param # \n","=================================================================\n"," conv2d (Conv2D) (None, 48, 48, 64) 640 \n"," \n"," max_pooling2d (MaxPooling2D (None, 24, 24, 64) 0 \n"," ) \n"," \n"," conv2d_1 (Conv2D) (None, 24, 24, 128) 73856 \n"," \n"," max_pooling2d_1 (MaxPooling (None, 12, 12, 128) 0 \n"," 2D) \n"," \n"," conv2d_2 (Conv2D) (None, 12, 12, 256) 295168 \n"," \n"," max_pooling2d_2 (MaxPooling (None, 6, 6, 256) 0 \n"," 2D) \n"," \n"," conv2d_3 (Conv2D) (None, 6, 6, 512) 1180160 \n"," \n"," max_pooling2d_3 (MaxPooling (None, 3, 3, 512) 0 \n"," 2D) \n"," \n"," flatten (Flatten) (None, 4608) 0 \n"," \n"," dense (Dense) (None, 512) 2359808 \n"," \n"," dropout (Dropout) (None, 512) 0 \n"," \n"," dense_1 (Dense) (None, 7) 3591 \n"," \n","=================================================================\n","Total params: 3,913,223\n","Trainable params: 3,913,223\n","Non-trainable params: 0\n","_________________________________________________________________\n"]}],"source":["model = tf.keras.models.Sequential(\n"," [\n"," Conv2D(64,(3,3),padding='same',activation='relu',input_shape=(48,48,1)),\n"," MaxPooling2D((2,2)),\n"," Conv2D(128,(3,3),padding='same',activation='relu'),\n"," MaxPooling2D((2,2)),\n"," Conv2D(256,(3,3),padding='same',activation='relu'),\n"," MaxPooling2D((2,2)),\n"," Conv2D(512,(3,3),padding='same',activation='relu'),\n"," MaxPooling2D((2,2)),\n"," Flatten(),\n"," Dense(512,activation='relu',kernel_regularizer=tf.keras.regularizers.l1()),\n"," Dropout(0.5),\n"," Dense(7,activation='softmax',kernel_regularizer=tf.keras.regularizers.l1())\n"," ]\n",")\n","\n","model.compile(\n"," optimizer=Adam(),\n"," loss='categorical_crossentropy',\n"," metrics=['accuracy']\n",")\n","\n","model.summary()"]},{"cell_type":"markdown","id":"59198e5d","metadata":{"papermill":{"duration":0.008397,"end_time":"2023-12-11T16:13:21.242612","exception":false,"start_time":"2023-12-11T16:13:21.234215","status":"completed"},"tags":[]},"source":["# Data Augmentation"]},{"cell_type":"code","execution_count":7,"id":"bf9f3ead","metadata":{"ExecuteTime":{"end_time":"2023-07-24T18:55:44.874241800Z","start_time":"2023-07-24T18:55:41.515718100Z"},"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:13:21.260657Z","iopub.status.busy":"2023-12-11T16:13:21.260366Z","iopub.status.idle":"2023-12-11T16:13:41.329532Z","shell.execute_reply":"2023-12-11T16:13:41.328611Z"},"jupyter":{"outputs_hidden":false},"papermill":{"duration":20.080636,"end_time":"2023-12-11T16:13:41.331617","exception":false,"start_time":"2023-12-11T16:13:21.250981","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Found 28821 images belonging to 7 classes.\n","Found 7066 images belonging to 7 classes.\n"]}],"source":["datagen_train = ImageDataGenerator(\n"," rescale=1. / 255,\n"," zoom_range=0.3,\n"," horizontal_flip=True\n",")\n","\n","train_generator = datagen_train.flow_from_directory(\n"," '/kaggle/input/face-expression-recognition-dataset/images/train/',\n"," batch_size=64,\n"," target_size=(48, 48),\n"," shuffle=True,\n"," color_mode='grayscale',\n"," class_mode='categorical'\n",")\n","\n","datagen_test = ImageDataGenerator(\n"," rescale=1. / 255,\n"," zoom_range=0.3,\n"," horizontal_flip=True\n",")\n","\n","test_generator = datagen_test.flow_from_directory(\n"," '/kaggle/input/face-expression-recognition-dataset/images/validation/',\n"," batch_size=64,\n"," target_size=(48, 48),\n"," shuffle=True,\n"," color_mode='grayscale',\n"," class_mode='categorical'\n",")\n","\n","steps_per_epoch = train_generator.n / train_generator.batch_size\n","testing_steps = test_generator.n / test_generator.batch_size"]},{"cell_type":"code","execution_count":8,"id":"086dfaba","metadata":{"ExecuteTime":{"start_time":"2023-07-24T18:55:44.876242900Z"},"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:13:41.350896Z","iopub.status.busy":"2023-12-11T16:13:41.350609Z","iopub.status.idle":"2023-12-11T16:56:17.251219Z","shell.execute_reply":"2023-12-11T16:56:17.250288Z"},"is_executing":true,"jupyter":{"outputs_hidden":false},"papermill":{"duration":2555.913011,"end_time":"2023-12-11T16:56:17.253561","exception":false,"start_time":"2023-12-11T16:13:41.34055","status":"completed"},"tags":[]},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/50\n","451/451 [==============================] - 208s 437ms/step - loss: 17.0263 - accuracy: 0.2421 - val_loss: 4.6978 - val_accuracy: 0.2583\n","Epoch 2/50\n","451/451 [==============================] - 49s 108ms/step - loss: 4.6728 - accuracy: 0.2487 - val_loss: 4.6183 - val_accuracy: 0.2583\n","Epoch 3/50\n","451/451 [==============================] - 49s 109ms/step - loss: 4.6495 - accuracy: 0.2465 - val_loss: 4.6297 - val_accuracy: 0.2583\n","Epoch 4/50\n","451/451 [==============================] - 49s 108ms/step - loss: 4.6349 - accuracy: 0.2490 - val_loss: 4.6094 - val_accuracy: 0.2584\n","Epoch 5/50\n","451/451 [==============================] - 48s 107ms/step - loss: 4.6140 - accuracy: 0.2487 - val_loss: 4.6121 - val_accuracy: 0.2596\n","Epoch 6/50\n","451/451 [==============================] - 49s 108ms/step - loss: 4.5860 - accuracy: 0.2496 - val_loss: 4.5821 - val_accuracy: 0.2579\n","Epoch 7/50\n","451/451 [==============================] - 49s 109ms/step - loss: 4.5615 - accuracy: 0.2511 - val_loss: 4.5473 - val_accuracy: 0.2883\n","Epoch 8/50\n","451/451 [==============================] - 49s 108ms/step - loss: 4.5160 - accuracy: 0.2694 - val_loss: 4.4794 - val_accuracy: 0.3121\n","Epoch 9/50\n","451/451 [==============================] - 48s 107ms/step - loss: 4.4840 - accuracy: 0.2911 - val_loss: 4.4166 - val_accuracy: 0.3146\n","Epoch 10/50\n","451/451 [==============================] - 48s 106ms/step - loss: 4.4566 - accuracy: 0.3077 - val_loss: 4.4191 - val_accuracy: 0.3506\n","Epoch 11/50\n","451/451 [==============================] - 48s 107ms/step - loss: 4.4278 - accuracy: 0.3170 - val_loss: 4.3782 - val_accuracy: 0.3627\n","Epoch 12/50\n","451/451 [==============================] - 49s 110ms/step - loss: 4.4099 - accuracy: 0.3280 - val_loss: 4.3953 - val_accuracy: 0.3646\n","Epoch 13/50\n","451/451 [==============================] - 49s 109ms/step - loss: 4.3914 - accuracy: 0.3468 - val_loss: 4.3468 - val_accuracy: 0.3866\n","Epoch 14/50\n","451/451 [==============================] - 49s 109ms/step - loss: 4.3647 - accuracy: 0.3626 - val_loss: 4.3255 - val_accuracy: 0.3978\n","Epoch 15/50\n","451/451 [==============================] - 46s 103ms/step - loss: 4.3452 - accuracy: 0.3700 - val_loss: 4.2761 - val_accuracy: 0.4031\n","Epoch 16/50\n","451/451 [==============================] - 47s 104ms/step - loss: 4.3289 - accuracy: 0.3782 - val_loss: 4.2404 - val_accuracy: 0.4202\n","Epoch 17/50\n","451/451 [==============================] - 47s 103ms/step - loss: 4.3071 - accuracy: 0.3909 - val_loss: 4.2441 - val_accuracy: 0.4128\n","Epoch 18/50\n","451/451 [==============================] - 47s 105ms/step - loss: 4.3010 - accuracy: 0.3962 - val_loss: 4.2537 - val_accuracy: 0.4168\n","Epoch 19/50\n","451/451 [==============================] - 46s 103ms/step - loss: 4.2852 - accuracy: 0.4036 - val_loss: 