neural_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential, load_model\n",
    "from tensorflow.keras.layers import Dense, Activation, Flatten, Conv2D, MaxPooling2D, Dropout\n",
    "import cv2\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "CATEGORIES = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n",
    "DATADIR = \"Dataset\"\n",
    "training_data = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for category in CATEGORIES:\n",
    "    path = os.path.join(DATADIR, category)\n",
    "    class_num = CATEGORIES.index(category)\n",
    "    for img in os.listdir(path):\n",
    "        img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)\n",
    "        new_array = cv2.resize(img_array, (28, 28))\n",
    "        training_data.append([new_array, class_num])\n",
    "\n",
    "random.shuffle(training_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "\n",
    "for features, label in training_data:\n",
    "    X.append(features)\n",
    "    y.append(label)\n",
    "\n",
    "X = np.array(X).reshape(-1, 28, 28, 1)\n",
    "y = np.array(y).astype(float)\n",
    "\n",
    "X = X/255.0  # Normailizing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(64, (3, 3), input_shape=X.shape[1:]))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Conv2D(256, (3, 3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(Dropout(0.2))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128))\n",
    "model.add(Activation(\"relu\"))\n",
    "\n",
    "model.add(Dense(26))  # Equal to the no. of categories\n",
    "model.add(Activation('softmax'))\n",
    "\n",
    "model.compile(\n",
    "        optimizer=tf.optimizers.Adam(),\n",
    "        loss='sparse_categorical_crossentropy',\n",
    "        metrics=['accuracy']\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(X, y, batch_size=224, epochs=5) #, callbacks = [tensorboard])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model = load_model('weights.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = []\n",
    "for i in range(1, 27):\n",
    "    image = cv2.imread('data/'+str(i)+'.png', 0)\n",
    "    image = cv2.resize(image, (28,28))\n",
    "    image = image.reshape(-1, 28, 28, 1).astype(float)\n",
    "    prediction = np.argmax(model.predict(image), axis=1)\n",
    "    result.append([CATEGORIES[i-1], CATEGORIES[prediction[0]]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "wrong = 0\n",
    "right = 0\n",
    "for i, j in result:\n",
    "    if i == j:\n",
    "        right += 1\n",
    "wrong = len(result) - right\n",
    "print(f'Correct Prediction: {right}')\n",
    "print(f'Wrong Prediction: {wrong}')\n",
    "print(f'Accuracy: {round(right / len(result), 3) * 100}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tf.keras.models.save_model(\n",
    "#     model,\n",
    "#     './weights.h5',\n",
    "#     overwrite=True,\n",
    "#     include_optimizer=True\n",
    "# )"
   ]
  }
 ],
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