second_stage_preparation_with_threshold_plot_8ensemble_xxx.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6cb07693",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import random\n",
    "import warnings\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import cv2\n",
    "\n",
    "from tqdm import tqdm_notebook, tnrange\n",
    "from itertools import chain\n",
    "from skimage.io import imread, imshow, concatenate_images\n",
    "from skimage.transform import resize\n",
    "from skimage.morphology import label\n",
    "\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "from keras.layers.core import Lambda\n",
    "from keras.layers.convolutional import Conv2D, Conv2DTranspose\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint,ReduceLROnPlateau\n",
    "from keras import backend as K\n",
    "#!pip install segmentation-models\n",
    "import tensorflow as tf\n",
    "\n",
    "from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97a32ff0",
   "metadata": {},
   "source": [
    "### added 100 gigabytes of memory limit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c95bab1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"stage1/X_train\", \"rb\") as f:\n",
    "    X_train=pickle.load( f)\n",
    "with open(\"stage1/X_val\", \"rb\") as f:\n",
    "    X_val=pickle.load( f)\n",
    "with open(\"stage1/y_train\", \"rb\") as f:\n",
    "    y_train=pickle.load( f)\n",
    "with open(\"stage1/y_val\", \"rb\") as f:\n",
    "    y_val=pickle.load(f)\n",
    "with open(\"preds_test_8ensemble\", \"rb\") as f:\n",
    "    preds_test=pickle.load(f)    \n",
    "#with open(\"stage1/X_test\", \"rb\") as f:\n",
    "    #X_test=pickle.load(f)    \n",
    "#preds_test=preds_test/8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91c1ed78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "12b89364",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-3-b4647d3bd7b7>:13: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n"
     ]
    },
    {
     "data": {
      "application/json": {
       "ascii": false,
       "bar_format": null,
       "colour": null,
       "elapsed": 0.01564955711364746,
       "initial": 0,
       "n": 0,
       "ncols": null,
       "nrows": null,
       "postfix": null,
       "prefix": "",
       "rate": null,
       "total": 18000,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "213b5874b510464286e9d07e214f1abd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/18000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "im_width = 128\n",
    "im_height = 128\n",
    "im_chan = 1\n",
    "test_ids=[]\n",
    "path_test = r'C:\\\\Users\\diego\\OneDrive\\Desktop\\DS\\seismic_segmentation\\test'\n",
    "for i in os.listdir(os.path.join(path_test,\"images\")):\n",
    "    test_ids.append(i)    \n",
    "# Get and resize test images\n",
    "X_test = np.zeros((len(test_ids), im_height, im_width, im_chan), dtype=np.uint8)\n",
    "sizes_test = []\n",
    "print('Getting and resizing test images ... ')\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n",
    "    path = path_test\n",
    "    img = load_img(path + '/images/' + id_)\n",
    "    x = img_to_array(img)[:,:,1]\n",
    "    sizes_test.append([x.shape[0], x.shape[1]])\n",
    "    x = resize(x, (128, 128, 1), mode='constant', preserve_range=True)\n",
    "    X_test[n] = x\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ad397ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6b73463b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "    \u001b[1;31m[... skipping hidden 1 frame]\u001b[0m\n",
      "\u001b[1;32m<ipython-input-14-2ef1d12485f0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     18\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mconfident_X_test1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mconfident_X_test1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconfident_images\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpreds_test\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1900\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-14-2ef1d12485f0>\u001b[0m in \u001b[0;36mconfident_images\u001b[1;34m(X_test, preds_test, threshold)\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpreds_test\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m             \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfidence\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mconfident_X_test1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mconfident_y_test1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mwrite\u001b[1;34m(self, string)\u001b[0m\n\u001b[0;32m    403\u001b[0m             \u001b[1;31m# only touch the buffer in the IO thread to avoid races\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 404\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpub_thread\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschedule\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstring\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    405\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_child\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\ipykernel\\iostream.py\u001b[0m in \u001b[0;36mschedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m    204\u001b[0m             \u001b[1;31m# wake event thread (message content is ignored)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 205\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_event_pipe\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mb''\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    206\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\zmq\\sugar\\socket.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m    490\u001b[0m             \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgroup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mSocket\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mflags\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mflags\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    492\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mzmq/backend/cython/socket.pyx\u001b[0m in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\envs\\env1_cloned_tf\\lib\\site-packages\\zmq\\backend\\cython\\checkrc.pxd\u001b[0m in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: ",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "'''\n",
