test_train_split_DenseNet.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "induced-church",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2518\n"
     ]
    }
   ],
   "source": [
    "#create dataset train, test, val\n",
    "import math\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import glob\n",
    "import xlrd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "\n",
    "def train_and_test_set():\n",
    "    \"\"\"\n",
    "    ECUST-FBP dataset creation\n",
    "    \"\"\"\n",
    "    \n",
    "    DATA_DIR = '/home/ubuntu/crop/'\n",
    "    LABELS_FILE = '/home/ubuntu/test5.xlsx'\n",
    "    \n",
    "    \n",
    "    df = pd.read_excel(LABELS_FILE)  \n",
    "    labels_dict= {}\n",
    "    for root, dirs, files in os.walk(DATA_DIR):\n",
    "     \n",
    "      for file in files: \n",
    "        \n",
    "        f = os.path.join(root, file)\n",
    "        df1 = df.loc[df['image']==file]\n",
    "        if (len(df1.index)==1):\n",
    "         \n",
    "           labels_dict[f] = float(df1['mean'])\n",
    "  \n",
    "    #keys =  list(labels_dict.keys())\n",
    "    l = list(labels_dict.items())\n",
    "    random.shuffle(l)\n",
    "    labels_dict = dict(l)\n",
    "    print(len(labels_dict))\n",
    "    #random.shuffle(keys)\n",
    "    #labels_dict =dict(keys)\n",
    "    d1 = dict(list(labels_dict.items())[0:1767])\n",
    "    d2 = dict(list(labels_dict.items())[1768:2268])\n",
    "    d3 = dict(list(labels_dict.items())[2268:2518])\n",
    "    return d1, d2, d3      \n",
    "\n",
    "train,test,val = train_and_test_set()\n",
    "with open('train.txt', 'w') as file:\n",
    "     for k,v in train.items():\n",
    "            file.write(str(k)+' '+str(v)+'\\n')\n",
    "\n",
    "with open('test.txt', 'w') as file2:\n",
    "     for k,v in test.items():\n",
    "            file2.write(str(k)+' '+str(v)+'\\n')\n",
    "            \n",
    "with open('val.txt', 'w') as file3:\n",
    "     for k,v in val.items():\n",
    "            file3.write(str(k)+' '+str(v)+'\\n') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "driving-insight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "/home/ubuntu/crop/female/asian/jasmin-chew-WUVozkQFLz0-unsplash.jpg 5.111111111111111\n",
      "\n",
      "/home/ubuntu/crop/male/indian/pexels-anna-tarazevich-5119607.jpg 5.888888888888889\n",
      "\n",
      "/home/ubuntu/crop/female/caucasian/woman-918583_1920.jpg 7.1\n",
      "\n",
      "/home/ubuntu/crop/female/asian/selfie-4127339_1920.jpg 4.892857142857143\n",
      "\n",
      "/home/ubuntu/crop/male/caucasian/man-67467_1920.jpg 8.012411347517732\n",
      "\n",
      "/home/ubuntu/crop/male/mideasten/katie-moum-yXfdoI66AtQ-unsplash.jpg 3.251700680272109\n",
      "\n",
      "/home/ubuntu/crop/female/midestern/pexels-fotografierende-1793059.jpg 6.875\n",
      "\n",
      "/home/ubuntu/crop/female/caucasian/cristian-newman-l9I93gZKTG4-unsplash.jpg 3.3\n",
      "\n",
      "/home/ubuntu/crop/male/hispanic/amir-esrafili-xmYL_5Hpe4U-unsplash.jpg 7.117647058823529\n",
      "\n",
      "/home/ubuntu/crop/male/caucasian/derick-mckinney-243Q39DzM3M-unsplash.jpg 7.484848484848484\n",
      "\n",
      "/home/ubuntu/crop/female/black/oladimeji-odunsi-aU_eOcelLhQ-unsplash.jpg 5.0\n",
      "\n",
      "/home/ubuntu/crop/female/asian/anthony-tran-6s3L8El33QE-unsplash.jpg 7.684210526315789\n",
      "\n",
      "/home/ubuntu/crop/female/hispanic/blake-carpenter-qWiJBa6soE0-unsplash.jpg 6.653846153846154\n",
      "\n",
      "/home/ubuntu/crop/female/caucasian/logan-weaver-QoZRUI6LdKw-unsplash.jpg 7.535714285714286\n",
      "\n",
      "/home/ubuntu/crop/female/caucasian/aircraft-976685_1920.jpg 7.489583333333333\n",
      "\n",
      "/home/ubuntu/crop/male/indian/ashkan-forouzani-Y8QiDJMEUGc-unsplash.jpg 6.558333333333334\n",
