Model creator.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "42b35245-93b6-45ed-bcf8-d9ff22473269",
    "_kg_hide-input": true,
    "_kg_hide-output": true,
    "_uuid": "3d3bc91774b6b395666c22dc2cca97af6d5dcbe3"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import keras\n",
    "from keras import backend as K\n",
    "from keras import models, layers, optimizers\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten, Conv2D, MaxPooling2D, Lambda, MaxPool2D, BatchNormalization\n",
    "from keras.layers import Activation,AveragePooling2D\n",
    "from keras.utils import np_utils\n",
    "from keras.utils.np_utils import to_categorical\n",
    "from keras.models import Sequential, model_from_json\n",
    "from keras.models import Model\n",
    "from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint\n",
    "from keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau, ModelCheckpoint\n",
    "from keras.optimizers import SGD, RMSprop, Adam, Adagrad, Adadelta, RMSprop\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.applications.vgg16 import VGG16\n",
    "\n",
    "import sklearn\n",
    "import skimage\n",
    "from sklearn import model_selection\n",
    "from sklearn.model_selection import train_test_split, learning_curve,KFold,cross_val_score,StratifiedKFold\n",
    "from sklearn.utils import class_weight\n",
    "from sklearn.utils import shuffle\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score\n",
    "from skimage.transform import resize\n",
    "\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "\n",
    "import matplotlib.gridspec as gridspec\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import os\n",
    "from glob import glob\n",
    "import random\n",
    "import cv2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sb\n",
    "import zlib\n",
    "import itertools\n",
    "import scipy\n",
    "import csv\n",
    "from tqdm import tqdm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "86a1fb25-c9b2-41fe-8bc3-01d91f7054bb",
    "_uuid": "22c127e3183a316ca314946688e21db95a7dc4ca"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "IMG_SIZE=50\n",
    "train_dir = \"handgestures/train/\"\n",
    "test_dir =  \"handgestures/test/\"\n",
    "def get_data(folder):\n",
    "    #Load the data and labels from the given folder.\n",
    "    X = []\n",
    "    y = []\n",
    "    \n",
    "    for folderName in os.listdir(folder):\n",
    "        if not folderName.startswith('.'):\n",
    "            if folderName in ['A']:\n",
    "                label = 0\n",
    "            elif folderName in ['B']:\n",
    "                label = 1\n",
    "            elif folderName in ['C']:\n",
    "                label = 2\n",
    "            elif folderName in ['D']:\n",
    "                label = 3\n",
    "            elif folderName in ['E']:\n",
    "                label = 4\n",
    "            elif folderName in ['F']:\n",
    "                label = 5\n",
    "            elif folderName in ['G']:\n",
    "                label = 6\n",
    "            elif folderName in ['H']:\n",
    "                label = 7\n",
    "            elif folderName in ['I']:\n",
    "                label = 8\n",
    "            elif folderName in ['K']:\n",
    "                label = 9\n",
    "            elif folderName in ['L']:\n",
    "                label = 10\n",
    "            elif folderName in ['M']:\n",
    "                label = 11\n",
    "            elif folderName in ['N']:\n",
    "                label = 12\n",
    "            elif folderName in ['O']:\n",
    "                label = 13\n",
    "            elif folderName in ['P']:\n",
    "                label = 14\n",
    "            elif folderName in ['Q']:\n",
    "                label = 15\n",
    "            elif folderName in ['R']:\n",
    "                label = 16\n",
    "            elif folderName in ['S']:\n",
    "                label = 17\n",
    "            elif folderName in ['T']:\n",
    "                label = 18\n",
    "            elif folderName in ['U']:\n",
    "                label = 19\n",
    "            elif folderName in ['V']:\n",
    "                label = 20\n",
    "            elif folderName in ['W']:\n",
    "                label = 21\n",
    "            elif folderName in ['X']:\n",
    "                label = 22\n",