    "def confident_images(X_test, preds_test,threshold=1900):\n",
    "    #X_test=X_test.tolist()\n",
    "    #preds_test=preds_test.tolist()\n",
    "    confident_X_test1=[]\n",
    "    confident_y_test1=[]\n",
    "    def BinaryCrossEntropy(y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "        term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "        term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "        return -np.mean(term_0+term_1, axis=0)\n",
    "    for i,elem in enumerate(preds_test):\n",
    "        confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                      ,preds_test[i].flatten())\n",
    "        if confidence>=threshold:\n",
    "            confident_X_test1.append(X_test[i].tolist())\n",
    "            confident_y_test1.append(preds_test[i].tolist())\n",
    "            print(confidence)\n",
    "    return confident_X_test1,confident_y_test1\n",
    "confident_X_test1,confident_y_test1=confident_images(X_test, preds_test,threshold=1900)'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "224ce275",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        [[[0.15367094],\n",
       "          [0.08783691],\n",
       "          [0.07364161],\n",
       "          ...,\n",
       "          [0.06103715],\n",
       "          [0.08479187],\n",
       "          [0.17096094]],\n",
       " \n",
       "         [[0.07741006],\n",
       "          [0.03338425],\n",
       "          [0.03070321],\n",
       "          ...,\n",
       "          [0.02785635],\n",
       "          [0.03399269],\n",
       "          [0.08751913]],\n",
       " \n",
       "         [[0.06354789],\n",
       "          [0.02425988],\n",
       "          [0.02707133],\n",
       "          ...,\n",
       "          [0.0278316 ],\n",
       "          [0.03434233],\n",
       "          [0.07382488]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.05518735],\n",
       "          [0.02231076],\n",
       "          [0.02006663],\n",
       "          ...,\n",
       "          [0.02592955],\n",
       "          [0.02889075],\n",
       "          [0.07457046]],\n",
       " \n",
       "         [[0.08089829],\n",
       "          [0.03146153],\n",
       "          [0.02213553],\n",
       "          ...,\n",
       "          [0.03089258],\n",
       "          [0.03338139],\n",
       "          [0.09061517]],\n",
       " \n",
       "         [[0.17896758],\n",
       "          [0.08038741],\n",
       "          [0.05743518],\n",
       "          ...,\n",
       "          [0.06872702],\n",
       "          [0.07999385],\n",
       "          [0.15787953]]],\n",
       " \n",
       " \n",
       "        ...,\n",
       " \n",
       " \n",
       "        [[[0.13235444],\n",
       "          [0.07057228],\n",
       "          [0.060172  ],\n",
       "          ...,\n",
       "          [0.04447305],\n",
       "          [0.05879083],\n",
       "          [0.13362062]],\n",
       " \n",
       "         [[0.06211763],\n",
       "          [0.02361135],\n",
       "          [0.02220295],\n",
       "          ...,\n",
       "          [0.01245202],\n",
       "          [0.01507732],\n",
       "          [0.05751184]],\n",
       " \n",
       "         [[0.04765025],\n",
       "          [0.01463218],\n",
       "          [0.01539921],\n",
       "          ...,\n",
       "          [0.00911013],\n",
       "          [0.00996229],\n",
       "          [0.04098514]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.85304364],\n",
       "          [0.93790572],\n",
       "          [0.96507642],\n",
       "          ...,\n",
       "          [0.03089425],\n",
       "          [0.03501814],\n",
       "          [0.0837126 ]],\n",
       " \n",
       "         [[0.77512523],\n",
       "          [0.90510564],\n",
       "          [0.93986892],\n",
       "          ...,\n",
       "          [0.03416885],\n",
       "          [0.03936854],\n",
       "          [0.0994188 ]],\n",
       " \n",
       "         [[0.67650454],\n",
       "          [0.81940272],\n",
       "          [0.87394334],\n",
       "          ...,\n",
       "          [0.07624412],\n",
       "          [0.08464613],\n",
       "          [0.16449532]]],\n",
       " \n",
       " \n",
       "        [[[0.26771497],\n",
       "          [0.22003955],\n",
       "          [0.25479421],\n",
       "          ...,\n",
       "          [0.50226959],\n",
       "          [0.44642373],\n",
       "          [0.42821242]],\n",
       " \n",
       "         [[0.20424077],\n",
       "          [0.17397954],\n",
       "          [0.22728165],\n",
       "          ...,\n",
       "          [0.54734733],\n",
       "          [0.47839942],\n",
       "          [0.4340199 ]],\n",
       " \n",
       "         [[0.22190968],\n",
       "          [0.18649495],\n",
       "          [0.22862356],\n",
       "          ...,\n",
       "          [0.58542186],\n",
       "          [0.5465465 ],\n",
       "          [0.49851387]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.06358859],\n",
       "          [0.03699582],\n",
       "          [0.02784219],\n",
       "          ...,\n",
       "          [0.10572692],\n",
       "          [0.10180446],\n",
