cifar10_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e27856d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
      "170498071/170498071 [==============================] - 190s 1us/step\n"
     ]
    }
   ],
   "source": [
    "from keras.datasets import cifar10\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "(train_X,train_Y),(test_X,test_Y)=cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bbd00262",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 6\n",
    "plt.figure(figsize=(20,10))\n",
    "for i in range(n):\n",
    "    plt.subplot(330+1+i)\n",
    "    plt.imshow(train_X[i])\n",
    "plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c33e5df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import utils\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.layers import Dropout\n",
    "from keras.layers import Flatten\n",
    "from keras.constraints import max_norm\n",
    "from keras.optimizers import SGD\n",
    "from keras.layers import Conv2D\n",
    "from keras.layers import MaxPooling2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a45f7bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = train_X.astype('float32')\n",
    "test_x = test_X.astype('float32')\n",
    "\n",
    "train_x = train_x/255.0\n",
    "test_x = test_x/255.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bf558514",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_y = utils.to_categorical(train_Y)\n",
    "test_y = utils.to_categorical(test_Y)\n",
    "\n",
    "num_classes =test_y.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6f18c60b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10,\n",
       " array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 1., 0.],\n",
       "        [0., 0., 0., ..., 0., 1., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 1., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 1., 0., 0.]], dtype=float32))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_classes,test_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "010584fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Conv2D(filters=32,kernel_size=(4,4),input_shape=(32,32,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Conv2D(filters=32,kernel_size=(4,4),input_shape=(32,32,3),activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2,2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(256,activation='relu'))\n",
    "model.add(Dense(10,activation='softmax'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "213dc9c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "94e281dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_10\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_10 (Conv2D)          (None, 29, 29, 32)        1568      \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 14, 14, 32)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_11 (Conv2D)          (None, 11, 11, 32)        16416     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 5, 5, 32)          0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_4 (Flatten)         (None, 800)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 256)               205056    \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 10)                2570      \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 225610 (881.29 KB)\n",
      "Trainable params: 225610 (881.29 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "d2fcbb0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 19s 12ms/step - loss: 0.3880 - accuracy: 0.8634 - val_loss: 1.1467 - val_accuracy: 0.6810\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 19s 12ms/step - loss: 0.3370 - accuracy: 0.8813 - val_loss: 1.2855 - val_accuracy: 0.6785\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 20s 13ms/step - loss: 0.2908 - accuracy: 0.8973 - val_loss: 1.3662 - val_accuracy: 0.6686\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 20s 12ms/step - loss: 0.2609 - accuracy: 0.9075 - val_loss: 1.4042 - val_accuracy: 0.6761\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 19s 12ms/step - loss: 0.2181 - accuracy: 0.9238 - val_loss: 1.5606 - val_accuracy: 0.6736\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 20s 13ms/step - loss: 0.2009 - accuracy: 0.9284 - val_loss: 1.6459 - val_accuracy: 0.6773\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 20s 13ms/step - loss: 0.1768 - accuracy: 0.9373 - val_loss: 1.7448 - val_accuracy: 0.6697\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 19s 12ms/step - loss: 0.1611 - accuracy: 0.9414 - val_loss: 1.8999 - val_accuracy: 0.6722\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 20s 13ms/step - loss: 0.1385 - accuracy: 0.9510 - val_loss: 2.0222 - val_accuracy: 0.6730\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 20s 13ms/step - loss: 0.1441 - accuracy: 0.9500 - val_loss: 2.1397 - val_accuracy: 0.6642\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x25ee9ec8880>"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_x,train_y,\n",
    "         validation_data =(test_x,test_y),\n",
    "         epochs = 10 , batch_size =32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "4a7d4ba1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 1s 3ms/step - loss: 2.1397 - accuracy: 0.6642\n",
      "66.42000079154968\n"
     ]
    }
   ],
   "source": [
    "_,acc=model.evaluate(test_x,test_y)\n",
    "print(acc*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "39133878",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(r\"C:\\Users\\krishaant.S.H\\Downloads\\model1_cifar_5epoch\",save_format='h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "f31e92fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 31ms/step\n",
      "7 horse\n"
     ]
    }
   ],
   "source": [
    "results={\n",
    "   0:'aeroplane',\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'\n",
    "}\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "im=Image.open(r\"C:\\Users\\krishaant.S.H\\Downloads\\horse.jpg\")\n",
    "# the input image is required to be in the shape of dataset, i.e (32,32,3)\n",
    " \n",
    "im=im.resize((32,32))\n",
    "im=np.expand_dims(im,axis=0)\n",
    "im=np.array(im)\n",
    "pred=np.argmax(model.predict([im])[0])\n",
    "print(pred,results[pred])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18e45063",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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