Car_Logo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow.compat.v1 as tf\n",
    "tf.compat.v1.disable_v2_behavior\n",
    "import os\n",
    "import PIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0\n"
     ]
    }
   ],
   "source": [
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras import datasets, layers, models\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  D:\\DATA_BASE\\cars_logo\\train\n",
      "Test:  D:\\DATA_BASE\\cars_logo\\test\n"
     ]
    }
   ],
   "source": [
    "PATH = \"D:\\\\DATA_BASE\\\\cars_logo\"\n",
    "train_dir = os.path.join(PATH, 'train')\n",
    "validation_dir = os.path.join(PATH, 'test')\n",
    "print(\"Train: \", train_dir) \n",
    "print(\"Test: \", validation_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 10\n",
    "epochs = 15\n",
    "IMG_HEIGHT = 70\n",
    "IMG_WIDTH = 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = []\n",
    "for i in range(10):\n",
    "    cols.append(os.listdir(train_dir)[i])\n",
    "    print(os.listdir(train_dir)[i], \"------->\", os.listdir(validation_dir)[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_image_generator = ImageDataGenerator(rescale=1./255,\n",
    "                                           channel_shift_range=10.0,\n",
    "                                           width_shift_range= 0.1,\n",
    "                                           fill_mode=\"nearest\", \n",
    "                                           brightness_range=(0.3, 0.9),\n",
    "                                           horizontal_flip= False,\n",
    "                                           validation_split = 0.2) # Generator for our training data\n",
    "\n",
    "validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10000 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                           directory=train_dir,\n",
    "                                                           shuffle=True,\n",
    "                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                           class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1500 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,\n",
    "                                                              directory=validation_dir,\n",
    "                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),\n",
    "                                                              class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_training_images, _ = next(train_data_gen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "num = 5\n",
    "def plotImages(images_arr):\n",
    "    fig, axes = plt.subplots(1, num, figsize=(20,20))\n",
    "    axes = axes.flatten()\n",
    "    for img, ax in zip( images_arr, axes):\n",
    "        ax.imshow(img)\n",
    "        ax.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "plotImages(sample_training_images[:num])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_20 (Conv2D)           (None, 60, 60, 96)        34944     \n",
      "_________________________________________________________________\n",
      "max_pooling2d_15 (MaxPooling (None, 29, 29, 96)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_21 (Conv2D)           (None, 25, 25, 256)       614656    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_16 (MaxPooling (None, 12, 12, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_22 (Conv2D)           (None, 10, 10, 384)       885120    \n",
      "_________________________________________________________________\n",
      "conv2d_23 (Conv2D)           (None, 8, 8, 384)         1327488   \n",
      "_________________________________________________________________\n",
      "conv2d_24 (Conv2D)           (None, 6, 6, 256)         884992    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_17 (MaxPooling (None, 2, 2, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_13 (Dense)             (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_14 (Dense)             (None, 1024)              1049600   \n",
      "_________________________________________________________________\n",
      "dense_15 (Dense)             (None, 10)                10250     \n",
      "=================================================================\n",
      "Total params: 5,856,650\n",
      "Trainable params: 5,856,650\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = models.Sequential()\n",
    "\n",
    "model.add(layers.Conv2D(96, (11, 11),strides = 1, activation='relu',use_bias=True, input_shape=(IMG_HEIGHT, IMG_WIDTH, 3)))\n",
    "model.add(layers.MaxPooling2D((3, 3), strides = 2))\n",
    "\n",
    "model.add(layers.Conv2D(256, (5, 5), activation='relu',use_bias=True))\n",
    "model.add(layers.MaxPooling2D((3, 3), strides = 2))\n",
    "\n",
    "model.add(layers.Conv2D(384, (3, 3), activation='relu',use_bias=True))\n",
    "model.add(layers.Conv2D(384, (3, 3), activation='relu',use_bias=True))\n",
    "model.add(layers.Conv2D(256, (3, 3), activation='relu',use_bias=True))\n",
    "model.add(layers.MaxPooling2D((3, 3), strides = 2))\n",
    "\n",
    "# ASL_model.add(layers.Conv2D(128, (3, 3), activation='relu',use_bias=True))\n",
    "model.add(layers.Flatten())\n",
    "\n",
    "model.add(layers.Dense(1024, activation='relu',use_bias=True))\n",
    "model.add(layers.Dense(1024, activation='relu',use_bias=True))\n",
    "# model.add(layers.Dropout(0.2))\n",
    "\n",
    "model.add(layers.Dense(10, activation='softmax'))\n",
    "\n",
    "\n",
    "# ASL_model.compile(optimizer='adam',\n",
    "#               loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