       "          [0.1443044 ]],\n",
       " \n",
       "         [[0.08718732],\n",
       "          [0.03517069],\n",
       "          [0.0252047 ],\n",
       "          ...,\n",
       "          [0.09101118],\n",
       "          [0.08937257],\n",
       "          [0.14590965]],\n",
       " \n",
       "         [[0.20037591],\n",
       "          [0.09927584],\n",
       "          [0.07033972],\n",
       "          ...,\n",
       "          [0.14839024],\n",
       "          [0.14054031],\n",
       "          [0.20147124]]],\n",
       " \n",
       " \n",
       "        [[[0.70231529],\n",
       "          [0.81086766],\n",
       "          [0.8924841 ],\n",
       "          ...,\n",
       "          [0.18260552],\n",
       "          [0.19398985],\n",
       "          [0.26261623]],\n",
       " \n",
       "         [[0.80648885],\n",
       "          [0.90793924],\n",
       "          [0.95400635],\n",
       "          ...,\n",
       "          [0.13750979],\n",
       "          [0.13645199],\n",
       "          [0.19874132]],\n",
       " \n",
       "         [[0.87626957],\n",
       "          [0.9435351 ],\n",
       "          [0.9709351 ],\n",
       "          ...,\n",
       "          [0.12900424],\n",
       "          [0.1471703 ],\n",
       "          [0.2014595 ]],\n",
       " \n",
       "         ...,\n",
       " \n",
       "         [[0.08290443],\n",
       "          [0.04029538],\n",
       "          [0.03774004],\n",
       "          ...,\n",
       "          [0.01499378],\n",
       "          [0.01875022],\n",
       "          [0.05920242]],\n",
       " \n",
       "         [[0.11679711],\n",
       "          [0.04906406],\n",
       "          [0.03333148],\n",
       "          ...,\n",
       "          [0.01888432],\n",
       "          [0.02549528],\n",
       "          [0.07821638]],\n",
       " \n",
       "         [[0.21490501],\n",
       "          [0.1093847 ],\n",
       "          [0.08141428],\n",
       "          ...,\n",
       "          [0.05189187],\n",
       "          [0.06436097],\n",
       "          [0.14080294]]]]))"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_true= np.where(preds_test >= 0.500, 1, 0)\n",
    "y_true,preds_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "83e01180",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 128, 128, 1)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cf2dec68",
   "metadata": {},
   "outputs": [],
   "source": [
    "thresholds=[y for y in range (950,2000,30)]\n",
    "#thresholds=[1900,2000]\n",
    "nimages=[]\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "        y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "        term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "        term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "        return -np.mean(term_0+term_1, axis=0)\n",
    "for j in thresholds:\n",
    "    k=0\n",
    "    for i,elem in enumerate(preds_test):\n",
    "        confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                      ,y_true[i].flatten())\n",
    "        #print(confidence)\n",
    "        if confidence>=j:         \n",
    "            k+=1\n",
    "            #print(k)\n",
    "    nimages.append(k)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e86e6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6920bf3b",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "#nimages\n",
    "\n",
    "plt.title('Number of images by threshold',fontsize=14,y=1.03 )\n",
    "plt.grid( True )    \n",
    "plt.plot(nimages,thresholds)\n",
    "plt.xlabel('Number of Images',labelpad=7,fontsize=10)    \n",
    "plt.ylabel('Thresholds',labelpad=7,fontsize=10)   \n",
    "#plt.gca().set_ylim(0,1)\n",
    "plt.savefig(os.path.join('plots/','Number_of_images_by_threshold_8_ensemble.png'), dpi=500, bbox_inches='tight',facecolor=(1,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ae13b933",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{950: 17510,\n",
       " 980: 17495,\n",
       " 1010: 17483,\n",
       " 1040: 17464,\n",
       " 1070: 17441,\n",
       " 1100: 17410,\n",
       " 1130: 17388,\n",
       " 1160: 17358,\n",
       " 1190: 17331,\n",
       " 1220: 17302,\n",
       " 1250: 17245,\n",
       " 1280: 17194,\n",
       " 1310: 17121,\n",
       " 1340: 17035,\n",
       " 1370: 16954,\n",
       " 1400: 16863,\n",
       " 1430: 16769,\n",
       " 1460: 16660,\n",
       " 1490: 16525,\n",
       " 1520: 16376,\n",
       " 1550: 16195,\n",
       " 1580: 16009,\n",
       " 1610: 15791,\n",
       " 1640: 15553,\n",
       " 1670: 15246,\n",
       " 1700: 14843,\n",
       " 1730: 14438,\n",
       " 1760: 13955,\n",
       " 1790: 13374,\n",
       " 1820: 12702,\n",
       " 1850: 11972,\n",
       " 1880: 11154,\n",
       " 1910: 10274,\n",
       " 1940: 9188,\n",
       " 1970: 7880}"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "thresholds_images = {thresholds[i]: nimages[i] for i in range(len(nimages))}\n",
    "thresholds_images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "d3e4d7ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1472.6359\n",
      "1634.9846\n",
      "1308.5106\n",
      "1522.6438\n",
      "1462.9843\n",
      "1964.0485\n",
      "1922.512\n",
      "1368.7544\n",
      "1903.526\n",
      "1593.7562\n",
      "1644.2834\n",
      "1805.2688\n",
      "2010.0925\n",
      "1636.3228\n",
      "1350.6238\n",
      "1786.3478\n",
      "1920.6299\n",
      "1337.1305\n",
      "1544.1599\n",
      "1665.7168\n",
      "1902.6768\n",
      "1681.6045\n",
      "1927.9142\n",
      "1459.2766\n",
      "1736.9176\n",
      "1848.068\n",
      "1661.8872\n",
      "1402.6268\n",
      "1602.0759\n",
      "1535.445\n",
      "1873.4133\n",
