[Part 3]Train_license_character_recognition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GxW3Qg-ScurV"
   },
   "source": [
    "#### This notebook is used to train a character recongition from input image using MobileNets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "XJmrQrB_curX",
    "outputId": "5a4bee5b-8c58-41bb-cd70-ff9ffa406ff9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "# ignore warning \n",
    "import os\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications import MobileNetV2\n",
    "from keras.layers import AveragePooling2D\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Input\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.preprocessing.image import img_to_array\n",
    "from keras.preprocessing.image import load_img\n",
    "from keras.utils import to_categorical\n",
    "from keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from keras.models import model_from_json\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "import glob\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z8ApWHt8curj"
   },
   "source": [
    "### Visualize dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 479
    },
    "colab_type": "code",
    "id": "-y1mnjyPcurk",
    "outputId": "de256a8f-5bb5-47c7-91c5-b29832539766",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset_paths = glob.glob(\"dataset_characters/**/*.jpg\")\n",
    "\n",
    "cols=4\n",
    "rows=3\n",
    "fig = plt.figure(figsize=(10,8))\n",
    "plt.rcParams.update({\"font.size\":14})\n",
    "grid = gridspec.GridSpec(ncols=cols,nrows=rows,figure=fig)\n",
    "\n",
    "# create a random list of images will be displayed\n",
    "np.random.seed(45)\n",
    "rand = np.random.randint(0,len(dataset_paths),size=(cols*rows))\n",
    "\n",
    "# Plot image\n",
    "for i in range(cols*rows):\n",
    "    fig.add_subplot(grid[i])\n",
    "    image = load_img(dataset_paths[rand[i]])\n",
    "    label = dataset_paths[rand[i]].split(os.path.sep)[-2]\n",
    "    plt.title('\"{:s}\"'.format(label))\n",
    "    plt.axis(False)\n",
    "    plt.imshow(image)\n",
    "\n",
    "plt.savefig(\"Visualize_dataset.jpg\",dpi=300)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LJzYBWt3bzAj"
   },
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "colab_type": "code",
    "id": "Yh443FGFRgFP",
    "outputId": "2fdc22f4-e393-4ae1-bb86-d5fb55ed1865"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Find 34575 images with 36 classes\n"
     ]
    }
   ],
   "source": [
    "# Arange input data and corresponding labels\n",
    "X=[]\n",
    "labels=[]\n",
    "\n",
    "for image_path in dataset_paths:\n",
    "  label = image_path.split(os.path.sep)[-2]\n",
    "  image=load_img(image_path,target_size=(80,80))\n",
    "  image=img_to_array(image)\n",
    "\n",
    "  X.append(image)\n",
    "  labels.append(label)\n",
    "\n",
    "X = np.array(X,dtype=\"float16\")\n",
    "labels = np.array(labels)\n",
    "\n",
    "print(\"[INFO] Find {:d} images with {:d} classes\".format(len(X),len(set(labels))))\n",
    "\n",
    "\n",
    "# perform one-hot encoding on the labels\n",
    "lb = LabelEncoder()\n",
    "lb.fit(labels)\n",
    "labels = lb.transform(labels)\n",
    "y = to_categorical(labels)\n",
    "\n",
    "# save label file so we can use in another script\n",
    "np.save('license_character_classes.npy', lb.classes_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AOHM-oeWSsPd"
   },
   "outputs": [],
   "source": [
    "# split 10% of data as validation set\n",
    "(trainX, testX, trainY, testY) = train_test_split(X, y, test_size=0.10, stratify=y, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1DfUhlOTcurq"
   },
   "outputs": [],
   "source": [
    "# data augumentation\n",
    "image_gen = ImageDataGenerator(rotation_range=10,\n",
    "                              width_shift_range=0.1,\n",
    "                              height_shift_range=0.1,\n",
    "                              shear_range=0.1,\n",
    "                              zoom_range=0.1,\n",
    "                              fill_mode=\"nearest\"\n",
    "                              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vNMFc03Mb45i"
   },
   "source": [
    "## Initialize MobileNets architecture with pre-trained weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AaemgepFcur0"
   },
   "outputs": [],
   "source": [
    "# Create our model with pre-trained MobileNetV2 architecture from imagenet\n",
    "def create_model(lr=1e-4,decay=1e-4/25, training=False,output_shape=y.shape[1]):\n",
    "    baseModel = MobileNetV2(weights=\"imagenet\", \n",
    "                            include_top=False,\n",