      "\n",
      "/home/ubuntu/crop/male/indian/muhammad-murtaza-ghani-ShxuPMbO1lM-unsplash.jpg 6.523809523809524\n",
      "\n",
      "/home/ubuntu/crop/female/caucasian/woman-5381567_1920.jpg 8.0\n",
      "\n",
      "/home/ubuntu/crop/male/asian/pexels-joen-nguyen-2599510.jpg 7.444444444444445\n",
      "\n",
      "/home/ubuntu/crop/male/mideasten/joshua-purnell-kz6qS9mWbAo-unsplash.jpg 5.888888888888889\n",
      "\n",
      "/home/ubuntu/crop/female/midestern/hisu-lee-NayZJyAK8vg-unsplash.jpg 6.666666666666667\n",
      "\n",
      "/home/ubuntu/crop/female/midestern/julian-florez-l5rmMuK8070-unsplash.jpg 6.714285714285714\n",
      "\n",
      "/home/ubuntu/crop/male/caucasian/joker-5485787_1920.jpg 4.0\n",
      "\n",
      "/home/ubuntu/crop/female/hispanic/2.jpg 6.1171875\n",
      "\n",
      "/home/ubuntu/crop/male/asian/man-537136_1920.jpg 4.958333333333334\n",
      "\n",
      "/home/ubuntu/crop/male/asian/old-man-971889_1920.jpg 4.333333333333333\n",
      "\n",
      "/home/ubuntu/crop/female/midestern/girl-2651718_1920.jpg 7.928571428571429\n",
      "\n",
      "(250, 224, 224, 3)\n"
     ]
    }
   ],
   "source": [
    "#create X,y \n",
    "\n",
    "import numpy as np\n",
    "import cv2\n",
    "import pandas as pd\n",
    "def create_dataset(path,target_size):\n",
    "    X = []\n",
    "    y = []\n",
    "   # df = pd.read_csv(path, sep = \"\\t\")\n",
    "   # img_files = glob.glob(DATA_DIR+'*.jpg') # glob library to get all files in path as list\n",
    "    #print(f'reading {len(img_files)} images into dataset')\n",
    "   # print(df)\n",
    "    with open(path) as f:\n",
    "       for line in f:\n",
    "          print(line)\n",
    "          (face,score) = line.split()\n",
    "          img = preprocess_image(cv2.imread(face), target_size)\n",
    "          X.append(img)\n",
    "          y.append(float(score))   \n",
    "    return np.array(X), np.array(y)\n",
    "\n",
    "\n",
    "def preprocess_image(image,target_size):\n",
    "    return cv2.resize(cv2.cvtColor(image, cv2.COLOR_BGR2RGB),target_size) / 255\n",
    "\n",
    "target_size = (224,224)\n",
    "X_train,y_train = create_dataset(\"/home/ubuntu/ME-beautydatabase/scores/train.txt\",target_size)\n",
    "X_test,y_test = create_dataset(\"/home/ubuntu/ME-beautydatabase/scores/test.txt\",target_size)\n",
    "X_val,y_val = create_dataset(\"/home/ubuntu/ME-beautydatabase/scores/val.txt\",target_size)\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "expensive-egypt",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "WARNING:tensorflow:sample_weight modes were coerced from\n",
      "  ...\n",
      "    to  \n",
      "  ['...']\n",
      "Train for 55 steps, validate for 8 steps\n",
      "Epoch 1/50\n",
      "55/55 [==============================] - 640s 12s/step - loss: 3.3491 - val_loss: 30.8923\n",
      "Epoch 2/50\n",
      "55/55 [==============================] - 598s 11s/step - loss: 1.1284 - val_loss: 2.0340\n",
      "Epoch 3/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 1.0014 - val_loss: 7.2218\n",
      "Epoch 4/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.9859 - val_loss: 2.6790\n",
      "Epoch 5/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.9488 - val_loss: 1.0543\n",
      "Epoch 6/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.9219 - val_loss: 1.4077\n",
      "Epoch 7/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.8759 - val_loss: 1.0563\n",
      "Epoch 8/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.8652 - val_loss: 2.0327\n",
      "Epoch 9/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.8854 - val_loss: 1.2945\n",
      "Epoch 10/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.8253 - val_loss: 1.3391\n",
      "Epoch 11/50\n",