      "1895.5964\n",
      "1947.8939\n",
      "1298.9797\n",
      "1796.1855\n",
      "1744.6359\n",
      "1382.9524\n",
      "1823.7787\n",
      "1899.9674\n",
      "1520.8308\n",
      "1696.0917\n",
      "1725.5372\n",
      "1282.567\n",
      "1790.5936\n",
      "1692.255\n",
      "1854.1204\n",
      "1438.7705\n",
      "1355.1785\n",
      "2065.3633\n",
      "1689.6796\n",
      "1894.7637\n",
      "1647.4738\n",
      "1758.6621\n",
      "2166.0366\n",
      "1848.3523\n",
      "1338.6316\n",
      "1936.2699\n",
      "1881.621\n",
      "1497.3165\n",
      "1366.8746\n",
      "1562.4729\n",
      "1408.8239\n",
      "1964.1313\n",
      "2134.8474\n",
      "1642.0396\n",
      "2248.5688\n",
      "2057.1152\n",
      "1499.8087\n",
      "2188.3584\n",
      "1505.8694\n",
      "1767.2942\n",
      "1421.3064\n",
      "2377.1108\n",
      "1985.593\n",
      "1488.9026\n",
      "1272.2872\n",
      "2197.579\n",
      "1472.5928\n",
      "1854.7081\n",
      "1755.1312\n",
      "1996.3522\n",
      "1569.9331\n",
      "1844.1974\n",
      "1266.2795\n",
      "2277.9531\n",
      "1466.4436\n",
      "1998.0907\n",
      "1992.3853\n",
      "1517.6035\n",
      "1424.9762\n",
      "1641.6306\n",
      "1451.6113\n",
      "1266.1382\n",
      "1784.699\n",
      "1483.4814\n",
      "1808.8999\n",
      "1554.3936\n",
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     ]
    }
   ],
   "source": [
    "#confident_X_test1=np.array([])\n",
    "confident_y_test1=np.array([])\n",
    "threshold=1250\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "    term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "    return -np.mean(term_0+term_1, axis=0)\n",
    "for i,elem in enumerate(preds_test):\n",
    "    confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                  ,preds_test[i].flatten())\n",
    "    if confidence>=threshold:\n",
    "        #confident_X_test1=np.append(confident_X_test1,X_test[i])\n",
    "        confident_y_test1=np.append(confident_y_test1,preds_test[i])\n",
    "        print(confidence)\n",
    "#confident_X_test1,confident_y_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "bd90363b",
   "metadata": {},
   "outputs": [],
   "source": [
    "confident_X_test1=np.array([])\n",
    "#confident_y_test1=np.array([])\n",
    "threshold=1250\n",
    "def BinaryCrossEntropy(y_true, y_pred):\n",
    "    y_pred = np.clip(y_pred, 1e-7, 1 - 1e-7)\n",
    "    term_0 = (1-y_true) * np.log(1-y_pred + 1e-7)\n",
    "    term_1 = y_true * np.log(y_pred + 1e-7)\n",
    "    return -np.mean(term_0+term_1, axis=0)\n",
    "for i,elem in enumerate(preds_test):\n",
    "    confidence=BinaryCrossEntropy(X_test[i].flatten()\n",
    "                                  ,preds_test[i].flatten())\n",
    "    if confidence>=threshold:\n",
    "        confident_X_test1=np.append(confident_X_test1,X_test[i])\n",
    "        #confident_y_test1=np.append(confident_y_test1,preds_test[i])\n",
    "        #print(confidence)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "17192e01",
   "metadata": {},
   "outputs": [],
   "source": [
    "#confident_X_test1_1250=pred_test.reshape((-1,128,128,1))\n",
    "#confident_y_test1_1250=confident_y_test1.reshape((-1,128,128,1))\n",
    "#with open(\"confident_X_test1_1250\", \"wb\") as f:\n",
    "  #  pickle.dump(confident_X_test1_1250, f)\n",
    "#with open(\"confident_y_test1_1250\", \"wb\") as f:\n",
    "   # pickle.dump(confident_y_test1_1250, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b08a8b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "2a7a1d28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14551, 128, 128, 1)"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confident_X_test1_1250.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "a96338d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3541, 128, 128, 1)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confident_X_test1_1900.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "defa29e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.001, 0.   , 0.   , ..., 0.   , 0.   , 0.001])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confident_y_test1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "ea3f4b92",
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       "        ...,\n",
       "\n",
       "        [[0.   ],\n",
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       "         ...,\n",
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       "\n",
       "        [[0.   ],\n",
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       "         [0.   ],\n",
       "         ...,\n",
       "         [0.   ],\n",
       "         [0.   ],\n",
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       "\n",
       "        [[0.001],\n",
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       "         ...,\n",
       "         [0.   ],\n",
       "         [0.   ],\n",
       "         [0.001]]]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confident_y_test1_1700"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "41b48af5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#[confident_y_test1_1750 >= 0.500]= True\n",
    "#confident_y_test1_1750[confident_y_test1_1750 < 0.500]= False\n",
    "pseudolabels_ensemble = np.where(preds_test >= 0.500, True, False)\n",
    "#pseudolabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "b7c786ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#confident_X_test1_1250=confident_X_test1_1250.astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "23cae706",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install albumentations\n",
    "from albumentations import (HorizontalFlip, ShiftScaleRotate, RandomContrast, RandomBrightness, Compose,Rotate)\n",