    "            elif folderName in ['Y']:\n",
    "                label = 23\n",
    "                \n",
    "            for image_filename in tqdm(os.listdir(folder + folderName)):\n",
    "                img_file = cv2.imread(folder + folderName + '/' + image_filename)\n",
    "                if img_file is not None:\n",
    "                    img_file = skimage.transform.resize(img_file, (IMG_SIZE, IMG_SIZE, 3))\n",
    "                    img_arr = np.asarray(img_file)\n",
    "                    X.append(img_arr)\n",
    "                    y.append(label)\n",
    "    X = np.asarray(X)\n",
    "    y = np.asarray(y)\n",
    "    \n",
    "    return X,y\n",
    "\n",
    "X_train, y_train = get_data(train_dir) \n",
    "X_test, y_test= get_data(test_dir)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2) \n",
    "\n",
    "#hot vector labels (1=[0,1,0,0,0,0,0,0,0,0])\n",
    "y_trainHot = to_categorical(y_train, num_classes = 24)\n",
    "y_testHot = to_categorical(y_test, num_classes = 24)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "416ed5f0-5aa3-4d80-a91b-3f16b450a495",
    "_uuid": "676667e7c2b33f134ed685202b2f79bffa3b1734"
   },
   "outputs": [],
   "source": [
    "# Shuffle data for subsampling\n",
    "X_train, y_trainHot = shuffle(X_train, y_trainHot, random_state=13)\n",
    "X_test, y_testHot = shuffle(X_test, y_testHot, random_state=13)\n",
    "X_train = X_train[:30000]\n",
    "X_test = X_test[:30000]\n",
    "y_trainHot = y_trainHot[:30000]\n",
    "y_testHot = y_testHot[:30000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "acafe27f-6c13-4091-9ce0-907c5784eb79",
    "_uuid": "6384bf60c740fc5cc97c90c6bb4a170e294210b2"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\Gradprojectnumpy\\lib\\site-packages\\seaborn\\_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  FutureWarning\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'K', 10: 'L', 11: 'M', 12: 'N', 13: 'O', 14: 'P', 15: 'Q', 16: 'R', 17: 'S', 18: 'T', 19: 'U', 20: 'V', 21: 'W', 22: 'X', 23: 'Y'}\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": [
    "#labels\n",
    "map_chars = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E', 5: 'F', 6: 'G', 7: 'H', 8: 'I', 9: 'K', 10: 'L', 11: 'M', 12: 'N', 13: 'O', 14: 'P', 15: 'Q', 16: 'R', 17: 'S', 18: 'T', 19: 'U', 20: 'V', 21: 'W', 22: 'X', 23: 'Y'}\n",
    "dict_chars=map_chars\n",
    "df = pd.DataFrame()\n",
    "df[\"labels\"]=y_train\n",
    "label = df['labels']\n",
    "dist = label.value_counts()\n",
    "sb.countplot(label)\n",
    "print(dict_chars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "5c2b5fc4-e1af-4dfe-a928-8a8076c73d59",
    "_uuid": "992129dbd3c7695bdd2e2497a6a56da0227c8c0d"
   },
   "outputs": [],
   "source": [
    "#Confusion Matrix and curves\n",
    "\n",
    "class MetricsCheckpoint(Callback):\n",
    "    #Saves metrics after each epoch.\n",
    "    def __init__(self, savepath):\n",
    "        super(MetricsCheckpoint, self).__init__()\n",
    "        self.savepath = savepath\n",
    "        self.history = {}\n",
    "    def on_epoch_end(self, epoch, logs=None):\n",
    "        for k, v in logs.items():\n",
    "            self.history.setdefault(k, []).append(v)\n",
    "        np.save(self.savepath, self.history)\n",
    "\n",
    "def KerasLearningCurve():\n",
    "    plt.figure(figsize=(10,5))\n",
    "    metrics = np.load('logs.npy')[()]\n",
    "    asd = ['accuracy'] # try to add 'loss' to see the loss learning curve\n",
    "    for k in filter(lambda x : np.any([kk in x for kk in asd]), metrics.keys()):\n",
    "        l = np.array(metrics[k])\n",
    "        plt.plot(l, c= 'r' if 'val' not in k else 'b', label='val' if 'val' in k else 'train')\n",
    "        x = np.argmin(l) if 'loss' in k else np.argmax(l)\n",
    "        y = l[x]\n",
    "        plt.scatter(x,y, lw=0, alpha=0.25, s=100, c='r' if 'val' not in k else 'b')\n",
    "        plt.text(x, y, '{} = {:.4f}'.format(x,y), size='15', color= 'r' if 'val' not in k else 'b')   \n",
    "    plt.legend(loc=4)\n",
    "    plt.axis([0, None, None, None]);\n",
    "    plt.grid()\n",
    "    plt.xlabel('Number of epochs')\n",
    "    plt.ylabel('Accuracy')\n",
    "\n",
    "def confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n",
    "    #Prints confusion matrix.\n",
    "    \n",
    "    plt.figure(figsize = (8,8))\n",
