Sign language translator model work on PC

requirements.txt

+numpy
+tensorflow
+matplotlib
+keras
+pandas
\ No newline at end of file

src/PC/sign_language_detector.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SIGN LANGUAGE TRANSLATOR"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import tensorflow as tf\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import random\n",
+    "\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get data and label from train and test dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_path = \"dataset/sign_mnist_train.csv\"\n",
+    "test_path = \"dataset/sign_mnist_test.csv\"\n",
+    "\n",
+    "train = pd.read_csv(train_path)\n",
+    "test = pd.read_csv(test_path)\n",
+    "\n",
+    "train_data = np.array(train)[:, 1:]\n",
+    "train_label = np.array(train)[:, 0]\n",
+    "\n",
+    "test_data = np.array(test)[:, 1:]\n",
+    "test_label = np.array(test)[:, 0]\n",
+    "\n",
+    "train_data = np.reshape(train_data, (-1, 28, 28, 1))\n",
+    "test_data = np.reshape(test_data, (-1, 28, 28, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Show random image from train dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "alphabet = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n",
+    "\n",
+    "rand_i = random.randint(1, train_data.shape[0])\n",
+    "img = train_data[rand_i]\n",
+    "print(f\"Letter : {alphabet[train_label[rand_i]]}\")\n",
+    "plt.imshow(img, cmap='gray')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CNN\n",
+    "Entrainement et evaluation du modèle."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Conv2D, Dense, MaxPool2D, Flatten, Rescaling, Dropout\n",
+    "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
+    "\n",
+    "\n",
+    "CLASS_COUNT = int(np.max(train_label) + 1)\n",
+    "INPUT_SHAPE = train_data.shape[1]\n",
+    "BATCH_SIZE = 32#train_data.shape[0] // 4\n",
+    "EPOCHS = 15\n",
+    "\n",
+    "train_label = tf.keras.utils.to_categorical(train_label, CLASS_COUNT)\n",
+    "test_label = tf.keras.utils.to_categorical(test_label, CLASS_COUNT)\n",
+    "\n",
+    "model = Sequential()\n",
+    "model.add(Rescaling(1./255, input_shape=(INPUT_SHAPE, INPUT_SHAPE, 1)))\n",
+    "model.add(Conv2D(filters=32, kernel_size=3, padding=\"same\", activation='relu'))\n",
+    "model.add(MaxPool2D())\n",
+    "model.add(Conv2D(filters=64, kernel_size=3, padding=\"same\", activation='relu'))\n",
+    "model.add(MaxPool2D())\n",
+    "model.add(Flatten())\n",
+    "model.add(Dropout(0.5))\n",
+    "model.add(Dense(CLASS_COUNT, activation=\"softmax\"))\n",
+    "model.summary()\n",
+    "\n",
+    "model.compile(optimizer='adam', loss=\"categorical_crossentropy\", metrics=['accuracy'])\n",
+    "history = model.fit(train_data, train_label, batch_size=BATCH_SIZE, epochs=EPOCHS)\n",
+    "\n",
+    "plt.plot(history.history[\"loss\"])\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "plt.ylabel(\"Loss value\")\n",
+    "plt.title(\"Evolution of Loss\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(history.history[\"accuracy\"])\n",
+    "plt.xlabel(\"Epochs\")\n",
+    "plt.ylabel(\"Accuracy\")\n",
+    "plt.title(\"Evolution of Accuracy\")\n",
+    "plt.show()\n",
+    "\n",
+    "evaluate = model.evaluate(test_data, test_label, verbose=2) # ~58%\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Export as Keras model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.save('sign_model.keras')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.11.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}