Transfer Leaning using Keras.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Activate conda environment - transferlearningDemmo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install tensorflow==2.0 #(downgrade from latest version 2.1 to 2.0 for error free implementation ) \n",
    "# installed with python3.6 bcoz tensorflow2 (Python 3.5–3.7) and keras (Python 2.7-3.6)\n",
    "# its tensorflow version 2\n",
    "# https://www.tensorflow.org/install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install keras\n",
    "\n",
    "# Keras transfer learning documentation - https://keras.io/applications/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting matplotlib\n",
      "  Downloading matplotlib-3.1.3-cp36-cp36m-win_amd64.whl (9.1 MB)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\sahil\\anaconda3\\envs\\transferlearningdemmo\\lib\\site-packages (from matplotlib) (2.8.1)\n",
      "Collecting pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1\n",
      "  Downloading pyparsing-2.4.6-py2.py3-none-any.whl (67 kB)\n",
      "Requirement already satisfied: numpy>=1.11 in c:\\users\\sahil\\anaconda3\\envs\\transferlearningdemmo\\lib\\site-packages (from matplotlib) (1.18.1)\n",
      "Collecting cycler>=0.10\n",
      "  Downloading cycler-0.10.0-py2.py3-none-any.whl (6.5 kB)\n",
      "Collecting kiwisolver>=1.0.1\n",
      "  Downloading kiwisolver-1.1.0-cp36-none-win_amd64.whl (57 kB)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\sahil\\anaconda3\\envs\\transferlearningdemmo\\lib\\site-packages (from python-dateutil>=2.1->matplotlib) (1.14.0)\n",
      "Requirement already satisfied: setuptools in c:\\users\\sahil\\anaconda3\\envs\\transferlearningdemmo\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib) (45.2.0.post20200210)\n",
      "Installing collected packages: pyparsing, cycler, kiwisolver, matplotlib\n",
      "Successfully installed cycler-0.10.0 kiwisolver-1.1.0 matplotlib-3.1.3 pyparsing-2.4.6\n"
     ]
    }
   ],
   "source": [
    "!pip install matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pillow\n",
      "  Downloading Pillow-7.0.0-cp36-cp36m-win_amd64.whl (2.0 MB)\n",
      "Installing collected packages: pillow\n",
      "Successfully installed pillow-7.0.0\n"
     ]
    }
   ],
   "source": [
    "! pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from keras.layers import Input, Lambda, Dense, Flatten\n",
    "from keras.models import Model\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from keras.applications.vgg19 import VGG19\n",
    "from keras.applications.resnet_v2 import ResNet101V2\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "from keras.preprocessing import image\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_path = '5-celebrity-faces-dataset/data/train'\n",
    "valid_path = '5-celebrity-faces-dataset/data/val'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'5-celebrity-faces-dataset/data/val'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "valid_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add preprocessing layer to the front of VGG\n",
    "vgg = VGG16(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "#vgg = VGG19(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "\n",
    "\n",
    "#following the RESNET archietcture\n",
    "#vgg = ResNet101V2(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# don't train existing weights\n",
    "for layer in vgg.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "  # useful for getting number of classes\n",
    "folders = glob('5-celebrity-faces-dataset/data/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# our layers - you can add more if you want\n",
    "x = Flatten()(vgg.output)\n",
    "# x = Dense(1000, activation='relu')(x)\n",
    "prediction = Dense(len(folders), activation='softmax')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a model object\n",
    "model = Model(inputs=vgg.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 5)                 125445    \n",
      "=================================================================\n",
      "Total params: 14,840,133\n",
      "Trainable params: 125,445\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# view the structure of the model\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tell the model what cost and optimization method to use\n",
