test_train_split_DenseNet-Copy1.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": 6,
   "id": "driving-insight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "/home/ubuntu/crop/male/hispanic/ronny-sison-3vzaG52ZCJY-unsplash.jpg\n",
      "/home/ubuntu/crop/female/caucasian/business-3456505_1920.jpg\n",
      "/home/ubuntu/crop/female/black/jessica-felicio-8vgkMCSq4EU-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/hunter-newton-WQEe4c757Gs-unsplash.jpg\n",
      "/home/ubuntu/crop/female/caucasian/sergey-sokolov-CbfFN5zE_sY-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/pexels-alex-green-6625960.jpg\n",
      "/home/ubuntu/crop/female/indian/indian-4755323_1920.jpg\n",
      "/home/ubuntu/crop/female/caucasian/woman-4791398_1920.jpg\n",
      "/home/ubuntu/crop/female/caucasian/girl-2663559_1920.jpg\n",
      "/home/ubuntu/crop/female/midestern/anastasia-leonova-J52-_tGlXfU-unsplash.jpg\n",
      "/home/ubuntu/crop/male/asian/sung-wang-g4DgCF90EM4-unsplash.jpg\n",
      "/home/ubuntu/crop/female/indian/youth-3983781_1920.jpg\n",
      "/home/ubuntu/crop/female/indian/ursula-spaulding-_B_UiKIAo20-unsplash.jpg\n",
      "/home/ubuntu/crop/female/caucasian/d15.jpg\n",
      "/home/ubuntu/crop/female/caucasian/teen-5096251_1920.jpg\n",
      "/home/ubuntu/crop/male/caucasian/g_3.jpg\n",
      "/home/ubuntu/crop/female/caucasian/person-man-1205800_1920.jpg\n",
      "/home/ubuntu/crop/male/asian/pexels-sultan-raimosan-6125903.jpg\n",
      "/home/ubuntu/crop/female/caucasian/brooke-cagle-iQ-CjxFHNQk-unsplash.jpg\n",
      "/home/ubuntu/crop/female/black/mehmet-turgut-kirkgoz-2rVzuyCowLI-unsplash.jpg\n",
      "/home/ubuntu/crop/female/asian/blake-cheek-3dT98ifg9J4-unsplash.jpg\n",
      "/home/ubuntu/crop/male/asian/pexels-cottonbro-5532846.jpg\n",
      "/home/ubuntu/crop/male/mideasten/ashkan-forouzani-gvjqEmux8SU-unsplash.jpg\n",
      "/home/ubuntu/crop/female/asian/asian-3752815_1920.jpg\n",
      "/home/ubuntu/crop/male/caucasian/joshua-reddekopp-lSD2Y9AydhY-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/man-3439283_1920.jpg\n",
      "/home/ubuntu/crop/female/black/emma-valerio-Hz_hQrk9hQ8-unsplash.jpg\n",
      "/home/ubuntu/crop/male/indian/dollar-gill-X0iNZ0UZIrI-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/male-4572748_1920.jpg\n",
      "/home/ubuntu/crop/male/indian/shivam-singh-DOmEd6CYP5M-unsplash.jpg\n",
      "/home/ubuntu/crop/female/hispanic/martin-lopez-mQVryuQEG_Y-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/person-700003_1920.jpg\n",
      "/home/ubuntu/crop/male/caucasian/damir-spanic-rHDK3UU7HUw-unsplash.jpg\n",
      "/home/ubuntu/crop/female/caucasian/love-2666674_1920.jpg\n",
      "/home/ubuntu/crop/male/asian/125.jpg\n",
      "/home/ubuntu/crop/female/caucasian/charisse-kenion-AZRVxIyCLxY-unsplash.jpg\n",
      "/home/ubuntu/crop/female/asian/tabitha-turner-e5ckVRrQj_8-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/ronny-sison-Ajrb9WJlVzI-unsplash.jpg\n",
      "/home/ubuntu/crop/female/black/pexels-marcelo-chagas-2535859.jpg\n",
      "/home/ubuntu/crop/female/caucasian/girl-1018414_1920.jpg\n",
      "/home/ubuntu/crop/male/indian/sammy-chandio-EvdFPEFqoxk-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/business-man-1385050_1920.jpg\n",
      "/home/ubuntu/crop/female/black/pexels-muhammadtaha-ibrahim-ma'aji-3280901.jpg\n",
      "/home/ubuntu/crop/male/asian/pexels-gustav-lundborg-2007119.jpg\n",
      "/home/ubuntu/crop/male/caucasian/joackim-weiler-lxX0dJQHV3w-unsplash.jpg\n",
      "/home/ubuntu/crop/male/asian/human-2538246_1920.jpg\n",
      "/home/ubuntu/crop/male/asian/boy-4754320_1920.jpg\n",
      "/home/ubuntu/crop/female/caucasian/hichem-dahmani-EhcwOfAn-mA-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/laura-dewilde-G2iXD2Bkt7g-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/irene-strong-v2aKnjMbP_k-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/cowboy-67630_1920.jpg\n",
      "/home/ubuntu/crop/female/caucasian/marketing-online-1427786_1920.jpg\n",
      "/home/ubuntu/crop/female/midestern/mia-cambriello-dQgKjETjqDg-unsplash.jpg\n",
      "/home/ubuntu/crop/female/hispanic/mateus-campos-felipe-ZxdJFgSGQBA-unsplash.jpg\n",
      "/home/ubuntu/crop/female/indian/pexels-mentatdgt-1306557.jpg\n",
      "/home/ubuntu/crop/male/caucasian/teenager-1131815_1280.jpg\n",
      "/home/ubuntu/crop/male/caucasian/nikita-fox-2ODe_T5WRzA-unsplash.jpg\n",
      "/home/ubuntu/crop/female/caucasian/jj-jordan-t9tNNBEPwV0-unsplash.jpg\n",
      "/home/ubuntu/crop/male/caucasian/joanna-nix-walkup-LalqRz_8QRE-unsplash.jpg\n",
      "/home/ubuntu/crop/female/asian/aodai-4603493_1920.jpg\n",
      "/home/ubuntu/crop/male/caucasian/young-4440156_1920.jpg\n",
      "/home/ubuntu/crop/female/caucasian/nathan-van-de-graaf-yomqHFm15N8-unsplash.jpg\n",
      "/home/ubuntu/crop/male/indian/harps-joseph-tAvpDE7fXgY-unsplash.jpg\n",
      "/home/ubuntu/crop/female/asian/mongolia-4538999_1920.jpg\n",
      "/home/ubuntu/crop/male/caucasian/man-3373869_1920.jpg\n",
      "/home/ubuntu/crop/male/hispanic/jack-finnigan-veB8-OSCyM4-unsplash.jpg\n",