    "    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    tick_marks = np.arange(len(classes))\n",
    "    plt.xticks(tick_marks, classes, rotation=90)\n",
    "    plt.yticks(tick_marks, classes)\n",
    "    thresh = cm.max() / 2.\n",
    "    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
    "        plt.text(j, i, cm[i, j],\n",
    "                 horizontalalignment=\"center\",\n",
    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "    plt.tight_layout()\n",
    "    plt.ylabel('True label')\n",
    "    plt.xlabel('Predicted label')\n",
    "\n",
    "def learning_curve(model_fit):\n",
    "    plt.figure(figsize=(8,8))\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(model_fit.model_fit['accuracy'])\n",
    "    plt.plot(model_fit.model_fit['val_accuracy'])\n",
    "    plt.title('model accuracy')\n",
    "    plt.ylabel('accuracy')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.savefig('./accuracy_curve.png')\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(model_fit.model_fit['loss'])\n",
    "    plt.plot(model_fit.model_fit['val_loss'])\n",
    "    plt.title('model loss')\n",
    "    plt.ylabel('loss')\n",
    "    plt.xlabel('epoch')\n",
    "    plt.legend(['train', 'test'], loc='upper left')\n",
    "    plt.savefig('./loss_curve.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "da473dc4-7e79-4be0-97aa-c7cca6e8aa43",
    "_uuid": "1b8d8acad18ea6c063c61c50d84c5c65f8678b21",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\envs\\Gradprojectnumpy\\lib\\site-packages\\sklearn\\utils\\validation.py:70: FutureWarning: Pass classes=[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23], y=[ 5 17 22 ...  8 19 21] as keyword args. From version 0.25 passing these as positional arguments will result in an error\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_4\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         (None, 50, 50, 3)         0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 50, 50, 64)        1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 50, 50, 64)        36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 25, 25, 64)        0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 25, 25, 128)       73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 25, 25, 128)       147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 12, 12, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 12, 12, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 12, 12, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 12, 12, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 6, 6, 256)         0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 6, 6, 512)         1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 6, 6, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 3, 3, 512)         0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 3, 3, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 3, 3, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 3, 3, 512)         2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 1, 1, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 24)                12312     \n",
      "=================================================================\n",
      "Total params: 14,727,000\n",
      "Trainable params: 12,312\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n",
      "Train on 30000 samples, validate on 14400 samples\n",
      "Epoch 1/4\n",
      "30000/30000 [==============================] - 172s 6ms/step - loss: 2.0725 - accuracy: 0.5309 - val_loss: 1.4977 - val_accuracy: 0.6897\n",
      "Epoch 2/4\n",
      "30000/30000 [==============================] - 187s 6ms/step - loss: 1.2620 - accuracy: 0.7405 - val_loss: 1.0921 - val_accuracy: 0.7794\n",
      "Epoch 3/4\n",
      "30000/30000 [==============================] - 189s 6ms/step - loss: 0.9743 - accuracy: 0.7987 - val_loss: 0.8846 - val_accuracy: 0.8150\n",
      "Epoch 4/4\n",
      "30000/30000 [==============================] - 190s 6ms/step - loss: 0.8092 - accuracy: 0.8322 - val_loss: 0.7526 - val_accuracy: 0.8449\n",
      "\n",
      "Keras CNN - accuracy: 0.8448610901832581 \n",
      "\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.71      0.88      0.79       583\n",
      "           B       0.88      0.85      0.86       592\n",
      "           C       0.98      0.98      0.98       605\n",
