Countering_Data_Imbalance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tropical Cyclone Intensity Estimation using a Deep  Convolutional Neural Network - Part 3 \n",
    "\n",
    "**Contents of the this notebook:**\n",
    "\n",
    "- [Understand the drawbacks of existing solution](#Understanding-the-drawbacks)\n",
    "- [Working out the solution](#Working-out-the-solution)\n",
    "    - [Data Augmentation](#Data-Augmentation)\n",
    "- [Training the model](#Training-the-Model-with-Data-Augmentation)\n",
    "\n",
    "**By the end of this notebook participant will:**\n",
    "\n",
    "- Learn about improving the previous model.\n",
    "- Data Augmentation.\n",
    "- Tweaking Hyperparameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding the drawbacks\n",
    "\n",
    "```python3\n",
    "Simply put, a machine learning model is only as good as the data it is fed with\n",
    "```\n",
    "We have achieved an accuracy nearly of ~85% running with 4 epochs. Now we will try to increase the accuracy by taking a closer look at the dataset and images. We can observe the following from our previous notebook : "
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "NC :441\n",
    "TD :4033\n",
    "TC :7948\n",
    "H1 :5340\n",
    "H2 :3150\n",
    "H3 :2441\n",
    "H4 :2114\n",
    "H5 :390"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First thing that we will notice from the category count is that the number of images per category is very un-uniform with ratios of TC: H5 **greater than 1:20**, This imbalance can bias the vision of our CNN model because predicting wrong on the minority class wouldn't impact the model a lot as the class contribution is less than 5% of the dataset.\n",
    "\n",
    "The same can be shown also by the heatmap we obtained in the previous notebook : Notice Most of Classes with higher data was predicted correctly and the minority class was more mis-predicted than the other classes \n",
    "![alt_text](images/heatmap.png)\n",
    "\n",
    "\n",
    "Let us see now how we solve that problem using data augmentation : "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working out the solution\n",
    "\n",
    "## Data Augmentation \n",
    "\n",
    "To decrease the un-uniformity, we will be flipping and rotating images to compensate for the lack of data for class with less samples: \n",
    "\n",
    "![alt text](images/augment.png)\n",
    "\n",
    "\n",
    "We will be using OpenCV for Flipping and Image Rotations. \n",
    "\n",
    "``` python\n",
    "cv2.flip(img,0)\n",
    "cv2.flip(img,1)\n",
    "cv2.warpAffine(img, cv2.getRotationMatrix2D(center, 90, 1.0), (h, w))\n",
    "cv2.warpAffine(img, cv2.getRotationMatrix2D(center, 180, 1.0), (w, h))\n",
    "cv2.warpAffine(img, cv2.getRotationMatrix2D(center, 270, 1.0), (h, w))\n",
    "```\n",
    "\n",
    "There are other ways to counter data imbalance such as Class weightage, Oversampling, SMOTE etc.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the Model with Data Augmentation \n",
    "\n",
    "\n",
    "We create a new function called `augmentation(name,category,filenames,labels,i)` and here we add more samples to Category which have imbalanced data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('/workspace/python/source_code')\n",
    "# Import Utlility functions\n",
    "from utils import * \n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n",
    "\n",
    "\n",
    "# Define the Augmentation Function \n",
    "def augmentation(name,category,filenames,labels,i):\n",
    "    # Important Constants\n",
    "    file_path = \"Dataset/Aug/\"\n",
    "    images = []\n",
    "    (h, w) = (232,232)\n",
    "    center = (w / 2, h / 2)\n",
    "    angle90 = 90\n",
    "    angle180 = 180\n",
    "    angle270 = 270\n",
    "    scale = 1.0\n",
    "    img = load_image(name , interpolation = cv2.INTER_LINEAR)\n",
    "    \n",
    "    \n",
    "    if category == 0 :\n",
    "        images.append(cv2.flip(img,0))\n",
    "    elif category == 1 :\n",
    "        pass\n",
    "    elif category == 2 :\n",
    "        pass\n",
    "    elif category == 3 :\n",
    "        pass\n",
    "    elif category == 4 :\n",
    "        pass\n",
    "    elif category == 5 :\n",
    "        pass\n",
    "    elif category == 6 :\n",
    "        pass\n",
    "    elif category == 7 :\n",
    "        images.append(cv2.flip(img,0))\n",
    "        \n",
    "        \n",
    "    for j in range(len(images)):\n",
