cifar_image_classifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CIFAR10 Multi-Class Image Classification\n",
    "\n",
    "- Keras notes https://keras.io/api/datasets/cifar10/\n",
    "- CIFAR dataset details https://www.cs.toronto.edu/~kriz/cifar.html\n",
    "- How to process your own images with Keras https://keras.io/api/preprocessing/image/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import numpy as np\n",
    "from keras.datasets import cifar10\n",
    "from tensorflow import keras\n",
    "from keras.layers import Conv2D\n",
    "\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Flatten\n",
    "# from keras.optimizers import SGD\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Already pre-processed vectorized, already split data\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 32, 32, 3)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What are the dimensions of our tensor look like?\n",
    "# 50,000 images\n",
    "# 32 by 32 pixels\n",
    "# 3 color channels for RGB\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model parameters\n",
    "num_classes = 10\n",
    "\n",
    "input_shape = (32, 32, 3, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Target Variable Classes\n",
    "\n",
    "| Label | Description |\n",
    "| ----- | ----------- |\n",
    "| 0     | airplane    |\n",
    "| 1     | automobile  |\n",
    "| 2     | bird        |\n",
    "| 3     | cat         |\n",
    "| 4     | deer        |\n",
    "| 5     | dog         |\n",
    "| 6     | frog        |\n",
    "| 7     | horse       |\n",
    "| 8     | ship        |\n",
    "| 9     | truck       |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train label: [6]\n"
     ]
    }
   ],
   "source": [
    "digit = x_train[0]\n",
    "plt.imshow(digit, cmap=plt.cm.binary)\n",
    "plt.show()\n",
    "print(\"Train label:\", y_train[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train label: [9]\n"
     ]
    }
   ],
   "source": [
    "digit = x_train[1]\n",
    "plt.imshow(digit, cmap=plt.cm.binary)\n",
    "plt.show()\n",
    "print(\"Train label:\", y_train[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scale images to the [0, 1] range. \n",
    "x_train = x_train.astype(\"float32\") / 255\n",
    "x_test = x_test.astype(\"float32\") / 255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (50000, 32, 32, 3, 1)\n",
      "50000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "# Make sure images have shape (32, 32, 3, 1)\n",
    "x_train = np.expand_dims(x_train, -1)\n",
    "x_test = np.expand_dims(x_test, -1)\n",
    "print(\"x_train shape:\", x_train.shape)\n",
    "print(x_train.shape[0], \"train samples\")\n",
    "print(x_test.shape[0], \"test samples\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert class vectors to binary class matrices\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# example of a 3-block vgg style architecture\n",
    "model = keras.Sequential()\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same', input_shape=(32, 32, 3)))\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_uniform', padding='same'))\n",
    "model.add(MaxPooling2D((2, 2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Input 0 of layer \"max_pooling2d_4\" is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 32, 30, 1, 32)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/1s/_g4b0srs1s13gfc1m19673dc0000gn/T/ipykernel_63147/3807270608.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Build the model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m model = keras.Sequential(\n\u001b[0m\u001b[1;32m      3\u001b[0m     [\n\u001b[1;32m      4\u001b[0m         \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mInput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0mlayers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mConv2D\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m32\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkernel_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactivation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"relu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/tensorflow/python/training/tracking/base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    627\u001b[0m     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    628\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 629\u001b[0;31m       \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    630\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    631\u001b[0m       \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[0;31m# pylint: disable=protected-access\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 67\u001b[0;31m       \u001b[0;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     68\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     69\u001b[0m       \u001b[0;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/anaconda3/lib/python3.9/site-packages/keras/engine/input_spec.py\u001b[0m in \u001b[0;36massert_input_compatibility\u001b[0;34m(input_spec, inputs, layer_name)\u001b[0m\n\u001b[1;32m    212\u001b[0m       \u001b[0mndim\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrank\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    213\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mndim\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mspec\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 214\u001b[0;31m         raise ValueError(f'Input {input_index} of layer \"{layer_name}\" '\n\u001b[0m\u001b[1;32m    215\u001b[0m                          \u001b[0;34m'is incompatible with the layer: '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    216\u001b[0m                          \u001b[0;34mf'expected ndim={spec.ndim}, found ndim={ndim}. '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: Input 0 of layer \"max_pooling2d_4\" is incompatible with the layer: expected ndim=4, found ndim=5. Full shape received: (None, 32, 30, 1, 32)"
     ]
    }
   ],
   "source": [
    "# # Build the model\n",
    "# model = keras.Sequential(\n",
    "#     [\n",
    "#         keras.Input(shape=input_shape),\n",
    "#         layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "#         layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "#         layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "#         layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "#         layers.Flatten(),\n",
    "#         layers.Dropout(0.5),\n",
    "#         layers.Dense(num_classes, activation=\"softmax\"),\n",
    "#     ]\n",
    "# )\n",
    "\n",
    "# model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 128\n",
    "epochs = 3\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)\n",
    "model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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