      "55/55 [==============================] - 603s 11s/step - loss: 0.7913 - val_loss: 1.6876\n",
      "Epoch 12/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.8245 - val_loss: 1.2656\n",
      "Epoch 13/50\n",
      "55/55 [==============================] - 602s 11s/step - loss: 0.7950 - val_loss: 1.0908\n",
      "Epoch 14/50\n",
      "55/55 [==============================] - 602s 11s/step - loss: 0.7309 - val_loss: 1.2846\n",
      "Epoch 15/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.7598 - val_loss: 1.3256\n",
      "Epoch 16/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.7353 - val_loss: 0.9850\n",
      "Epoch 17/50\n",
      "55/55 [==============================] - 598s 11s/step - loss: 0.7218 - val_loss: 1.8060\n",
      "Epoch 18/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.7047 - val_loss: 1.4290\n",
      "Epoch 19/50\n",
      "55/55 [==============================] - 602s 11s/step - loss: 0.6921 - val_loss: 1.1537\n",
      "Epoch 20/50\n",
      "55/55 [==============================] - 602s 11s/step - loss: 0.7037 - val_loss: 1.8380\n",
      "Epoch 21/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.6629 - val_loss: 0.9698\n",
      "Epoch 22/50\n",
      "55/55 [==============================] - 613s 11s/step - loss: 0.6115 - val_loss: 1.3748\n",
      "Epoch 23/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.6145 - val_loss: 1.2001\n",
      "Epoch 24/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.5853 - val_loss: 0.7629\n",
      "Epoch 25/50\n",
      "55/55 [==============================] - 608s 11s/step - loss: 0.6000 - val_loss: 1.7850\n",
      "Epoch 26/50\n",
      "55/55 [==============================] - 604s 11s/step - loss: 0.5802 - val_loss: 0.9896\n",
      "Epoch 27/50\n",
      "55/55 [==============================] - 602s 11s/step - loss: 0.5578 - val_loss: 0.9836\n",
      "Epoch 28/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.5553 - val_loss: 1.2209\n",
      "Epoch 29/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.5289 - val_loss: 0.9271\n",
      "Epoch 30/50\n",
      "55/55 [==============================] - 602s 11s/step - loss: 0.5453 - val_loss: 1.0684\n",
      "Epoch 31/50\n",
      "55/55 [==============================] - 606s 11s/step - loss: 0.5177 - val_loss: 1.1611\n",
      "Epoch 32/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.4842 - val_loss: 1.0075\n",
      "Epoch 33/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.4965 - val_loss: 1.1039\n",
      "Epoch 34/50\n",
      "55/55 [==============================] - 599s 11s/step - loss: 0.4532 - val_loss: 2.4371\n",
      "Epoch 35/50\n",
      "55/55 [==============================] - 603s 11s/step - loss: 0.4631 - val_loss: 1.0005\n",
      "Epoch 36/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.4663 - val_loss: 3.8621\n",
      "Epoch 37/50\n",
      "55/55 [==============================] - 603s 11s/step - loss: 0.4391 - val_loss: 1.3365\n",
      "Epoch 38/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.4285 - val_loss: 1.0537\n",
      "Epoch 39/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.4362 - val_loss: 1.0891\n",
      "Epoch 40/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.3946 - val_loss: 0.8285\n",
      "Epoch 41/50\n",
      "55/55 [==============================] - 604s 11s/step - loss: 0.4339 - val_loss: 1.4125\n",
      "Epoch 42/50\n",
      "55/55 [==============================] - 609s 11s/step - loss: 0.4158 - val_loss: 1.2307\n",
      "Epoch 43/50\n",
      "55/55 [==============================] - 604s 11s/step - loss: 0.3658 - val_loss: 0.9178\n",
      "Epoch 44/50\n",
      "55/55 [==============================] - 604s 11s/step - loss: 0.3365 - val_loss: 1.0225\n",
      "Epoch 45/50\n",
      "55/55 [==============================] - 601s 11s/step - loss: 0.3527 - val_loss: 0.9927\n",