    "        cv2.imwrite(file_path+str(i+j)+'.jpeg',images[j])\n",
    "        filenames.append(file_path+str(i+j)+'.jpeg')\n",
    "        labels.append(category)\n",
    "    i = i + len(images)\n",
    "    return i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We pass this function to our `load_dataset()` function to generate these augmentations. \n",
    "\n",
    "Kindly wait for a couple of minutes while augments the images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "................................\n",
      "28574\n"
     ]
    }
   ],
   "source": [
    "filenames,labels = load_dataset(augment_fn = augmentation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[357, 357, 357, 357, 357, 357, 357, 357]\n",
      "Counter({2: 7923, 3: 5326, 1: 3790, 4: 2921, 5: 2323, 6: 2165, 7: 739, 0: 531})\n"
     ]
    }
   ],
   "source": [
    "# Set the Size of the Validation set\n",
    "val_filenames , val_labels = make_test_set(filenames,labels,val=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make train test set \n",
    "test = 0.1\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(filenames, labels, test_size=test, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "y_train = tf.one_hot(y_train,depth=8)\n",
    "y_test = tf.one_hot(y_test,depth=8)\n",
    "val_labels = tf.one_hot(val_labels,depth=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make Dataset compatible with Tensorflow Data Pipelining.\n",
    "train,test,val = make_dataset((x_train,y_train,128),(x_test,y_test,32),(val_filenames,val_labels,32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The model described in the paper :\n",
    "\n",
    "Now we will be using the model described in the paper to evaluate it's accuracy on the new dataset.\n",
    "\n",
    "![alt_text](images/model.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d (Conv2D)              (None, 75, 75, 64)        19264     \n",
      "_________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D) (None, 37, 37, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 33, 33, 256)       409856    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 16, 16, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 16, 16, 288)       663840    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 15, 15, 288)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 272)       705296    \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 256)       626944    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 9216)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 3584)              33033728  \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 2048)              7342080   \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 8)                 16392     \n",
      "=================================================================\n",
      "Total params: 42,817,400\n",
      "Trainable params: 42,817,400\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "tf.random.set_seed(1337)\n",
    "\n",
    "import tensorflow.keras\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, Flatten ,Dropout, MaxPooling2D\n",
    "from tensorflow.keras import backend as K \n",
    "\n",
    "#Reset Graphs and Create Sequential model\n",
    "\n",
    "K.clear_session()\n",
    "model = Sequential()\n",
    "#Convolution Layers\n",
    "\n",
    "model.add(Conv2D(64, kernel_size=10,strides=3, activation='relu', input_shape=(232,232,3)))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides=2))\n",
    "model.add(Conv2D(256, kernel_size=5,strides=1,activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides=2))\n",
    "model.add(Conv2D(288, kernel_size=3,strides=1,padding='same',activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2),strides=1))\n",
    "model.add(Conv2D(272, kernel_size=3,strides=1,padding='same',activation='relu'))\n",
    "model.add(Conv2D(256, kernel_size=3,strides=1,activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3),strides=2))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Flatten())\n",
    "\n",
    "#Linear Layers \n",
    "\n",
    "model.add(Dense(3584,activation='relu'))\n",
    "model.add(Dense(2048,activation='relu'))\n",
    "model.add(Dense(8, activation='softmax'))\n",
    "\n",
    "# Print Model Summary\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/4\n",
      "181/181 [==============================] - 57s 245ms/step - loss: 0.7175 - accuracy: 0.7548 - top2_acc: 0.9026 - val_loss: 0.4770 - val_accuracy: 0.8337 - val_top2_acc: 0.9443\n",