    "import albumentations as A\n",
    "import cv2\n",
    "\n",
    "transform = A.Compose([\n",
    "        HorizontalFlip(p=0.5),\n",
    "        #Rotate(limit=15,p=0.5)\n",
    "        #RandomBrightness(p=0.2, limit=0.2),\n",
    "        #RandomContrast(p=0.1, limit=0.2),\n",
    "        #ShiftScaleRotate(shift_limit=0.07, scale_limit=0.1, rotate_limit=15, p=0.5)\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e677543f",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "X_train_round2=np.concatenate((X_train,X_val,X_test))\n",
    "y_train_round2=np.concatenate((y_train,y_val,pseudolabels_ensemble))\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train2,X_val2,y_train2,y_val2 = train_test_split(X_train_round2,y_train_round2, test_size=0.1)\n",
    "\n",
    "\n",
    "transformed2 = transform(image=np.squeeze(X_train2), \n",
    "                        mask=np.squeeze(y_train2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a5e0fa02",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"X_train2\", \"wb\") as f:\n",
    "    pickle.dump(X_train2, f)\n",
    "with open(\"X_val2\", \"wb\") as f:\n",
    "    pickle.dump(X_val2, f)\n",
    "with open(\"y_train2\", \"wb\") as f:\n",
    "    pickle.dump(y_train2, f)\n",
    "with open(\"y_val2\", \"wb\") as f:\n",
    "    pickle.dump(y_val2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "b4f60d97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11355, 128, 128, 1)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.concatenate((X_train,X_val,confident_X_test1_1700)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "f92f3465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11355, 128, 128, 1)"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#np.concatenate((y_train,y_val,pseudolabels_1700)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "890d76b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install segmentation-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "062eb72f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Intersection_over_Union(y_true, y_pred, threshold=0.5):\n",
    "    y_pred = K.squeeze(tf.cast(y_pred > threshold,tf.int32), -1)\n",
    "    y_true = K.cast(y_true[..., 0], K.floatx())\n",
    "    y_pred = K.cast(y_pred, K.floatx())\n",
    "    truth_areas = K.sum(y_true, axis=[1, 2])\n",
    "    pred_areas = K.sum(y_pred, axis=[1, 2])\n",
    "    intersection = K.sum(y_true * y_pred, axis=[1, 2])\n",
    "    union = K.clip(truth_areas + pred_areas - intersection, 1e-9, 128 * 128)\n",
    "    check = K.map_fn(lambda x: K.equal(x, 0), truth_areas + pred_areas, dtype=tf.bool) #images with no predicted and true salt bodies\n",
    "    p = intersection / union #ratio for each image\n",
    "    iou = K.switch(check, p + 1., p) # put 1 for images where no salt bodies were found \n",
    "\n",
    "    prec = K.map_fn(lambda x: K.mean(K.greater(x, np.arange(0.5, 1.0, 0.05))), iou, dtype=tf.float32) \n",
    "    #does each image have a iou greater than threshold? if so, the image iou is used to calculate the mean\n",
    "    prec_iou = K.mean(prec)\n",
    "    return prec_iou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6bd951dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_7\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_4 (InputLayer)         [(None, None, None, 1)]   0         \n",
      "_________________________________________________________________\n",
      "tf.math.truediv_3 (TFOpLambd (None, None, None, 1)     0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, None, None, 3)     6         \n",
      "_________________________________________________________________\n",
      "model_7 (Functional)         (None, None, None, 1)     24456154  \n",
      "=================================================================\n",
      "Total params: 24,456,160\n",
      "Trainable params: 24,438,810\n",
      "Non-trainable params: 17,350\n",
      "_________________________________________________________________\n",
      "Epoch 1/25\n",
      "1320/1320 [==============================] - 158s 116ms/step - loss: 0.2069 - Intersection_over_Union: 0.6309 - val_loss: 0.6329 - val_Intersection_over_Union: 0.4795\n",
      "Epoch 2/25\n",
      "1320/1320 [==============================] - 155s 118ms/step - loss: 0.0993 - Intersection_over_Union: 0.7932 - val_loss: 0.0695 - val_Intersection_over_Union: 0.8204\n",
      "Epoch 3/25\n",
      "1320/1320 [==============================] - 155s 117ms/step - loss: 0.0765 - Intersection_over_Union: 0.8217 - val_loss: 0.0673 - val_Intersection_over_Union: 0.8212\n",
      "Epoch 4/25\n",
      "1320/1320 [==============================] - 154s 117ms/step - loss: 0.0652 - Intersection_over_Union: 0.8368 - val_loss: 0.0668 - val_Intersection_over_Union: 0.8328\n",
      "Epoch 5/25\n",
      "1320/1320 [==============================] - 154s 117ms/step - loss: 0.0566 - Intersection_over_Union: 0.8465 - val_loss: 0.0540 - val_Intersection_over_Union: 0.8444\n",
      "Epoch 6/25\n",
      "1320/1320 [==============================] - 154s 117ms/step - loss: 0.0506 - Intersection_over_Union: 0.8546 - val_loss: 0.0617 - val_Intersection_over_Union: 0.8295\n",
      "Epoch 7/25\n",
      "1320/1320 [==============================] - 154s 117ms/step - loss: 0.0424 - Intersection_over_Union: 0.8646 - val_loss: 0.0599 - val_Intersection_over_Union: 0.8378\n",
      "Epoch 8/25\n",
      "1320/1320 [==============================] - 434s 329ms/step - loss: 0.0416 - Intersection_over_Union: 0.8692 - val_loss: 0.0559 - val_Intersection_over_Union: 0.8460\n",
      "Epoch 9/25\n",
      "1320/1320 [==============================] - 457s 346ms/step - loss: 0.0371 - Intersection_over_Union: 0.8744 - val_loss: 0.0571 - val_Intersection_over_Union: 0.8483\n",