      "           D       0.92      0.95      0.93       544\n",
      "           E       0.89      0.75      0.82       669\n",
      "           F       0.93      0.95      0.94       607\n",
      "           G       0.95      0.87      0.91       594\n",
      "           H       0.92      0.95      0.94       585\n",
      "           I       0.84      0.88      0.86       586\n",
      "           K       0.81      0.79      0.80       595\n",
      "           L       0.94      0.95      0.94       625\n",
      "           M       0.74      0.87      0.80       608\n",
      "           N       0.95      0.70      0.80       551\n",
      "           O       0.91      0.92      0.92       593\n",
      "           P       0.94      0.95      0.95       627\n",
      "           Q       0.96      0.97      0.97       626\n",
      "           R       0.80      0.78      0.79       627\n",
      "           S       0.71      0.65      0.68       613\n",
      "           T       0.80      0.89      0.84       592\n",
      "           U       0.65      0.71      0.68       568\n",
      "           V       0.78      0.62      0.69       634\n",
      "           W       0.83      0.78      0.81       580\n",
      "           X       0.73      0.78      0.75       602\n",
      "           Y       0.81      0.87      0.84       594\n",
      "\n",
      "    accuracy                           0.84     14400\n",
      "   macro avg       0.85      0.85      0.84     14400\n",
      "weighted avg       0.85      0.84      0.84     14400\n",
      "\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Model weights with vgg16 model.\n",
    "#Optained from: 'https://github.com/fchollet/deep-learning-models/releases/download/v0.1/vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "map_chars1 = map_chars\n",
    "class_weight1 = class_weight.compute_class_weight('balanced', np.unique(y_train), y_train)\n",
    "weight_path1 = 'vgg16_weights_tf_dim_ordering_tf_kernels_notop.h5'\n",
    "pretrained_model_1 = VGG16(weights = weight_path1, include_top=False, input_shape=(IMG_SIZE, IMG_SIZE, 3))\n",
    "\n",
    "optimizer1 = keras.optimizers.Adam()\n",
    "optimizer2 = keras.optimizers.RMSprop(lr=0.0001)\n",
    "\n",
    "def pretrainedNetwork(xtrain,ytrain,xtest,ytest,pretrainedmodel,pretrainedweights,classweight,numclasses,numepochs,optimizer,labels):\n",
    "    \n",
    "    base_model = pretrained_model_1 # Topless\n",
    "    # Add top layer\n",
    "    x = base_model.output\n",
    "    x = Flatten()(x)\n",
    "    predictions = Dense(numclasses, activation='softmax')(x)\n",
    "    model = Model(inputs=base_model.input, outputs=predictions)\n",
    "    # Train top layer\n",
    "    for layer in base_model.layers:\n",
    "        layer.trainable = False\n",
    "        \n",
    "    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n",
    "    callbacks_list = [keras.callbacks.EarlyStopping(monitor='val_acc', patience=3, verbose=1)]\n",
    "    model.summary()\n",
    "    \n",
    "    # Fit model\n",
    "    model_fit = model.fit(xtrain,ytrain, epochs=numepochs, class_weight=classweight, validation_data=(xtest,ytest), verbose=1,callbacks = [MetricsCheckpoint('logs')])\n",
    "    \n",
    "    # Evaluate model\n",
    "    score = model.evaluate(xtest,ytest, verbose=0)\n",
    "    print('\\nKeras CNN - accuracy:', score[1], '\\n')\n",
    "    y_pred = model.predict(xtest)\n",
    "    print('\\n', sklearn.metrics.classification_report(np.where(ytest > 0)[1], np.argmax(y_pred, axis=1), target_names=list(labels.values())), sep='') \n",
    "    Y_pred_classes = np.argmax(y_pred,axis = 1) \n",
    "    Y_true = np.argmax(ytest,axis = 1) \n",
    "    confusion_mtx = confusion_matrix(Y_true, Y_pred_classes) \n",
    "    KerasLearningCurve()\n",
    "    plt.show()\n",
    "    learning_curve(model_fit)\n",
    "    plt.show()\n",
    "    confusion_matrix(confusion_mtx, classes = list(labels.values()))\n",
    "    plt.show()\n",
    "    return model\n",
    "\n",
    "trained_model = pretrainedNetwork(X_train, y_trainHot, X_test, y_testHot,pretrained_model_1,weight_path1,class_weight1,24,4,optimizer1,map_chars1)\n",
    "\n",
    "trained_model.save('modelfinalpls.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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