    "                            input_tensor=Input(shape=(80, 80, 3)))\n",
    "\n",
    "    headModel = baseModel.output\n",
    "    headModel = AveragePooling2D(pool_size=(3, 3))(headModel)\n",
    "    headModel = Flatten(name=\"flatten\")(headModel)\n",
    "    headModel = Dense(128, activation=\"relu\")(headModel)\n",
    "    headModel = Dropout(0.5)(headModel)\n",
    "    headModel = Dense(output_shape, activation=\"softmax\")(headModel)\n",
    "    \n",
    "    model = Model(inputs=baseModel.input, outputs=headModel)\n",
    "    \n",
    "    if training:\n",
    "        # define trainable lalyer\n",
    "        for layer in baseModel.layers:\n",
    "            layer.trainable = True\n",
    "        # compile model\n",
    "        optimizer = Adam(lr=lr, decay = decay)\n",
    "        model.compile(loss=\"categorical_crossentropy\", optimizer=optimizer,metrics=[\"accuracy\"])    \n",
    "        \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 108
    },
    "colab_type": "code",
    "id": "-B5O6zG_cur5",
    "outputId": "83bfaffb-85bd-4219-da8e-ed68d7c656ad"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_applications/mobilenet_v2.py:294: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [96, 128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "  warnings.warn('`input_shape` is undefined or non-square, '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/JonathanCMitchell/mobilenet_v2_keras/releases/download/v1.1/mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_224_no_top.h5\n",
      "9412608/9406464 [==============================] - 0s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# initilaize initial hyperparameter\n",
    "INIT_LR = 1e-4\n",
    "EPOCHS = 30\n",
    "\n",
    "model = create_model(lr=INIT_LR, decay=INIT_LR/EPOCHS,training=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JR0L3nWgb-gK"
   },
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "4d4NQLwDcusA",
    "outputId": "29c5fad5-3f23-427c-f04c-01af2a140e40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "486/486 [==============================] - 147s 303ms/step - loss: 1.5441 - accuracy: 0.5989 - val_loss: 1.5866 - val_accuracy: 0.6090\n",
      "\n",
      "Epoch 00001: saving model to License_character_recognition.h5\n",
      "Epoch 2/30\n",
      "486/486 [==============================] - 124s 256ms/step - loss: 0.4201 - accuracy: 0.8930 - val_loss: 0.4698 - val_accuracy: 0.8788\n",
      "\n",
      "Epoch 00002: saving model to License_character_recognition.h5\n",
      "Epoch 3/30\n",
      "486/486 [==============================] - 125s 258ms/step - loss: 0.2769 - accuracy: 0.9281 - val_loss: 0.2617 - val_accuracy: 0.9352\n",
      "\n",
      "Epoch 00003: saving model to License_character_recognition.h5\n",
      "Epoch 4/30\n",
      "486/486 [==============================] - 125s 257ms/step - loss: 0.2103 - accuracy: 0.9454 - val_loss: 0.1744 - val_accuracy: 0.9563\n",
      "\n",
      "Epoch 00004: saving model to License_character_recognition.h5\n",
      "Epoch 5/30\n",
      "486/486 [==============================] - 126s 259ms/step - loss: 0.1642 - accuracy: 0.9554 - val_loss: 0.1491 - val_accuracy: 0.9592\n",
      "\n",
      "Epoch 00005: saving model to License_character_recognition.h5\n",
      "Epoch 6/30\n",
      "486/486 [==============================] - 127s 261ms/step - loss: 0.1429 - accuracy: 0.9610 - val_loss: 0.1245 - val_accuracy: 0.9656\n",
      "\n",
      "Epoch 00006: saving model to License_character_recognition.h5\n",
      "Epoch 7/30\n",
      "486/486 [==============================] - 125s 257ms/step - loss: 0.1311 - accuracy: 0.9654 - val_loss: 0.1258 - val_accuracy: 0.9641\n",
      "\n",
      "Epoch 00007: saving model to License_character_recognition.h5\n",
      "Epoch 8/30\n",
      "486/486 [==============================] - 125s 256ms/step - loss: 0.1125 - accuracy: 0.9693 - val_loss: 0.1202 - val_accuracy: 0.9688\n",
      "\n",
      "Epoch 00008: saving model to License_character_recognition.h5\n",
      "Epoch 9/30\n",
      "486/486 [==============================] - 124s 256ms/step - loss: 0.1024 - accuracy: 0.9730 - val_loss: 0.1038 - val_accuracy: 0.9714\n",
      "\n",
      "Epoch 00009: saving model to License_character_recognition.h5\n",
      "Epoch 10/30\n",
      "486/486 [==============================] - 126s 260ms/step - loss: 0.0924 - accuracy: 0.9751 - val_loss: 0.0972 - val_accuracy: 0.9746\n",
      "\n",
      "Epoch 00010: saving model to License_character_recognition.h5\n",
      "Epoch 11/30\n",
      "486/486 [==============================] - 126s 259ms/step - loss: 0.0825 - accuracy: 0.9772 - val_loss: 0.0869 - val_accuracy: 0.9751\n",