      "Epoch 10/25\n",
      "1320/1320 [==============================] - 457s 346ms/step - loss: 0.0369 - Intersection_over_Union: 0.8751 - val_loss: 0.0545 - val_Intersection_over_Union: 0.8468\n",
      "Epoch 11/25\n",
      "1320/1320 [==============================] - 298s 226ms/step - loss: 0.0329 - Intersection_over_Union: 0.8836 - val_loss: 0.0520 - val_Intersection_over_Union: 0.8451\n",
      "Epoch 12/25\n",
      "1320/1320 [==============================] - 151s 114ms/step - loss: 0.0284 - Intersection_over_Union: 0.8892 - val_loss: 0.0539 - val_Intersection_over_Union: 0.8520\n",
      "Epoch 13/25\n",
      "1320/1320 [==============================] - 152s 115ms/step - loss: 0.0270 - Intersection_over_Union: 0.8938 - val_loss: 0.0561 - val_Intersection_over_Union: 0.8407\n",
      "Epoch 14/25\n",
      "1320/1320 [==============================] - 153s 116ms/step - loss: 0.0251 - Intersection_over_Union: 0.8954 - val_loss: 0.0557 - val_Intersection_over_Union: 0.8509\n",
      "Epoch 15/25\n",
      "1320/1320 [==============================] - 153s 116ms/step - loss: 0.0234 - Intersection_over_Union: 0.9007 - val_loss: 0.0559 - val_Intersection_over_Union: 0.8480\n",
      "Epoch 16/25\n",
      "1320/1320 [==============================] - 152s 116ms/step - loss: 0.0248 - Intersection_over_Union: 0.9000 - val_loss: 0.0579 - val_Intersection_over_Union: 0.8372\n",
      "Epoch 17/25\n",
      "1320/1320 [==============================] - 153s 116ms/step - loss: 0.0203 - Intersection_over_Union: 0.9128 - val_loss: 0.0571 - val_Intersection_over_Union: 0.8460\n",
      "Restoring model weights from the end of the best epoch.\n",
      "Epoch 00017: early stopping\n"
     ]
    }
   ],
   "source": [
    "# tf.float32\n",
    "#tf.keras.clear_session()\n",
    "tf.random.set_seed(90)\n",
    "#\n",
    "#import keras\n",
    "#from keras.callbacks import EarlyStopping\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras import layers\n",
    "from tensorflow.keras.models import Model, load_model\n",
    "from tensorflow.keras.layers import Input\n",
    "import segmentation_models as sm\n",
    "sm.set_framework('tf.keras')\n",
    "\n",
    "sm.framework()\n",
    "\n",
    "\n",
    "\n",
    "#reduce_lr = ReduceLROnPlateau(factor=0.1, patience=4, min_lr=0.00001, verbose=1)\n",
    "N=1\n",
    "#epochs = 100\n",
    "#batch_size = 32\n",
    "backbone='resnet34'\n",
    "base_model = sm.Unet(backbone, encoder_weights='imagenet')# classes=1, activation='sigmoid')\n",
    "inp = Input(shape=(None, None, N))\n",
    "s = Lambda(lambda x: x / 255)(inp)\n",
    "l1 = tf.keras.layers.Conv2D(3, (1, 1))(s) # map N channels data to 3 channels\n",
    "out = base_model(l1)\n",
    "\n",
    "model5 = Model(inp, out, name=base_model.name)\n",
    "\n",
    "checkpoint=r\"models/checkpoint\"\n",
    "if  os.path.exists(os.path.join(os.getcwd(),checkpoint))==False:\n",
    "    if  os.path.exists(os.path.join(os.getcwd(),'models'))==False:\n",
    "        os.mkdir('models/')\n",
    "    os.mkdir(checkpoint)\n",
    "\n",
    "from tensorflow.keras.optimizers import schedules\n",
    "lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n",
    "    initial_learning_rate=0.000065,\n",
    "    decay_steps=4200,\n",
    "    decay_rate=0.9\n",
    "    )\n",
    "\n",
    "\n",
    "opt = Adam(\n",
    "    learning_rate=lr_schedule, # add exponential decay\n",
    "    epsilon=1e-08)\n",
    "\n",
    "\n",
    "model5.compile(optimizer=opt, loss='binary_crossentropy', metrics=[Intersection_over_Union])# metrics=[tf.keras.metrics.MeanIoU(num_classes=2)])\n",
    "\n",
    "# summarize the model\n",
    "\n",
    "model5.summary()\n",
    "\n",
    "earlystopper = tf.keras.callbacks.EarlyStopping(patience=5, verbose=1,restore_best_weights=True\n",
    "                                                ,monitor='val_Intersection_over_Union', mode='max')\n",
    "\n",
    "\n",
    "\n",
    "history5=model5.fit( np.expand_dims(transformed2['image'],axis=3),  np.expand_dims(transformed2['mask'],axis=3),validation_data = (X_val2,y_val2), batch_size=15, epochs=25, \n",
    "                    callbacks=[earlystopper])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "18d96d43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting Pillow==9.0.0\n",
      "  Downloading Pillow-9.0.0-cp39-cp39-win_amd64.whl (3.2 MB)\n",
      "Installing collected packages: Pillow\n",
      "  Attempting uninstall: Pillow\n",
      "    Found existing installation: Pillow 9.2.0\n",
      "    Uninstalling Pillow-9.2.0:\n",
      "      Successfully uninstalled Pillow-9.2.0\n",
      "Successfully installed Pillow-9.0.0\n"
     ]
    }
   ],
   "source": [
    "!pip install Pillow==9.0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "edc1c8a2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\diego\\AppData\\Roaming\\Python\\Python39\\site-packages\\tensorflow\\python\\keras\\utils\\generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "69/69 [==============================] - 5s 59ms/step - loss: 0.0423 - Intersection_over_Union: 0.8695\n",
      "Accuracy: 86.947465 Loss: 0.042270\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#save the model\n",
    "name='CNN_Unet_onlyflip_exponentialdecay__1loss_2round_ensemble'\n",
    "#os.mkdir('models/')\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/'))==False:\n",
    "    os.mkdir('models/')\n",
    "model3.save(os.path.join('models/',name+'.h5'))\n",
    "model3.save_weights(os.path.join('models/',name+'_weights.h5'))\n",
    "#plt.gca().set_xlim(0,33)\n",
    "   \n",
    "    \n",
    "if  os.path.exists(os.path.join(os.getcwd(),'plots/'))==False:\n",
    "    os.mkdir('plots/')\n",
    "\n",
    "\n",
    "            \n",
    "pd.DataFrame(history2.history).plot(figsize=(8, 5))\n",
    "\n",