      "/home/ubuntu/crop/female/black/engin-akyurt-Obi2AUzEZyU-unsplash.jpg\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",
    "          \n",
    "           (face,score) = line.split()\n",
    "          #if \"/female/\" in face:\n",
    "           print(face)     \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": 5,
   "id": "expensive-egypt",
   "metadata": {},
   "outputs": [],
   "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": 8,
   "id": "surrounded-charles",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-8-1dc899d91741>: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=new_model.predict_generator(test_generator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "flexible-ambassador",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "60 [4.8958793] 5.777777777777778\n",
      "61 [6.271761] 6.925\n",
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      "109 [7.1288304] 7.035460992907802\n",
      "110 [4.5528884] 5.866228070175439\n",
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      "124 [6.7331376] 7.4\n",
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      "158 [7.3598695] 7.230769230769231\n",
      "159 [6.3872685] 5.444444444444445\n",
      "160 [8.009841] 6.423076923076923\n",
      "161 [7.231212] 6.344827586206897\n",
      "162 [6.952688] 8.178571428571429\n",
      "163 [6.540279] 7.144736842105263\n",
      "164 [6.5039244] 6.879310344827586\n",
      "165 [4.424028] 3.764705882352941\n",
      "166 [5.3792977] 4.380952380952381\n",
      "167 [3.1100047] 3.333333333333333\n",
      "168 [5.2387285] 4.34433962264151\n",
      "169 [4.540526] 5.559027777777779\n",
      "170 [5.5952897] 5.392857142857143\n",
      "171 [4.0864797] 3.640350877192982\n",
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      "175 [5.825018] 7.2\n",
      "176 [5.4843297] 7.166666666666667\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "380 [5.0877395] 7.0\n",
      "381 [2.5167341] 2.793333333333334\n",
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      "493 [5.444212] 4.552287581699346\n",
      "494 [4.2377276] 3.6\n",
      "495 [6.2578697] 7.5625\n",
      "496 [5.831716] 6.473684210526316\n",
      "497 [4.7782755] 4.962962962962963\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "index 497 is out of bounds for axis 0 with size 497",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-3110ab15da0d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0;36m499\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mprediction\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 497 is out of bounds for axis 0 with size 497"
     ]
    }
   ],
   "source": [
    "i=0\n",
    "while i<499:\n",
    "    print(i+1,prediction[i], y_test[i])\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "operating-sigma",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9201421357883471, 0.7691225904253741, 0.719405255919293)"
      ]
     },
     "execution_count": 81,
     "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": 4,
   "id": "intended-dylan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: 123/assets\n"
     ]
    }
   ],
   "source": [
    "model.save('123')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "actual-preview",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "ename": "OSError",
     "evalue": "Unable to open file (file read failed: time = Wed Mar 24 15:26:53 2021\n, filename = '123', file descriptor = 44, errno = 21, error message = 'Is a directory', buf = 0x7fffbd6ff020, total read size = 8, bytes this sub-read = 8, bytes actually read = 18446744073709551615, offset = 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-9e6a1668e65a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mkeras\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mload_model\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmodel1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mload_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'123'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    490\u001b[0m                 \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp_filepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    491\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 492\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mload_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    493\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    494\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mload_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/keras/engine/saving.py\u001b[0m in \u001b[0;36mload_model\u001b[0;34m(filepath, custom_objects, compile)\u001b[0m\n\u001b[1;32m    581\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    582\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_supported_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 583\u001b[0;31m         \u001b[0;32mwith\u001b[0m \u001b[0mH5Dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mh5dict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    584\u001b[0m             \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_deserialize_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mh5dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcustom_objects\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'write'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/keras/utils/io_utils.