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generating more images for training the model..\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "# Rescaling the test images as train images..\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 93 images belonging to 5 classes.\n",
      "Found 25 images belonging to 5 classes.\n"
     ]
    }
   ],
   "source": [
    "# Building the training and testing sets..\n",
    "\n",
    "training_set = train_datagen.flow_from_directory('5-celebrity-faces-dataset/data/train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 20,\n",
    "                                                 class_mode = 'categorical')\n",
    "\n",
    "test_set = test_datagen.flow_from_directory('5-celebrity-faces-dataset/data/val',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 20,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "5/5 [==============================] - 36s 7s/step - loss: 2.7552 - accuracy: 0.2043 - val_loss: 2.2692 - val_accuracy: 0.2800\n",
      "Epoch 2/25\n",
      "5/5 [==============================] - 39s 8s/step - loss: 1.7088 - accuracy: 0.3871 - val_loss: 1.3857 - val_accuracy: 0.4400\n",
      "Epoch 3/25\n",
      "5/5 [==============================] - 40s 8s/step - loss: 0.7878 - accuracy: 0.7312 - val_loss: 0.9938 - val_accuracy: 0.6800\n",
      "Epoch 4/25\n",
      "5/5 [==============================] - 43s 9s/step - loss: 0.4209 - accuracy: 0.8280 - val_loss: 0.7073 - val_accuracy: 0.8000\n",
      "Epoch 5/25\n",
      "5/5 [==============================] - 70s 14s/step - loss: 0.2099 - accuracy: 0.9785 - val_loss: 0.3460 - val_accuracy: 0.6800\n",
      "Epoch 6/25\n",
      "5/5 [==============================] - 55s 11s/step - loss: 0.1492 - accuracy: 0.9892 - val_loss: 0.7762 - val_accuracy: 0.8400\n",
      "Epoch 7/25\n",
      "5/5 [==============================] - 59s 12s/step - loss: 0.0915 - accuracy: 1.0000 - val_loss: 0.4792 - val_accuracy: 0.7600\n",
      "Epoch 8/25\n",
      "5/5 [==============================] - 54s 11s/step - loss: 0.0674 - accuracy: 1.0000 - val_loss: 0.6928 - val_accuracy: 0.8800\n",
      "Epoch 9/25\n",
      "5/5 [==============================] - 58s 12s/step - loss: 0.0769 - accuracy: 0.9892 - val_loss: 0.5078 - val_accuracy: 0.9200\n",
      "Epoch 10/25\n",
      "5/5 [==============================] - 57s 11s/step - loss: 0.0373 - accuracy: 1.0000 - val_loss: 0.2775 - val_accuracy: 0.9600\n",
      "Epoch 11/25\n",
      "5/5 [==============================] - 55s 11s/step - loss: 0.0375 - accuracy: 1.0000 - val_loss: 0.4495 - val_accuracy: 0.9600\n",
      "Epoch 12/25\n",
      "5/5 [==============================] - 61s 12s/step - loss: 0.0231 - accuracy: 1.0000 - val_loss: 0.3498 - val_accuracy: 0.9200\n",
      "Epoch 13/25\n",
      "5/5 [==============================] - 56s 11s/step - loss: 0.0216 - accuracy: 1.0000 - val_loss: 0.5913 - val_accuracy: 0.9200\n",
      "Epoch 14/25\n",
      "5/5 [==============================] - 56s 11s/step - loss: 0.0335 - accuracy: 1.0000 - val_loss: 0.6003 - val_accuracy: 0.9600\n",
      "Epoch 15/25\n",
      "5/5 [==============================] - 55s 11s/step - loss: 0.0271 - accuracy: 1.0000 - val_loss: 0.4380 - val_accuracy: 0.9600\n",
      "Epoch 16/25\n",
      "5/5 [==============================] - 57s 11s/step - loss: 0.0211 - accuracy: 1.0000 - val_loss: 0.4611 - val_accuracy: 0.9600\n",
      "Epoch 17/25\n",
      "5/5 [==============================] - 57s 11s/step - loss: 0.0196 - accuracy: 1.0000 - val_loss: 0.3204 - val_accuracy: 0.9600\n",
      "Epoch 18/25\n",
      "5/5 [==============================] - 52s 10s/step - loss: 0.0152 - accuracy: 1.0000 - val_loss: 0.4555 - val_accuracy: 0.9200\n",
      "Epoch 19/25\n",
      "5/5 [==============================] - 55s 11s/step - loss: 0.0128 - accuracy: 1.0000 - val_loss: 0.3134 - val_accuracy: 0.9600\n",
      "Epoch 20/25\n",
      "5/5 [==============================] - 58s 12s/step - loss: 0.0161 - accuracy: 1.0000 - val_loss: 0.5006 - val_accuracy: 0.9600\n",
      "Epoch 21/25\n",
      "5/5 [==============================] - 59s 12s/step - loss: 0.0176 - accuracy: 1.0000 - val_loss: 0.2409 - val_accuracy: 1.0000\n",
      "Epoch 22/25\n",