    "#               metrics=['accuracy'])\n",
    "model.compile(loss = 'categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/15\n",
      "1000/1000 [==============================] - 45s 45ms/step - loss: 2.5710 - accuracy: 0.1150 - val_loss: 2.1014 - val_accuracy: 0.2247\n",
      "Epoch 2/15\n",
      "1000/1000 [==============================] - 41s 41ms/step - loss: 1.0916 - accuracy: 0.6771 - val_loss: 0.2718 - val_accuracy: 0.9513\n",
      "Epoch 3/15\n",
      "1000/1000 [==============================] - 45s 45ms/step - loss: 0.8891 - accuracy: 0.8517 - val_loss: 4.8461 - val_accuracy: 0.7933\n",
      "Epoch 4/15\n",
      "1000/1000 [==============================] - 41s 41ms/step - loss: 0.9591 - accuracy: 0.8833 - val_loss: 0.2014 - val_accuracy: 0.9320\n",
      "Epoch 5/15\n",
      "1000/1000 [==============================] - 30s 30ms/step - loss: 1.1553 - accuracy: 0.9114 - val_loss: 1.4187 - val_accuracy: 0.9053\n",
      "Epoch 6/15\n",
      "1000/1000 [==============================] - 25s 25ms/step - loss: 0.8656 - accuracy: 0.9217 - val_loss: 0.2065 - val_accuracy: 0.9640\n",
      "Epoch 7/15\n",
      "1000/1000 [==============================] - 26s 26ms/step - loss: 0.9783 - accuracy: 0.9352 - val_loss: 1.4379 - val_accuracy: 0.8887\n",
      "Epoch 8/15\n",
      "1000/1000 [==============================] - 26s 26ms/step - loss: 0.9612 - accuracy: 0.9307 - val_loss: 0.0226 - val_accuracy: 0.9967\n",
      "Epoch 9/15\n",
      "1000/1000 [==============================] - 26s 26ms/step - loss: 1.0185 - accuracy: 0.9417 - val_loss: 0.1142 - val_accuracy: 0.9807\n",
      "Epoch 10/15\n",
      "1000/1000 [==============================] - 27s 27ms/step - loss: 1.0279 - accuracy: 0.9428 - val_loss: 0.1087 - val_accuracy: 0.9893\n",
      "Epoch 11/15\n",
      "1000/1000 [==============================] - 27s 27ms/step - loss: 0.9293 - accuracy: 0.9162 - val_loss: 2.9112 - val_accuracy: 0.9827\n",
      "Epoch 12/15\n",
      "1000/1000 [==============================] - 26s 26ms/step - loss: 0.8084 - accuracy: 0.9563 - val_loss: 0.2234 - val_accuracy: 0.9887\n",
      "Epoch 13/15\n",
      "1000/1000 [==============================] - 27s 27ms/step - loss: 1.1026 - accuracy: 0.8917 - val_loss: 2.3137 - val_accuracy: 0.1087\n",
      "Epoch 14/15\n",
      "1000/1000 [==============================] - 26s 26ms/step - loss: 1.0557 - accuracy: 0.8983 - val_loss: 3.3500 - val_accuracy: 0.9393\n",
      "Epoch 15/15\n",
      "1000/1000 [==============================] - 27s 27ms/step - loss: 1.1995 - accuracy: 0.9181 - val_loss: 0.3730 - val_accuracy: 0.9800\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neuralNet = model.fit(\n",
    "    train_data_gen,\n",
    "    steps_per_epoch= (10000 // batch_size),\n",
    "    epochs=epochs,\n",
    "    validation_data=val_data_gen,\n",
    "    validation_steps= (1500 // batch_size)\n",
    ")\n",
    "acc = neuralNet.history['accuracy']\n",
    "val_acc = neuralNet.history['val_accuracy']\n",
    "\n",
    "loss = neuralNet.history['loss']\n",
    "val_loss = neuralNet.history['val_loss']\n",
    "\n",
    "epochs_range = range(epochs)\n",
    "\n",
    "plt.figure(figsize=(8, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
    "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
    "plt.legend(loc='lower right')\n",
    "plt.title('Training and Validation Accuracy')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(epochs_range, loss, label='Training Loss')\n",
    "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Training and Validation Loss')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('D:\\\\Machine Learning\\\\Working_models')\n",
    "model.save(\"Car_logo_new_rmsprop_CNN.h5\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6 5 8]\n",
      "Mazda\n",
      "Lexus\n",
      "Toyota\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "from keras.preprocessing import image\n",
    "import numpy as np\n",
    "\n",
    "# dimensions of our images\n",
    "img_width, img_height = IMG_WIDTH, IMG_HEIGHT\n",
    "\n",
    "# load the model we saved\n",
    "model = load_model('D:\\\\Machine Learning\\\\Working_models\\\\Car_logo_rmsprop_CNN.h5')\n",
    "\n",
    "# predicting images\n",
    "img = image.load_img('C:\\\\Users\\\\Hetav Pandya\\\\Pictures\\\\mazda.jpg', target_size=(img_width, img_height))\n",
    "x = image.img_to_array(img)\n",
    "x = np.expand_dims(x, axis=0)\n",
    "plt.imshow(img)\n",
    "\n",
    "images = np.vstack([x])\n",
    "classes = model.predict_classes(images, batch_size=10)\n",
    "\n",
    "# Predicting multiple images at once\n",
    "img = image.load_img('C:\\\\Users\\\\Hetav Pandya\\\\Pictures\\\\lexus.jpg', target_size=(img_width, img_height))\n",
    "y = image.img_to_array(img)\n",
    "y = np.expand_dims(y, axis=0)\n",
    "plt.imshow(img)\n",
    "\n",
    "img = image.load_img('C:\\\\Users\\\\Hetav Pandya\\\\Pictures\\\\toyota.jpg', target_size=(img_width, img_height))\n",
    "w = image.img_to_array(img)\n",
    "w = np.expand_dims(w, axis=0)\n",
    "plt.imshow(img)\n",
    "\n",
    "# pass the list of multiple images np.vstack()\n",
    "images = np.vstack([x, y, w])\n",
    "classes = np.argmax(model.predict(images), axis=-1)\n",
    "\n",
    "# classes = model.predict_classes(images, batch_size=10)\n",
    "\n",
    "# print the classes, the images belong to\n",
    "print(classes)\n",
    "for elem in classes:\n",
    "    print(cols[elem])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "deepLearning",
   "language": "python",
   "name": "deeplearning"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}