      "\n",
      "Epoch 00011: saving model to License_character_recognition.h5\n",
      "Epoch 12/30\n",
      "486/486 [==============================] - 125s 257ms/step - loss: 0.0821 - accuracy: 0.9782 - val_loss: 0.0872 - val_accuracy: 0.9786\n",
      "\n",
      "Epoch 00012: saving model to License_character_recognition.h5\n",
      "Epoch 13/30\n",
      "486/486 [==============================] - 125s 257ms/step - loss: 0.0736 - accuracy: 0.9803 - val_loss: 0.0601 - val_accuracy: 0.9844\n",
      "\n",
      "Epoch 00013: saving model to License_character_recognition.h5\n",
      "Epoch 14/30\n",
      "486/486 [==============================] - 125s 256ms/step - loss: 0.0761 - accuracy: 0.9800 - val_loss: 0.0747 - val_accuracy: 0.9800\n",
      "\n",
      "Epoch 00014: saving model to License_character_recognition.h5\n",
      "Epoch 15/30\n",
      "486/486 [==============================] - 126s 260ms/step - loss: 0.0631 - accuracy: 0.9829 - val_loss: 0.0598 - val_accuracy: 0.9853\n",
      "\n",
      "Epoch 00015: saving model to License_character_recognition.h5\n",
      "Epoch 16/30\n",
      "486/486 [==============================] - 126s 260ms/step - loss: 0.0563 - accuracy: 0.9847 - val_loss: 0.0873 - val_accuracy: 0.9803\n",
      "\n",
      "Epoch 00016: saving model to License_character_recognition.h5\n",
      "Epoch 17/30\n",
      "486/486 [==============================] - 125s 258ms/step - loss: 0.0565 - accuracy: 0.9848 - val_loss: 0.0726 - val_accuracy: 0.9832\n",
      "\n",
      "Epoch 00017: saving model to License_character_recognition.h5\n",
      "Epoch 18/30\n",
      "486/486 [==============================] - 126s 259ms/step - loss: 0.0534 - accuracy: 0.9851 - val_loss: 0.0762 - val_accuracy: 0.9803\n",
      "\n",
      "Epoch 00018: saving model to License_character_recognition.h5\n",
      "Epoch 19/30\n",
      "486/486 [==============================] - 126s 259ms/step - loss: 0.0516 - accuracy: 0.9861 - val_loss: 0.0979 - val_accuracy: 0.9792\n",
      "\n",
      "Epoch 00019: saving model to License_character_recognition.h5\n",
      "Epoch 20/30\n",
      "486/486 [==============================] - 127s 261ms/step - loss: 0.0462 - accuracy: 0.9873 - val_loss: 0.0749 - val_accuracy: 0.9847\n",
      "\n",
      "Epoch 00020: saving model to License_character_recognition.h5\n"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 64\n",
    "\n",
    "my_checkpointer = [\n",
    "                EarlyStopping(monitor='val_loss', patience=5, verbose=0),\n",
    "                ModelCheckpoint(filepath=\"License_character_recognition.h5\", verbose=1, save_weights_only=True)\n",
    "                ]\n",
    "\n",
    "result = model.fit(image_gen.flow(trainX, trainY, batch_size=BATCH_SIZE), \n",
    "                   steps_per_epoch=len(trainX) // BATCH_SIZE, \n",
    "                   validation_data=(testX, testY), \n",
    "                   validation_steps=len(testX) // BATCH_SIZE, \n",
    "                   epochs=EPOCHS, callbacks=my_checkpointer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "va-WvqTpcCJw"
   },
   "source": [
    "## Visualize training result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 362
    },
    "colab_type": "code",
    "id": "qUYOQrVzcusF",
    "outputId": "66bf8f56-e839-40bb-f1b7-3d7307c7fe26"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize=(14,5))\n",
    "grid=gridspec.GridSpec(ncols=2,nrows=1,figure=fig)\n",
    "fig.add_subplot(grid[0])\n",
    "plt.plot(result.history['accuracy'], label='training accuracy')\n",
    "plt.plot(result.history['val_accuracy'], label='val accuracy')\n",
    "plt.title('Accuracy')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('accuracy')\n",
    "plt.legend()\n",
    "\n",
    "fig.add_subplot(grid[1])\n",
    "plt.plot(result.history['loss'], label='training loss')\n",
    "plt.plot(result.history['val_loss'], label='val loss')\n",
    "plt.title('Loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "\n",
    "#plt.savefig(\"Training_result.jpg\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w8JOq60Xw1UY"
   },
   "outputs": [],
   "source": [
    "# save model architectur as json file\n",
    "model_json = model.to_json()\n",
    "with open(\"MobileNets_character_recognition.json\", \"w\") as json_file:\n",
    "  json_file.write(model_json)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "t92Je4kBbs0g"
   },
   "source": [
    "## The End!"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Train_license_character_recognition.ipynb",
   "provenance": [],
   "toc_visible": true
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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