      "Epoch 46/50\n",
      "55/55 [==============================] - 604s 11s/step - loss: 0.3622 - val_loss: 1.4548\n",
      "Epoch 47/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.3387 - val_loss: 1.0391\n",
      "Epoch 48/50\n",
      "55/55 [==============================] - 600s 11s/step - loss: 0.3571 - val_loss: 1.1196\n",
      "Epoch 49/50\n",
      "55/55 [==============================] - 604s 11s/step - loss: 0.3219 - val_loss: 1.1818\n",
      "Epoch 50/50\n",
      "55/55 [==============================] - 605s 11s/step - loss: 0.2848 - val_loss: 0.9008\n"
     ]
    }
   ],
   "source": [
    "#DenseNet\n",
    "\n",
    "import os\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "from tensorflow.keras import Sequential, Model, Input\n",
    "from tensorflow.keras.layers import Dense\n",
    "from tensorflow.keras.applications import DenseNet201\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.utils import multi_gpu_model\n",
    "from tensorflow.keras.models import load_model\n",
    "import tensorflow.keras.backend as K\n",
    "\n",
    "basemodel = DenseNet201(include_top=False, pooling='avg', weights='imagenet')\n",
    "\n",
    "\n",
    "#X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.30, random_state=42)\n",
    "#X_val, X_test, y_val, y_test = train_test_split(X_val, y_val, test_size=1/3, random_state=42)\n",
    "\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "test_val_datagen = ImageDataGenerator()\n",
    "train_datagen = ImageDataGenerator(horizontal_flip=True,\n",
    "                                   rotation_range=40,\n",
    "                                   width_shift_range=0.2,\n",
    "                                   height_shift_range=0.2,\n",
    "                                   shear_range=0.2,\n",
    "                                   zoom_range=0.2)\n",
    "\n",
    "batch_size = 32\n",
    "train_generator = train_datagen.flow(X_train, y_train, batch_size=batch_size)\n",
    "val_generator = test_val_datagen.flow(X_val, y_val, batch_size=batch_size)\n",
    "test_generator = test_val_datagen.flow(X_test, y_test, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "for layer in basemodel.layers[:100]:\n",
    "    layer.Trainable = False\n",
    "\n",
    "model = Sequential()\n",
    "model.add(basemodel)\n",
    "model.add(Dense(1))\n",
    "epochs = 50\n",
    "lr=0.001\n",
    "#model.layers[0].trainable = False\n",
    "model.compile(loss='mean_squared_error', optimizer=Adam(learning_rate=lr))\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    epochs=epochs,\n",
    "    steps_per_epoch=len(y_train) // batch_size,\n",
    "    validation_data=val_generator,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "surrounded-charles",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-5-4eafd1643ada>:1: Model.predict_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.predict, which supports generators.\n"
     ]
    }
   ],
   "source": [
    "prediction=model.predict_generator(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "operating-sigma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8750043316213281, 0.7540868070156527, 0.7218692172026)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "RMSE = mean_squared_error(y_test, prediction)\n",
    "MAE = mean_absolute_error(y_test, prediction)\n",
    "from scipy.stats import pearsonr\n",
    "corr,t = pearsonr (y_test.flatten(),prediction.flatten())\n",
    "RMSE,MAE,corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "intended-dylan",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load('123.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dental-newfoundland",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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