4.2249 - val_accuracy: 0.4209\n","Epoch 20/50\n","451/451 [==============================] - 45s 101ms/step - loss: 4.2765 - accuracy: 0.4101 - val_loss: 4.1969 - val_accuracy: 0.4346\n","Epoch 21/50\n","451/451 [==============================] - 46s 103ms/step - loss: 4.2631 - accuracy: 0.4098 - val_loss: 4.1970 - val_accuracy: 0.4342\n","Epoch 22/50\n","451/451 [==============================] - 47s 104ms/step - loss: 4.2585 - accuracy: 0.4163 - val_loss: 4.1780 - val_accuracy: 0.4471\n","Epoch 23/50\n","451/451 [==============================] - 47s 105ms/step - loss: 4.2505 - accuracy: 0.4163 - val_loss: 4.1832 - val_accuracy: 0.4488\n","Epoch 24/50\n","451/451 [==============================] - 47s 104ms/step - loss: 4.2411 - accuracy: 0.4268 - val_loss: 4.1919 - val_accuracy: 0.4524\n","Epoch 25/50\n","451/451 [==============================] - 47s 103ms/step - loss: 4.2219 - accuracy: 0.4356 - val_loss: 4.1687 - val_accuracy: 0.4597\n","Epoch 26/50\n","451/451 [==============================] - 48s 106ms/step - loss: 4.2165 - accuracy: 0.4414 - val_loss: 4.1850 - val_accuracy: 0.4526\n","Epoch 27/50\n","451/451 [==============================] - 49s 108ms/step - loss: 4.2082 - accuracy: 0.4396 - val_loss: 4.1218 - val_accuracy: 0.4616\n","Epoch 28/50\n","451/451 [==============================] - 48s 106ms/step - loss: 4.2093 - accuracy: 0.4475 - val_loss: 4.1406 - val_accuracy: 0.4604\n","Epoch 29/50\n","451/451 [==============================] - 45s 101ms/step - loss: 4.1980 - accuracy: 0.4535 - val_loss: 4.1879 - val_accuracy: 0.4452\n","Epoch 30/50\n","451/451 [==============================] - 45s 100ms/step - loss: 4.1910 - accuracy: 0.4563 - val_loss: 4.1788 - val_accuracy: 0.4687\n","Epoch 31/50\n","451/451 [==============================] - 53s 118ms/step - loss: 4.1904 - accuracy: 0.4567 - val_loss: 4.1402 - val_accuracy: 0.4871\n","Epoch 32/50\n","451/451 [==============================] - 54s 119ms/step - loss: 4.1822 - accuracy: 0.4585 - val_loss: 4.1378 - val_accuracy: 0.4724\n","Epoch 33/50\n","451/451 [==============================] - 46s 101ms/step - loss: 4.1736 - accuracy: 0.4657 - val_loss: 4.1259 - val_accuracy: 0.4740\n","Epoch 34/50\n","451/451 [==============================] - 45s 100ms/step - loss: 4.1669 - accuracy: 0.4708 - val_loss: 4.1487 - val_accuracy: 0.4822\n","Epoch 35/50\n","451/451 [==============================] - 46s 102ms/step - loss: 4.1621 - accuracy: 0.4688 - val_loss: 4.1387 - val_accuracy: 0.4867\n","Epoch 36/50\n","451/451 [==============================] - 46s 101ms/step - loss: 4.1548 - accuracy: 0.4727 - val_loss: 4.1413 - val_accuracy: 0.4741\n","Epoch 37/50\n","451/451 [==============================] - 46s 102ms/step - loss: 4.1504 - accuracy: 0.4764 - val_loss: 4.1358 - val_accuracy: 0.4819\n","Epoch 38/50\n","451/451 [==============================] - 48s 106ms/step - loss: 4.1490 - accuracy: 0.4780 - val_loss: 4.1150 - val_accuracy: 0.4921\n","Epoch 39/50\n","451/451 [==============================] - 45s 101ms/step - loss: 4.1387 - accuracy: 0.4801 - val_loss: 4.1046 - val_accuracy: 0.4892\n","Epoch 40/50\n","451/451 [==============================] - 46s 101ms/step - loss: 4.1368 - accuracy: 0.4824 - val_loss: 4.0841 - val_accuracy: 0.5003\n","Epoch 41/50\n","451/451 [==============================] - 47s 103ms/step - loss: 4.1308 - accuracy: 0.4847 - val_loss: 4.1028 - val_accuracy: 0.5004\n","Epoch 42/50\n","451/451 [==============================] - 46s 102ms/step - loss: 4.1287 - accuracy: 0.4853 - val_loss: 4.1248 - val_accuracy: 