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+++ b/test_preds_function.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: efficientnet in /home/surya/anaconda3/lib/python3.6/site-packages (1.0.0)\n",
+      "Requirement already satisfied: scikit-image in /home/surya/anaconda3/lib/python3.6/site-packages (from efficientnet) (0.15.0)\n",
+      "Requirement already satisfied: keras-applications<=1.0.8,>=1.0.7 in /home/surya/anaconda3/lib/python3.6/site-packages (from efficientnet) (1.0.8)\n",
+      "Requirement already satisfied: matplotlib!=3.0.0,>=2.0.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.2.2)\n",
+      "Requirement already satisfied: scipy>=0.17.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (1.1.0)\n",
+      "Requirement already satisfied: imageio>=2.0.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.3.0)\n",
+      "Requirement already satisfied: PyWavelets>=0.4.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (0.5.2)\n",
+      "Requirement already satisfied: pillow>=4.3.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (6.2.0)\n",
+      "Requirement already satisfied: networkx>=2.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from scikit-image->efficientnet) (2.1)\n",
+      "Requirement already satisfied: h5py in /home/surya/anaconda3/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (2.7.1)\n",
+      "Requirement already satisfied: numpy>=1.9.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.17.3)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/surya/anaconda3/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (0.10.0)\n",
+      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.2.0)\n",
+      "Requirement already satisfied: python-dateutil>=2.1 in /home/surya/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2.7.3)\n",
+      "Requirement already satisfied: pytz in /home/surya/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (2018.5)\n",
+      "Requirement already satisfied: six>=1.10 in /home/surya/.local/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.11.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (1.0.1)\n",
+      "Requirement already satisfied: decorator>=4.1.0 in /home/surya/anaconda3/lib/python3.6/site-packages (from networkx>=2.0->scikit-image->efficientnet) (4.3.0)\n",
+      "Requirement already satisfied: setuptools in /home/surya/anaconda3/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib!=3.0.0,>=2.0.0->scikit-image->efficientnet) (39.1.0)\n",
+      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.3.1 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
+      "Requirement already satisfied: iterative-stratification in /home/surya/anaconda3/lib/python3.6/site-packages (0.1.6)\n",
+      "Requirement already satisfied: numpy in /home/surya/anaconda3/lib/python3.6/site-packages (from iterative-stratification) (1.17.3)\n",
+      "Requirement already satisfied: scipy in /home/surya/anaconda3/lib/python3.6/site-packages (from iterative-stratification) (1.1.0)\n",
+      "Requirement already satisfied: scikit-learn in /home/surya/anaconda3/lib/python3.6/site-packages (from iterative-stratification) (0.20.3)\n",
+      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.3.1 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
+      "Requirement already satisfied: gdown in /home/surya/anaconda3/lib/python3.6/site-packages (3.8.1)\n",
+      "Requirement already satisfied: filelock in /home/surya/anaconda3/lib/python3.6/site-packages (from gdown) (3.0.4)\n",
+      "Requirement already satisfied: six in /home/surya/.local/lib/python3.6/site-packages (from gdown) (1.11.0)\n",
+      "Requirement already satisfied: tqdm in /home/surya/anaconda3/lib/python3.6/site-packages (from gdown) (4.28.1)\n",
+      "Requirement already satisfied: requests in /home/surya/anaconda3/lib/python3.6/site-packages (from gdown) (2.18.4)\n",
+      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (3.0.4)\n",
+      "Requirement already satisfied: idna<2.7,>=2.5 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (2.6)\n",
+      "Requirement already satisfied: urllib3<1.23,>=1.21.1 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (1.22)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /home/surya/anaconda3/lib/python3.6/site-packages (from requests->gdown) (2018.4.16)\n",
+      "\u001b[33mWARNING: You are using pip version 19.1.1, however version 19.3.1 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install efficientnet\n",
+    "!pip install iterative-stratification\n",
