16 ImageNet Application.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0f5ab9e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "29c86931",
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_cnn(input_shape=(224, 224, 3), num_classes=10):\n",
    "    \"\"\"Defines a CNN model with the specified \n",
    "    input shape and number of classes.\"\"\"\n",
    "    model = Sequential()\n",
    "    model.add(Conv2D(32, (3, 3),activation='relu', \n",
    "                   input_shape=input_shape))\n",
    "    model.add(MaxPool2D((2, 2)))\n",
    "    model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "    model.add(MaxPool2D((2, 2)))\n",
    "    model.add(Flatten())\n",
    "    model.add(Dense(num_classes, activation='softmax'))\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "710cd7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = build_cnn()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ab04a18d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_2\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_3 (Conv2D)           (None, 222, 222, 32)      896       \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 111, 111, 32)     0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 109, 109, 64)      18496     \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 54, 54, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 186624)            0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 10)                1866250   \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,885,642\n",
      "Trainable params: 1,885,642\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be879057",
   "metadata": {},
   "source": [
    "#### Create the object of Image Data Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "279656e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_datagen = ImageDataGenerator(\n",
    "    rescale=1/255,\n",
    "    shear_range=0.2,\n",
    "    zoom_range=0.2,\n",
    "    horizontal_flip=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "058e42c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_datagen = ImageDataGenerator(rescale=1/255)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26a29ef0",
   "metadata": {},
   "source": [
    "#### Read the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fe5b704b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 5000 images belonging to 10 classes.\n"
     ]
    }
   ],
   "source": [
    "train_dataset = train_datagen.flow_from_directory(\n",
    "    '/media/mitu/data/datasets/imagenet/train',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "861c948e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 10000 images belonging to 1 classes.\n"
     ]
    }
   ],
   "source": [
    "test_dataset = test_datagen.flow_from_directory(\n",
    "    '/media/mitu/data/datasets/imagenet/val',\n",
    "    target_size=(224, 224),\n",
    "    batch_size=32,\n",
    "    class_mode='categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "24ffa61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'crocodile': 0,\n",
       " 'fish': 1,\n",
       " 'fossils': 2,\n",
       " 'frog': 3,\n",
       " 'insect': 4,\n",
       " 'reptiles': 5,\n",
       " 'scorpion': 6,\n",
       " 'snake': 7,\n",
       " 'spider': 8,\n",
       " 'toad': 9}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "703e410e",
   "metadata": {},
   "outputs": [
    {
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       " 'fish/images/n01443537_50.JPEG',\n",
       " 'fish/images/n01443537_51.JPEG',\n",
       " 'fish/images/n01443537_52.JPEG',\n",
       " 'fish/images/n01443537_53.JPEG',\n",
       " 'fish/images/n01443537_54.JPEG',\n",
       " 'fish/images/n01443537_55.JPEG',\n",
       " 'fish/images/n01443537_56.JPEG',\n",
       " 'fish/images/n01443537_57.JPEG',\n",
       " 'fish/images/n01443537_58.JPEG',\n",
       " 'fish/images/n01443537_59.JPEG',\n",
       " 'fish/images/n01443537_6.JPEG',\n",
       " 'fish/images/n01443537_60.JPEG',\n",
       " 'fish/images/n01443537_61.JPEG',\n",
       " 'fish/images/n01443537_62.JPEG',\n",
       " 'fish/images/n01443537_63.JPEG',\n",
       " 'fish/images/n01443537_64.JPEG',\n",
       " 'fish/images/n01443537_65.JPEG',\n",
       " 'fish/images/n01443537_66.JPEG',\n",
       " 'fish/images/n01443537_67.JPEG',\n",
       " 'fish/images/n01443537_68.JPEG',\n",
       " 'fish/images/n01443537_69.JPEG',\n",
       " 'fish/images/n01443537_7.JPEG',\n",
       " 'fish/images/n01443537_70.JPEG',\n",
       " 'fish/images/n01443537_71.JPEG',\n",
       " 'fish/images/n01443537_72.JPEG',\n",
       " 'fish/images/n01443537_73.JPEG',\n",
       " 'fish/images/n01443537_74.JPEG',\n",
       " 'fish/images/n01443537_75.JPEG',\n",
       " 'fish/images/n01443537_76.JPEG',\n",
       " 'fish/images/n01443537_77.JPEG',\n",
       " 'fish/images/n01443537_78.JPEG',\n",
       " 'fish/images/n01443537_79.JPEG',\n",
       " 'fish/images/n01443537_8.JPEG',\n",
       " 'fish/images/n01443537_80.JPEG',\n",
       " 'fish/images/n01443537_81.JPEG',\n",
       " 'fish/images/n01443537_82.JPEG',\n",
       " 'fish/images/n01443537_83.JPEG',\n",
       " 'fish/images/n01443537_84.JPEG',\n",
       " 'fish/images/n01443537_85.JPEG',\n",
       " 'fish/images/n01443537_86.JPEG',\n",
       " 'fish/images/n01443537_87.JPEG',\n",
       " 'fish/images/n01443537_88.JPEG',\n",
       " 'fish/images/n01443537_89.JPEG',\n",
       " 'fish/images/n01443537_9.JPEG',\n",
       " 'fish/images/n01443537_90.JPEG',\n",
       " 'fish/images/n01443537_91.JPEG',\n",
       " 'fish/images/n01443537_92.JPEG',\n",
       " 'fish/images/n01443537_93.JPEG',\n",
       " 'fish/images/n01443537_94.JPEG',\n",
       " 'fish/images/n01443537_95.JPEG',\n",
       " 'fish/images/n01443537_96.JPEG',\n",
       " 'fish/images/n01443537_97.JPEG',\n",
       " 'fish/images/n01443537_98.JPEG',\n",
       " 'fish/images/n01443537_99.JPEG',\n",
       " ...]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_dataset.filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6ba402fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3dc23899",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='categorical_crossentropy', \n",
    "              optimizer='adam', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e8003b10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/2\n",
      "157/157 [==============================] - 263s 2s/step - loss: 1.2057 - accuracy: 0.5936\n",
      "Epoch 2/2\n",
      "157/157 [==============================] - 227s 1s/step - loss: 1.1645 - accuracy: 0.6100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7f64305d44c0>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(train_dataset, epochs=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "22f582e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4e871909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f64306fba90>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "new = plt.imread('/media/mitu/data/datasets/imagenet/val/images/val_192.JPEG')\n",
    "plt.imshow(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "2ba37ef1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow.keras.preprocessing import image\n",
    "test_image = image.load_img('/media/mitu/data/datasets/imagenet/val/images/val_205.JPEG', \n",
    "                            target_size = (224, 224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c85fb2bf",
   "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"
    }
   ],
   "source": [
    "plt.imshow(test_image);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0163c99a",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_image = image.img_to_array(test_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "722447e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_image = np.expand_dims(test_image, axis = 0)\n",
    "test_image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "39758432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 44ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.argmax(model.predict(test_image))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "a45cb049",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = train_dataset.class_indices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "a7cdfed8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'crocodile': 0,\n",
       " 'fish': 1,\n",
       " 'fossils': 2,\n",
       " 'frog': 3,\n",
       " 'insect': 4,\n",
       " 'reptiles': 5,\n",
       " 'scorpion': 6,\n",
       " 'snake': 7,\n",
       " 'spider': 8,\n",
       " 'toad': 9}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c24650bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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