    "plt.title('Model Loss and Accuracy',fontsize=14,y=1.03 )\n",
    "plt.grid( True )    \n",
    "\n",
    "plt.ylabel('Loss and Accuracy',labelpad=7,fontsize=10)    \n",
    "plt.xlabel('Epochs',labelpad=7,fontsize=10)   \n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.savefig(os.path.join('plots/',name+'.png'), dpi=500, bbox_inches='tight',facecolor=(1, 1, 1) )\n",
    "\n",
    "#evaluate loss and accuracy\n",
    "loss, accuracy = model.evaluate(X_val2,y_val2)\n",
    "#plot loss and accuracy\n",
    "\n",
    "#plt.gca().set_ylim(0,1)\n",
    "#plt.grid(True)\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/details'))==False:\n",
    "    os.mkdir('models/details')\n",
    "with open(os.path.join('models/details',name+'.txt'), 'w+') as f:\n",
    "    f.write(f'{name} \\n  \\n ')\n",
    "    f.write(f'accuracy : {round(accuracy*100,4)} \\n loss : {round(loss,4)}  \\n ')\n",
    "    f.write(f' \\n optimizer: \\n {model.optimizer.get_config()}')\n",
    "\n",
    "\n",
    "\n",
    "#print accuracy    \n",
    "print('Accuracy: %f' % (accuracy*100),'Loss: %f' % (loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "4405afd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\diego\\AppData\\Roaming\\Python\\Python39\\site-packages\\tensorflow\\python\\keras\\utils\\generic_utils.py:494: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
      "  warnings.warn('Custom mask layers require a config and must override '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24/24 [==============================] - 1s 49ms/step - loss: 0.0942 - Intersection_over_Union: 0.7619\n",
      "Accuracy: 76.191068 Loss: 0.094204\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#save the model\n",
    "name='CNN_Unet_onlyflip_exponentialdecay__2loss_2round'\n",
    "#os.mkdir('models/')\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/'))==False:\n",
    "    os.mkdir('models/')\n",
    "model.save(os.path.join('models/',name+'.h5'))\n",
    "model.save_weights(os.path.join('models/',name+'_weights.h5'))\n",
    "#plt.gca().set_xlim(0,33)\n",
    "   \n",
    "    \n",
    "if  os.path.exists(os.path.join(os.getcwd(),'plots/'))==False:\n",
    "    os.mkdir('plots/')\n",
    "\n",
    "\n",
    "            \n",
    "pd.DataFrame(history2.history).plot(figsize=(8, 5))\n",
    "\n",
    "plt.title('Model Loss and Accuracy',fontsize=14,y=1.03 )\n",
    "plt.grid( True )    \n",
    "\n",
    "plt.ylabel('Loss and Accuracy',labelpad=7,fontsize=10)    \n",
    "plt.xlabel('Epochs',labelpad=7,fontsize=10)   \n",
    "plt.gca().set_ylim(0,1)\n",
    "plt.savefig(os.path.join('plots/',name+'.png'), dpi=500, bbox_inches='tight',facecolor=(1, 1, 1) )\n",
    "\n",
    "#evaluate loss and accuracy\n",
    "loss, accuracy = model.evaluate(X_val2,y_val2)\n",
    "#plot loss and accuracy\n",
    "\n",
    "#plt.gca().set_ylim(0,1)\n",
    "#plt.grid(True)\n",
    "if  os.path.exists(os.path.join(os.getcwd(),'models/details'))==False:\n",
    "    os.mkdir('models/details')\n",
    "with open(os.path.join('models/details',name+'.txt'), 'w+') as f:\n",
    "    f.write(f'{name} \\n  \\n ')\n",
    "    f.write(f'accuracy : {round(accuracy*100,4)} \\n loss : {round(loss,4)}  \\n ')\n",
    "    f.write(f' \\n optimizer: \\n {model.optimizer.get_config()}')\n",
    "\n",
    "\n",
    "\n",
    "#print accuracy    \n",
    "print('Accuracy: %f' % (accuracy*100),'Loss: %f' % (loss))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "7955810d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"X_train2\", \"wb\") as f:\n",
    "    pickle.dump(X_train2, f)\n",
    "with open(\"X_val2\", \"wb\") as f:\n",
    "    pickle.dump(X_val2, f)\n",
    "with open(\"y_train2\", \"wb\") as f:\n",
    "    pickle.dump(y_train2, f)\n",
    "with open(\"y_val2\", \"wb\") as f:\n",
    "    pickle.dump(y_val2, f)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "00fa0f2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 20s 35ms/step\n"
     ]
    }
   ],
   "source": [
    "preds_test2 = model.predict(X_test, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6ae90e28",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "e5a5ef4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"preds_test2\", \"wb\") as f:\n",
    "    pickle.dump(preds_test2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a9353336",
   "metadata": {},
   "outputs": [],
   "source": [
    "im_width = 128\n",
    "im_height = 128\n",
    "im_chan = 1\n",
    "path_test = r'C:\\\\Users\\diego\\OneDrive\\Desktop\\DS\\seismic_segmentation\\test'\n",
    "test_ids = next(os.walk(os.path.join(path_test,\"images\")))[2]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d833560a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting and resizing test images ... \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-10-80c5a58b954f>:6: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n"
     ]
    },
    {
     "data": {
      "application/json": {
       "ascii": false,
       "bar_format": null,
       "colour": null,
       "elapsed": 0.04890727996826172,
       "initial": 0,
       "n": 0,
       "ncols": null,
       "nrows": null,
       "postfix": null,
       "prefix": "",
       "rate": null,
       "total": 18000,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9e524fd8b90a498d82906fb5f809c6b2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/18000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!\n"
     ]
    }
   ],
   "source": [
    "# Get and resize test images\n",
    "X_test = np.zeros((len(test_ids), im_height, im_width, im_chan), dtype=np.uint8)\n",
    "sizes_test = []\n",
    "print('Getting and resizing test images ... ')\n",
    "sys.stdout.flush()\n",
    "for n, id_ in tqdm_notebook(enumerate(test_ids), total=len(test_ids)):\n",