      "5/5 [==============================] - 56s 11s/step - loss: 0.0148 - accuracy: 1.0000 - val_loss: 0.3641 - val_accuracy: 1.0000\n",
      "Epoch 23/25\n",
      "5/5 [==============================] - 58s 12s/step - loss: 0.0133 - accuracy: 1.0000 - val_loss: 0.1923 - val_accuracy: 1.0000\n",
      "Epoch 24/25\n",
      "5/5 [==============================] - 55s 11s/step - loss: 0.0134 - accuracy: 1.0000 - val_loss: 0.1809 - val_accuracy: 0.9600\n",
      "Epoch 25/25\n",
      "5/5 [==============================] - 53s 11s/step - loss: 0.0099 - accuracy: 1.0000 - val_loss: 0.5946 - val_accuracy: 0.9600\n"
     ]
    }
   ],
   "source": [
    "# fit the model..\n",
    "\n",
    "r = model.fit_generator(\n",
    "  training_set,\n",
    "  validation_data=test_set,\n",
    "  epochs=25,\n",
    "  steps_per_epoch=len(training_set),\n",
    "  validation_steps=len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NOT BAD!!! - 96% accuracy without much efforts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# loss..\n",
    "\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# accuracies..\n",
    "\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import tensorflow as tf\n",
    "\n",
    "#from keras.models import load_model\n",
    "\n",
    "model.save('celebrity_faces_transfered_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Validating the new model.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "import numpy as np\n",
    "from IPython.display import Image, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_list = ['5-celebrity-faces-dataset/data/val/elton_john/httpafilesbiographycomimageuploadcfillcssrgbdprgfacehqwMTEODAOTcxNjcMjczMjkzjpg.jpg',\n",
    "              '5-celebrity-faces-dataset/data/val/ben_afflek/httpafilesbiographycomimageuploadcfillcssrgbdprgfacehqwMTENDgMDUODczNDcNTcjpg.jpg',\n",
    "              '5-celebrity-faces-dataset/data/val/jerry_seinfeld/httpblognjcomentertainmentimpactcelebritiesmediumjerrybjpg.jpg',\n",
    "              '5-celebrity-faces-dataset/data/val/madonna/httpcdnfuncheapcomwpcontentuploadsVOGUEjpg.jpg',\n",
    "              '5-celebrity-faces-dataset/data/val/mindy_kaling/httpcdnpastemagazinecomwwwarticlesmindykalingndbookjpg.jpg']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ben_afflek', 'elton_john', 'jerry_seinfeld', 'madonna', 'mindy_kaling']\n",
      "original name: elton_john\n",
      "99.99966621398926 elton_john\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ben_afflek', 'elton_john', 'jerry_seinfeld', 'madonna', 'mindy_kaling']\n",
      "original name: ben_afflek\n",
      "99.51813817024231 ben_afflek\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ben_afflek', 'elton_john', 'jerry_seinfeld', 'madonna', 'mindy_kaling']\n",
      "original name: jerry_seinfeld\n",
      "100.0 jerry_seinfeld\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ben_afflek', 'elton_john', 'jerry_seinfeld', 'madonna', 'mindy_kaling']\n",
      "original name: madonna\n",
      "100.0 madonna\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['ben_afflek', 'elton_john', 'jerry_seinfeld', 'madonna', 'mindy_kaling']\n",
      "original name: mindy_kaling\n",
      "100.0 mindy_kaling\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['ben_afflek',\n",
    "                 'elton_john',\n",
    "                 'jerry_seinfeld',\n",
    "                 'madonna',\n",
    "                 'mindy_kaling'] \n",
    "\n",
    "\n",
    "for image_name in image_list:\n",
    "    img = image.load_img(image_name, target_size=(224, 224))\n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    img_data = preprocess_input(x)\n",
    "    classes = model.predict(img_data)\n",
    "    result = np.squeeze(classes)\n",
    "    result_indices = np.argmax(result)\n",
    "    \n",
    "    print(labels)\n",
    "    print(f'original name: {image_name.split(\"/\")[3]}')\n",
    "    print(result[result_indices]*100, labels[result_indices])\n",
    "    display(Image(filename=image_name))\n",
    "    \n",
    "    '''print(f'original name: {image_name.split(\"/\")[3]}')\n",
    "    print(f'{labels}')\n",
    "    '''\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Awesome Predictions!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To be continue with other state of the art models.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}