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, path, mode)\u001b[0m\n\u001b[1;32m    189\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_is_path_instance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_file\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)\u001b[0m\n\u001b[1;32m    406\u001b[0m                 fid = make_fid(name, mode, userblock_size,\n\u001b[1;32m    407\u001b[0m                                \u001b[0mfapl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfcpl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmake_fcpl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtrack_order\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m                                swmr=swmr)\n\u001b[0m\u001b[1;32m    409\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlibver\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/h5py/_hl/files.py\u001b[0m in \u001b[0;36mmake_fid\u001b[0;34m(name, mode, userblock_size, fapl, fcpl, swmr)\u001b[0m\n\u001b[1;32m    171\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mswmr\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mswmr_support\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    172\u001b[0m             \u001b[0mflags\u001b[0m \u001b[0;34m|=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_SWMR_READ\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 173\u001b[0;31m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    174\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r+'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m         \u001b[0mfid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5f\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mACC_RDWR\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfapl\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfapl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/_objects.pyx\u001b[0m in \u001b[0;36mh5py._objects.with_phil.wrapper\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mh5py/h5f.pyx\u001b[0m in \u001b[0;36mh5py.h5f.open\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: Unable to open file (file read failed: time = Wed Mar 24 15:26:53 2021\n, filename = '123', file descriptor = 44, errno = 21, error message = 'Is a directory', buf = 0x7fffbd6ff020, total read size = 8, bytes this sub-read = 8, bytes actually read = 18446744073709551615, offset = 0)"
     ]
    }
   ],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "model1=load_model('123')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "later-envelope",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /home/ubuntu/anaconda3/envs/tensorflow2_p36/lib/python3.6/site-packages/tensorflow_core/python/ops/resource_variable_ops.py:1786: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: 124446.model/assets\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.models import save_model, load_model\n",
    "save_model(model,'124446.model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "faced-america",
   "metadata": {},
   "outputs": [],
   "source": [
    "# using model's methods\n",
    "model.save(\"my_model.h5\")\n",
    "\n",
    "# load the saved model back\n",
    "#new_model = load_model('my_model.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "opponent-authority",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import save_model, load_model\n",
    "new_model = load_model('123.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "subsequent-salvation",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "test_val_datagen = ImageDataGenerator()\n",
    "batch_size = 32\n",
    "test_generator = test_val_datagen.flow(X_test, y_test, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "horizontal-wheat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.0258965],\n",
       "       [4.96753  ],\n",
       "       [4.8958793],\n",
       "       [3.6430225],\n",
       "       [5.036416 ],\n",
       "       [4.5528884],\n",
       "       [5.191028 ],\n",
       "       [3.7399616],\n",
       "       [3.7317595],\n",
       "       [4.0546784],\n",
       "       [5.3792977],\n",
       "       [3.6357708],\n",
       "       [4.472362 ],\n",
       "       [4.9831214],\n",
       "       [4.3312874],\n",
       "       [3.3602407],\n",
       "       [4.7875466],\n",
       "       [4.4954524],\n",
       "       [4.473179 ],\n",
       "       [5.881285 ],\n",
       "       [5.075103 ],\n",
       "       [5.7372518],\n",
       "       [4.3751764],\n",
       "       [4.8490396],\n",
       "       [4.542068 ],\n",
       "       [6.0529075],\n",
       "       [3.247426 ],\n",
       "       [5.538263 ],\n",
       "       [5.410579 ],\n",
       "       [5.0418496],\n",
       "       [3.7853897],\n",
       "       [5.5664024],\n",
       "       [4.8959365]], dtype=float32)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "undefined-basketball",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
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