3-EfficientNetB0_for_transfer_learning.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning:\n",
    "\n",
    "In transfer learning, the knowledge of an already trained machine learning model is applied to a different but related problem. For example, if you trained a simple classifier to predict whether an image contains a watch, you could use the knowledge that the model gained during its training to recognize other objects like clock.\n",
    "\n",
    "\n",
    "#### Pre trained model as Feature Extractor\n",
    "Remove the last dense layer which is performing classification and use this pre trained model as Feature Extractor.Fixed feature extractor for the new dataset.\n",
    "\n",
    "For example, if you want to build a self learning car. You can spend years to build a decent image recognition algorithm from scratch or you can take inception model (a pre-trained model) from Google which was built on ImageNet data to identify images in those pictures.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dataset Prepration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['earphone', 'headphone', 'phones']\n",
      "Types of classes labels found:  3\n",
      "     Labels                         image\n",
      "0  earphone  dataset_path/earphone/21.png\n",
      "1  earphone  dataset_path/earphone/22.png\n",
      "2  earphone  dataset_path/earphone/23.png\n",
      "3  earphone  dataset_path/earphone/24.png\n",
      "4  earphone  dataset_path/earphone/25.png\n",
      "     Labels                           image\n",
      "98   phones  dataset_path/phones/phs112.png\n",
      "99   phones  dataset_path/phones/phs123.jpg\n",
      "100  phones  dataset_path/phones/phs123.png\n",
      "101  phones  dataset_path/phones/phs157.png\n",
      "102  phones  dataset_path/phones/phs165.png\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "dataset_path = os.listdir('dataset')\n",
    "\n",
    "print (dataset_path)  #what kinds of classes are in this dataset\n",
    "\n",
    "print(\"Types of classes labels found: \", len(dataset_path))\n",
    "\n",
    "class_labels = []\n",
    "\n",
    "for item in dataset_path:\n",
    " # Get all the file names\n",
    " all_classes = os.listdir('dataset' + '/' +item)\n",
    " #print(all_classes)\n",
    "\n",
    " # Add them to the list\n",
    " for room in all_classes:\n",
    "    class_labels.append((item, str('dataset_path' + '/' +item) + '/' + room))\n",
    "    #print(class_labels[:5])\n",
    "    \n",
    "    \n",
    "# Build a dataframe        \n",
    "df = pd.DataFrame(data=class_labels, columns=['Labels', 'image'])\n",
    "print(df.head())\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of images in the dataset:  103\n",
      "headphone    50\n",
      "phones       29\n",
      "earphone     24\n",
      "Name: Labels, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Let's check how many samples for each category are present\n",
    "print(\"Total number of images in the dataset: \", len(df))\n",
    "\n",
    "label_count = df['Labels'].value_counts()\n",
    "print(label_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "path = 'dataset/'\n",
    "dataset_path = os.listdir('dataset')\n",
    "\n",
    "im_size = 224\n",
    "\n",
    "images = []\n",
    "labels = []\n",
    "\n",
    "for i in dataset_path:\n",
    "    data_path = path + str(i)  \n",
    "    filenames = [i for i in os.listdir(data_path) ]\n",
    "   \n",
    "    for f in filenames:\n",
    "        img = cv2.imread(data_path + '/' + f)\n",
    "        img = cv2.resize(img, (im_size, im_size))\n",
    "        images.append(img)\n",
    "        labels.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(103, 224, 224, 3)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#This model takes input images of shape (224, 224, 3), and the input data should range [0, 255]. \n",
    "\n",
    "images = np.array(images)\n",
    "\n",
    "images = images.astype('float32') / 255.0\n",
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['earphone' 'earphone' 'earphone' 'earphone' 'earphone' 'earphone'\n",
      " 'earphone' 'earphone' 'earphone' 'earphone' 'earphone' 'earphone'\n",