    "    path = path_test\n",
    "    img = load_img(path + '/images/' + id_)\n",
    "    x = img_to_array(img)[:,:,1]\n",
    "    sizes_test.append([x.shape[0], x.shape[1]])\n",
    "    x = resize(x, (128, 128, 1), mode='constant', preserve_range=True)\n",
    "    X_test[n] = x\n",
    "\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d90286d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "563/563 [==============================] - 36s 54ms/step\n"
     ]
    }
   ],
   "source": [
    "#preds_test = model.predict(X_test, verbose=1)\n",
    "#with open(\"preds_test2_8_ensemble\", \"wb\") as f:\n",
    "    #pickle.dump(preds_test, f)\n",
    "\n",
    "#preds_test = np.round_(preds_test, decimals = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "18441f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('preds_test_8ensemble', \"rb\") as f:\n",
    "    stage1_predictions=pickle.load(f)\n",
    "#with open(\"preds_test2_8_4_ensemble\", \"rb\") as f:\n",
    "    #ensemble_4=pickle.load(f)\n",
    "with open('preds_test2_2_ensemble', \"rb\") as f:\n",
    "    pred1=pickle.load(f)    \n",
    "with open('preds_test2_3_ensemble', \"rb\") as f:\n",
    "    pred2=stage1_predictions=pickle.load(f)\n",
    "with open('preds_test2_8_ensemble', \"rb\") as f:\n",
    "    pred3=pickle.load(f)    \n",
    "\n",
    "results=(preds_test2_4+stage1_predictions+pred1+pred2+pred3+preds_test2_5)/6\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6c000551",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18000, 128, 128, 1)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stage1_predictions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5c316771",
   "metadata": {},
   "outputs": [],
   "source": [
    "#save best\n",
    "#with open(\"preds_test_best_submission\", \"wb\") as f:\n",
    "#    pickle.dump(results, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4adfa53",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-31-6d843828a469>:4: TqdmDeprecationWarning: Please use `tqdm.notebook.trange` instead of `tqdm.tnrange`\n",
      "  for i in tnrange(len(preds_test)):\n"
     ]
    },
    {
     "data": {
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       "bar_format": null,
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       "prefix": "",
       "rate": null,
       "total": 18000,
       "unit": "it",
       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2f06544440943fa8282e7087a26d0e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/18000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create list of upsampled test masks\n",
    "preds_test_upsampled = []\n",
    "preds_test=results\n",
    "for i in tnrange(len(preds_test)):\n",
    "    preds_test_upsampled.append(resize(np.squeeze(preds_test[i]), \n",
    "                                       (sizes_test[i][0], sizes_test[i][1]), \n",
    "                                       mode='constant', preserve_range=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3f83aff6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(101, 101)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds_test_upsampled[0].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c8617c59",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-33-6ceb950ee2b3>:38: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n",
      "  pred_dict = {fn[:-4]:RLenc(np.round(preds_test_upsampled[i])) for i,fn in tqdm_notebook(enumerate(test_ids))}\n"
     ]
    },
    {
     "data": {
      "application/json": {
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       "bar_format": null,
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       "unit_divisor": 1000,
       "unit_scale": false
      },
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "67f0c10339954543b0a893454670e6d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def RLenc(img, order='F', format=True):\n",
    "    \"\"\"\n",
    "    img is binary mask image, shape (r,c)\n",
    "    order is down-then-right, i.e. Fortran\n",
    "    format determines if the order needs to be preformatted (according to submission rules) or not\n",
    "\n",
    "    returns run length as an array or string (if format is True)\n",
    "    \"\"\"\n",
    "    bytes = img.reshape(img.shape[0] * img.shape[1], order=order)\n",
    "    runs = []  ## list of run lengths\n",
    "    r = 0  ## the current run length\n",
    "    pos = 1  ## count starts from 1 per WK\n",
    "    for c in bytes:\n",
    "        if (c == 0):\n",
    "            if r != 0:\n",
    "                runs.append((pos, r))\n",
    "                pos += r\n",
    "                r = 0\n",
    "            pos += 1\n",
    "        else:\n",
    "            r += 1\n",
    "\n",
    "    # if last run is unsaved (i.e. data ends with 1)\n",
    "    if r != 0:\n",
    "        runs.append((pos, r))\n",
    "        pos += r\n",
    "        r = 0\n",
    "\n",
    "    if format:\n",
    "        z = ''\n",
    "\n",
    "        for rr in runs:\n",
    "            z += '{} {} '.format(rr[0], rr[1])\n",
    "        return z[:-1]\n",
    "    else:\n",
    "        return runs\n",
    "\n",
    "pred_dict = {fn[:-4]:RLenc(np.round(preds_test_upsampled[i])) for i,fn in tqdm_notebook(enumerate(test_ids))}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "97bce003",
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.DataFrame.from_dict(pred_dict,orient='index')\n",
    "sub.index.names = ['id']\n",
    "sub.columns = ['rle_mask']\n",
    "sub.to_csv('submission_2stage_1stage2_together_8and5_1ensemble_nolovasz.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e3cb233",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "711a8a37",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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