+    "!pip install gdown\n",
+    "\n",
+    "import os\n",
+    "if not os.path.isfile('model_effnet_bo_087.h5'):\n",
+    "    !gdown https://drive.google.com/uc?id=1FXF1HymYbRf3OlThMTXAa74TRup3AhD_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pydicom\n",
+    "import cv2\n",
+    "import tensorflow as tf\n",
+    "import multiprocessing\n",
+    "from math import ceil, floor\n",
+    "import keras\n",
+    "import keras.backend as K\n",
+    "from keras.callbacks import Callback, ModelCheckpoint\n",
+    "from keras.layers import Dense, Flatten, Dropout\n",
+    "from keras.models import Model, load_model\n",
+    "from keras.utils import Sequence\n",
+    "from keras.losses import binary_crossentropy\n",
+    "from keras.optimizers import Adam\n",
+    "import efficientnet.keras as efn \n",
+    "from iterstrat.ml_stratifiers import MultilabelStratifiedShuffleSplit\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
+    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
+   },
+   "outputs": [],
+   "source": [
+    "HEIGHT = 256\n",
+    "WIDTH = 256\n",
+    "CHANNELS = 3\n",
+    "SHAPE = (HEIGHT, WIDTH, CHANNELS)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def correct_dcm(dcm):\n",
+    "    x = dcm.pixel_array + 1000\n",
+    "    px_mode = 4096\n",
+    "    x[x>=px_mode] = x[x>=px_mode] - px_mode\n",
+    "    dcm.PixelData = x.tobytes()\n",
+    "    dcm.RescaleIntercept = -1000\n",
+    "\n",
+    "def window_image(dcm, window_center, window_width):    \n",
+    "    if (dcm.BitsStored == 12) and (dcm.PixelRepresentation == 0) and (int(dcm.RescaleIntercept) > -100):\n",
+    "        correct_dcm(dcm)\n",
+    "    img = dcm.pixel_array * dcm.RescaleSlope + dcm.RescaleIntercept\n",
+    "    \n",
+    "    # Resize\n",
+    "    img = cv2.resize(img, SHAPE[:2], interpolation = cv2.INTER_LINEAR)\n",
+    "   \n",
+    "    img_min = window_center - window_width // 2\n",
+    "    img_max = window_center + window_width // 2\n",
+    "    img = np.clip(img, img_min, img_max)\n",
+    "    return img\n",
+    "\n",
+    "def bsb_window(dcm):\n",
+    "    brain_img = window_image(dcm, 40, 80)\n",
+    "    subdural_img = window_image(dcm, 80, 200)\n",
+    "    soft_img = window_image(dcm, 40, 380)\n",
+    "    \n",
+    "    brain_img = (brain_img - 0) / 80\n",
+    "    subdural_img = (subdural_img - (-20)) / 200\n",
+    "    soft_img = (soft_img - (-150)) / 380\n",
+    "    bsb_img = np.array([brain_img, subdural_img, soft_img]).transpose(1,2,0)\n",
+    "    return bsb_img\n",
+    "\n",
+    "def _read(path, SHAPE):\n",
+    "    dcm = pydicom.dcmread(path)\n",
+    "    try:\n",
+    "        img = bsb_window(dcm)\n",
+    "    except:\n",
+    "        img = np.zeros(SHAPE)\n",
+    "    return img"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import the training and test datasets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_model =  efn.EfficientNetB0(weights = 'imagenet', include_top = False, \\\n",
+    "                                 pooling = 'avg', input_shape = (HEIGHT, WIDTH, 3))\n",
+    "\n",
+    "x = base_model.output\n",
+    "x = Dropout(0.125)(x)\n",
+    "output_layer = Dense(6, activation = 'sigmoid')(x)\n",
+    "model = Model(inputs=base_model.input, outputs=output_layer)\n",
+    "model.compile(optimizer = Adam(lr = 0.0001), \n",
+    "              loss = 'binary_crossentropy',\n",
+    "              metrics = ['acc'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model.load_weights('model_effnet_bo_087.h5')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(256, 256, 3)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "img_data = _read('ID_000000e27.dcm', (256, 256))\n",
+    "img_data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7f9f047b2c18>"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.imshow(img_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 256, 256, 3)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Prepare the data\n",
+    "# 1 ==> Batch size\n",
+    "# 256, 256, 3 is the image shape\n",
+    "X = np.empty((1, 256,256, 3))\n",
+    "X[0] = img_data\n",
+    "X.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.05049768, 0.00077422, 0.00192805, 0.00035753, 0.0012597 ,\n",
+       "        0.02818136]], dtype=float32)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds = model.predict(X)\n",
+    "preds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 6)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "preds.shape"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}