      " 'earphone' 'earphone' 'earphone' 'earphone' 'earphone' 'earphone'\n",
      " 'earphone' 'earphone' 'earphone' 'earphone' 'earphone' 'earphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'headphone' 'headphone' 'headphone' 'headphone'\n",
      " 'headphone' 'headphone' 'phones' 'phones' 'phones' 'phones' 'phones'\n",
      " 'phones' 'phones' 'phones' 'phones' 'phones' 'phones' 'phones' 'phones'\n",
      " 'phones' 'phones' 'phones' 'phones' 'phones' 'phones' 'phones' 'phones'\n",
      " 'phones' 'phones' 'phones' 'phones' 'phones' 'phones' 'phones' 'phones']\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import LabelEncoder , OneHotEncoder\n",
    "y=df['Labels'].values\n",
    "print(y)\n",
    "\n",
    "y_labelencoder = LabelEncoder ()\n",
    "y = y_labelencoder.fit_transform (y)\n",
    "print (y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]\n",
      " [1. 0. 0.]]\n",
      "[[0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 1. 0.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]\n",
      " [0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "y=y.reshape(-1,1)\n",
    "\n",
    "from sklearn.compose import ColumnTransformer\n",
    "ct = ColumnTransformer([('my_ohe', OneHotEncoder(), [0])], remainder='passthrough')\n",
    "Y = ct.fit_transform(y) #.toarray()\n",
    "print(Y[:5])\n",
    "print(Y[35:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(97, 224, 224, 3)\n",
      "(97, 3)\n",
      "(6, 224, 224, 3)\n",
      "(6, 3)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils import shuffle\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "images, Y = shuffle(images, Y, random_state=1)\n",
    "\n",
    "\n",
    "train_x, test_x, train_y, test_y = train_test_split(images, Y, test_size=0.05, random_state=415)\n",
    "\n",
    "#inpect the shape of the training and testing.\n",
    "print(train_x.shape)\n",
    "print(train_y.shape)\n",
    "print(test_x.shape)\n",
    "print(test_y.shape)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer learning from pre-trained weights\n",
    "\n",
    "#### Here we initialize the model with pre-trained ImageNet weights,and we fine-tune it on our own dataset.\n",
    "\n",
    "\n",
    "\n",
    "The first step to transfer learning is to freeze all layers and train only the top layers. For this step, a relatively large learning rate (1e-2) can be used. \n",
    "\n",
    "EfficientNetB0(include_top=False, input_tensor=x, weights=\"imagenet\")\n",
    "\n",
    "include_top=False means we don't want to use classification layer(Dense from the pretrained network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import imread\n",
    "from matplotlib.pyplot import imshow\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.imagenet_utils import decode_predictions\n",
    "from tensorflow.keras.applications.imagenet_utils import preprocess_input\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.applications import EfficientNetB0\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "\n",
    "\n",
    "NUM_CLASSES = 3\n",
    "IMG_SIZE = 224\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def build_model(num_classes):\n",
    "    inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))\n",
    "    #x = img_augmentation(inputs)\n",
    "    x = inputs\n",
    "    model = EfficientNetB0(include_top=False, input_tensor=x, weights=\"imagenet\")\n",
    "\n",
    "    # Freeze the pretrained weights\n",
    "    model.trainable = False\n",
    "\n",
    "    # Rebuild top\n",
    "    x = layers.GlobalAveragePooling2D(name=\"avg_pool\")(model.output)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "\n",
    "    top_dropout_rate = 0.2\n",
    "    x = layers.Dropout(top_dropout_rate, name=\"top_dropout\")(x)\n",
    "    outputs = layers.Dense(NUM_CLASSES, activation=\"softmax\", name=\"pred\")(x)\n",
    "\n",
    "    # Compile\n",
    "    model = tf.keras.Model(inputs, outputs, name=\"EfficientNet\")\n",
    "    optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)\n",
    "    model.compile(\n",