0.5075\n","Epoch 43/50\n","451/451 [==============================] - 46s 101ms/step - loss: 4.1252 - accuracy: 0.4858 - val_loss: 4.0988 - val_accuracy: 0.5120\n","Epoch 44/50\n","451/451 [==============================] - 45s 99ms/step - loss: 4.1172 - accuracy: 0.4918 - val_loss: 4.0828 - val_accuracy: 0.5030\n","Epoch 45/50\n","451/451 [==============================] - 46s 101ms/step - loss: 4.1147 - accuracy: 0.4937 - val_loss: 4.1159 - val_accuracy: 0.5031\n","Epoch 46/50\n","451/451 [==============================] - 45s 99ms/step - loss: 4.1102 - accuracy: 0.4947 - val_loss: 4.0628 - val_accuracy: 0.5081\n","Epoch 47/50\n","451/451 [==============================] - 45s 100ms/step - loss: 4.1055 - accuracy: 0.4990 - val_loss: 4.0786 - val_accuracy: 0.5069\n","Epoch 48/50\n","451/451 [==============================] - 45s 101ms/step - loss: 4.1109 - accuracy: 0.5022 - val_loss: 4.0956 - val_accuracy: 0.5020\n","Epoch 49/50\n","451/451 [==============================] - 46s 101ms/step - loss: 4.0983 - accuracy: 0.5052 - val_loss: 4.0754 - val_accuracy: 0.5096\n","Epoch 50/50\n","451/451 [==============================] - 48s 106ms/step - loss: 4.0990 - accuracy: 0.5023 - val_loss: 4.0942 - val_accuracy: 0.5147\n"]}],"source":["history = model.fit(\n"," train_generator,\n"," validation_data=test_generator,\n"," batch_size=64,\n"," epochs=50,\n"," shuffle=True\n",")"]},{"cell_type":"markdown","id":"631bfde5","metadata":{"papermill":{"duration":1.797476,"end_time":"2023-12-11T16:56:20.872353","exception":false,"start_time":"2023-12-11T16:56:19.074877","status":"completed"},"tags":[]},"source":["# Plot the training and validation accuracy and loss at each epoch"]},{"cell_type":"code","execution_count":9,"id":"33051f48","metadata":{"collapsed":false,"execution":{"iopub.execute_input":"2023-12-11T16:56:24.544151Z","iopub.status.busy":"2023-12-11T16:56:24.54346Z","iopub.status.idle":"2023-12-11T16:56:24.894308Z","shell.execute_reply":"2023-12-11T16:56:24.893208Z"},"is_executing":true,"jupyter":{"outputs_hidden":false},"papermill":{"duration":2.224304,"end_time":"2023-12-11T16:56:24.896609","exception":false,"start_time":"2023-12-11T16:56:22.672305","status":"completed"},"tags":[]},"outputs":[{"data":{"image/png":"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","text/plain":[""]},"metadata":{},"output_type":"display_data"}],"source":["loss = history.history['loss']\n","val_loss = history.history['val_loss']\n","epochs = range(1, len(loss) + 1)\n","plt.plot(epochs, loss, label='Training loss')\n","plt.plot(epochs, val_loss, label='Validation loss')\n","acc = history.history['accuracy']\n","val_acc = history.history['val_accuracy']\n","plt.plot(epochs, acc, label='Training acc')\n","plt.plot(epochs, val_acc, label='Validation acc')\n","plt.title('Training and Validation Accuracy')\n","plt.xlabel('Epochs')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.show()"]}],"metadata":{"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"datasetId":99505,"sourceId":234911,"sourceType":"datasetVersion"}],"dockerImageVersionId":30529,"isGpuEnabled":true,"isInternetEnabled":true,"language":"python","sourceType":"notebook"},"kernelspec":{"display_name":"Python 3","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":"3.10.12"},"papermill":{"default_parameters":{},"duration":2619.006763,"end_time":"2023-12-11T16:56:30.456253","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2023-12-11T16:12:51.44949","version":"2.4.0"}},"nbformat":4,"nbformat_minor":5}
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