      "Epoch 2/4\n",
      "181/181 [==============================] - 13s 73ms/step - loss: 0.5526 - accuracy: 0.8028 - top2_acc: 0.9329 - val_loss: 0.4178 - val_accuracy: 0.8536 - val_top2_acc: 0.9468\n",
      "Epoch 3/4\n",
      "181/181 [==============================] - 15s 83ms/step - loss: 0.4849 - accuracy: 0.8278 - top2_acc: 0.9440 - val_loss: 0.3553 - val_accuracy: 0.8729 - val_top2_acc: 0.9576\n",
      "Epoch 4/4\n",
      "181/181 [==============================] - 13s 73ms/step - loss: 0.4486 - accuracy: 0.8376 - top2_acc: 0.9516 - val_loss: 0.3235 - val_accuracy: 0.8894 - val_top2_acc: 0.9667\n",
      "Test loss: 0.2548527121543884\n",
      "Test accuracy: 0.9195178747177124\n"
     ]
    }
   ],
   "source": [
    "import functools\n",
    "# Include Top-2 Accuracy Metrics \n",
    "top2_acc = functools.partial(tensorflow.keras.metrics.top_k_categorical_accuracy, k=2)\n",
    "top2_acc.__name__ = 'top2_acc'\n",
    "\n",
    "#Define Number of Epochs\n",
    "epochs = 4\n",
    "\n",
    "#But Training our model from scratch will take a long time\n",
    "#So we will load a partially trained model to speedup the process \n",
    "model.load_weights(\"trained_16.h5\")\n",
    "\n",
    "# Optimizer\n",
    "sgd = tensorflow.keras.optimizers.SGD(lr=0.001, decay=1e-6, momentum=0.9)\n",
    "\n",
    "\n",
    "#Compile Model with Loss Function , Optimizer and Metrics\n",
    "model.compile(loss=tensorflow.keras.losses.categorical_crossentropy, \n",
    "              optimizer=sgd,\n",
    "              metrics=['accuracy',top2_acc])\n",
    "\n",
    "# Train the Model \n",
    "trained_model = model.fit(train,\n",
    "          epochs=epochs,\n",
    "          verbose=1,\n",
    "          validation_data=val)\n",
    "\n",
    "# Test Model Aganist Validation Set\n",
    "score = model.evaluate(test, verbose=0)\n",
    "print('Test loss:', score[0])\n",
    "print('Test accuracy:', score[1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualisations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "f = plt.figure(figsize=(15,5))\n",
    "ax = f.add_subplot(121)\n",
    "ax.plot(trained_model.history['accuracy'])\n",
    "ax.plot(trained_model.history['val_accuracy'])\n",
    "ax.set_title('Model Accuracy')\n",
    "ax.set_ylabel('Accuracy')\n",
    "ax.set_xlabel('Epoch')\n",
    "ax.legend(['Train', 'Val'])\n",
    "\n",
    "ax2 = f.add_subplot(122)\n",
    "ax2.plot(trained_model.history['loss'])\n",
    "ax2.plot(trained_model.history['val_loss'])\n",
    "ax2.set_title('Model Loss')\n",
    "ax2.set_ylabel('Loss')\n",
    "ax2.set_xlabel('Epoch')\n",
    "ax2.legend(['Train', 'Val'],loc= 'upper left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import pandas as pd\n",
    "\n",
    "#Plotting a heatmap using the confusion matrix\n",
    "\n",
    "pred = model.predict(val)\n",
    "p = np.argmax(pred, axis=1)\n",
    "y_valid = np.argmax(val_labels, axis=1, out=None)\n",
    "results = confusion_matrix(y_valid, p) \n",
    "classes=['NC','TD','TC','H1','H3','H3','H4','H5']\n",
    "df_cm = pd.DataFrame(results, index = [i for i in classes], columns = [i for i in classes])\n",
    "plt.figure(figsize = (15,15))\n",
    "\n",
    "sn.heatmap(df_cm, annot=True, cmap=\"Blues\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us now save our Model and the trained weights for future usage :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Save Our Model \n",
    "model.save('cyc_pred.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Licensing\n",
    "This material is released by OpenACC-Standard.org, in collaboration with NVIDIA Corporation, under the Creative Commons Attribution 4.0 International (CC BY 4.0)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&ensp;\n",
    "[Home Page](../Start_Here.ipynb)\n",
    "\n",
    "\n",
    "[Previous Notebook](Manipulation_of_Image_Data_and_Category_Determination_using_Text_Data.ipynb)\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "[1](The_Problem_Statement.ipynb)\n",
    "[2](Approach_to_the_Problem_&_Inspecting_and_Cleaning_the_Required_Data.ipynb)\n",
    "[3](Manipulation_of_Image_Data_and_Category_Determination_using_Text_Data.ipynb)\n",
    "[4]\n",
    "[5](Competition.ipynb)\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "&emsp;&emsp;&emsp;&emsp;&emsp;\n",
    "[Next Notebook](Competition.ipynb)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}