    "        optimizer=optimizer, loss=\"categorical_crossentropy\", metrics=[\"accuracy\"]\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = build_model(num_classes=NUM_CLASSES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "def plot_hist(hist):\n",
    "    plt.plot(hist.history[\"accuracy\"])\n",
    "    #plt.plot(hist.history[\"val_accuracy\"])\n",
    "    plt.title(\"model accuracy\")\n",
    "    plt.ylabel(\"accuracy\")\n",
    "    plt.xlabel(\"epoch\")\n",
    "    plt.legend([\"train\", \"validation\"], loc=\"upper left\")\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "#plot_hist(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "4/4 - 1s - loss: 2.5313 - accuracy: 0.3093\n",
      "Epoch 2/30\n",
      "4/4 - 1s - loss: 3.7580 - accuracy: 0.2784\n",
      "Epoch 3/30\n",
      "4/4 - 1s - loss: 2.7970 - accuracy: 0.2887\n",
      "Epoch 4/30\n",
      "4/4 - 1s - loss: 2.4532 - accuracy: 0.3505\n",
      "Epoch 5/30\n",
      "4/4 - 1s - loss: 1.9094 - accuracy: 0.3402\n",
      "Epoch 6/30\n",
      "4/4 - 1s - loss: 2.9794 - accuracy: 0.3608\n",
      "Epoch 7/30\n",
      "4/4 - 1s - loss: 2.2961 - accuracy: 0.3711\n",
      "Epoch 8/30\n",
      "4/4 - 1s - loss: 2.7463 - accuracy: 0.3918\n",
      "Epoch 9/30\n",
      "4/4 - 1s - loss: 1.7156 - accuracy: 0.4330\n",
      "Epoch 10/30\n",
      "4/4 - 1s - loss: 2.0100 - accuracy: 0.2887\n",
      "Epoch 11/30\n",
      "4/4 - 1s - loss: 1.6033 - accuracy: 0.4227\n",
      "Epoch 12/30\n",
      "4/4 - 1s - loss: 1.5589 - accuracy: 0.4021\n",
      "Epoch 13/30\n",
      "4/4 - 1s - loss: 1.4422 - accuracy: 0.4536\n",
      "Epoch 14/30\n",
      "4/4 - 1s - loss: 1.3959 - accuracy: 0.3093\n",
      "Epoch 15/30\n",
      "4/4 - 1s - loss: 1.4293 - accuracy: 0.2784\n",
      "Epoch 16/30\n",
      "4/4 - 1s - loss: 1.4343 - accuracy: 0.3918\n",
      "Epoch 17/30\n",
      "4/4 - 1s - loss: 1.8570 - accuracy: 0.4227\n",
      "Epoch 18/30\n",
      "4/4 - 1s - loss: 1.6215 - accuracy: 0.4330\n",
      "Epoch 19/30\n",
      "4/4 - 1s - loss: 2.2830 - accuracy: 0.4021\n",
      "Epoch 20/30\n",
      "4/4 - 1s - loss: 1.5491 - accuracy: 0.3918\n",
      "Epoch 21/30\n",
      "4/4 - 1s - loss: 1.2870 - accuracy: 0.4742\n",
      "Epoch 22/30\n",
      "4/4 - 1s - loss: 1.2719 - accuracy: 0.3918\n",
      "Epoch 23/30\n",
      "4/4 - 1s - loss: 1.5234 - accuracy: 0.3608\n",
      "Epoch 24/30\n",
      "4/4 - 1s - loss: 1.5841 - accuracy: 0.3608\n",
      "Epoch 25/30\n",
      "4/4 - 1s - loss: 1.7138 - accuracy: 0.2784\n",
      "Epoch 26/30\n",
      "4/4 - 1s - loss: 1.4789 - accuracy: 0.3814\n",
      "Epoch 27/30\n",
      "4/4 - 1s - loss: 1.6273 - accuracy: 0.3608\n",
      "Epoch 28/30\n",
      "4/4 - 1s - loss: 1.5558 - accuracy: 0.3093\n",
      "Epoch 29/30\n",
      "4/4 - 1s - loss: 1.5504 - accuracy: 0.4845\n",
      "Epoch 30/30\n",
      "4/4 - 1s - loss: 1.8504 - accuracy: 0.2680\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": [
    "epochs = 30 \n",
    "hist = model.fit(train_x, train_y, epochs=epochs, verbose=2)\n",
    "plot_hist(hist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 6ms/step - loss: 1.0760 - accuracy: 0.3333\n",
      "Loss = 1.0759620666503906\n",
      "Test Accuracy = 0.3333333432674408\n"
     ]
    }
   ],
   "source": [
    "preds = model.evaluate(test_x, test_y)\n",
    "print (\"Loss = \" + str(preds[0]))\n",
    "print (\"Test Accuracy = \" + str(preds[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input image shape: (1, 224, 224, 3)\n",
      "predicted class:  [[0.5195962  0.35020834 0.13019536]]\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": [
    "img_path = 'unseen.jfif'\n",
    "\n",
    "\n",
    "img = cv2.imread(img_path)\n",
    "img = cv2.resize(img, (224, 224))\n",
    "\n",
    "x = np.expand_dims(img, axis=0)\n",
    "x = preprocess_input(x)\n",
    "\n",
    "print('Input image shape:', x.shape)\n",
    "\n",
    "my_image = imread(img_path)\n",
    "imshow(my_image)\n",
    "\n",
    "\n",
    "\n",
    "preds=model.predict(x)\n",
    "print(\"predicted class: \", preds )    # probabilities for being in each of the 3 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}