{"cells":[{"metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nprint(os.listdir(\"../input\"))","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train=pd.read_csv(\"../input/train.csv\")\ntest=pd.read_csv(\"../input/test.csv\")\nsample=pd.read_csv(\"../input/sample_submission.csv\")","execution_count":null,"outputs":[]},{"metadata":{"_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"sns.countplot(x='diagnosis',data=train)","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"\n\n    0 - No DR\n\n    1 - Mild\n\n    2 - Moderate\n\n    3 - Severe\n\n    4 - Proliferative DR\n"},{"metadata":{},"cell_type":"markdown","source":"#### Image Handling\n"},{"metadata":{"trusted":true},"cell_type":"code","source":"import cv2\nimport glob\n\nX_data = []\nimages = glob.glob (\"../input/train_images/*.png\")\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"images[0:5]","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import random\nr = random.sample(images, 3)\nr\n\nplt.figure(figsize=(16,16))\nplt.subplot(131)\nplt.imshow(cv2.imread(r[0]))\n\nplt.subplot(132)\nplt.imshow(cv2.imread(r[1]))\n\nplt.subplot(133)\nplt.imshow(cv2.imread(r[2]))","execution_count":null,"outputs":[]},{"metadata":{},"cell_type":"markdown","source":"##### wow our eyes are really beautiful :P"},{"metadata":{"trusted":true},"cell_type":"markdown","source":"### I'll update this kernel soon :)"},{"metadata":{},"cell_type":"markdown","source":"#### Image path"},{"metadata":{"trusted":true},"cell_type":"code","source":"train_path = '../input/train_images/'\ntest_path = '../input/test_images/'","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"import keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.utils import to_categorical\nfrom keras.preprocessing import image\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils import to_categorical\nfrom tqdm import tqdm\nimport os\nimport cv2","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"train.head()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"id_code=train['id_code'].values\ndiagnosis=train['diagnosis'].values\n\n\ntrain=[]\nX=[]\nY=[]\na=0\nIMG_SIZE=150\nfor i in tqdm(sorted(os.listdir(train_path))):\n    path=os.path.join(train_path,i)\n    i=cv2.imread(path,cv2.IMREAD_COLOR)\n    i = cv2.resize(i, (IMG_SIZE, IMG_SIZE))\n    X.append(i)\n    train.append([np.array(diagnosis),diagnosis[a]])\n    a=a+1\n\ntrain=np.array(train)\nY=train[:,1]\ntrain=train[:,0]\nX=np.array(X)\n\nX.shape\n\nX=X/255\ntrain=train/255","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"\n\ntest1=[]\nX_test=[]\nIMG_SIZE=150\nfor i in tqdm(os.listdir(test_path)):\n    id_code=i\n    path=os.path.join(test_path,i)\n    i=cv2.imread(path,cv2.IMREAD_COLOR)\n    i = cv2.resize(i, (IMG_SIZE, IMG_SIZE))\n    X_test.append(i)\n    test1.append([np.array(i),id_code])\n\nX_test=np.array(X_test)\nX_test.shape\ntest1=np.array(test1)\nid_test=test1[:,1]\ntest1=test1[:,0]\ntest1.shape\n\nX_test=X_test/255\ntest1=test1/255\n\n","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model = Sequential()\nmodel.add(Conv2D(filters=128,kernel_size=2,padding=\"same\",activation=\"relu\",input_shape=(150,150,3)))\nmodel.add(MaxPooling2D(pool_size=2,strides=1))\nmodel.add(Dropout(0.2))\nmodel.add(Conv2D(filters=64,kernel_size=2,padding=\"same\",activation=\"relu\"))\nmodel.add(MaxPooling2D(pool_size=2,strides=1))\nmodel.add(Dropout(0.2))\nmodel.add(Conv2D(filters=32,kernel_size=2,padding=\"same\",activation=\"relu\"))\nmodel.add(MaxPooling2D(pool_size=2,strides=1))\nmodel.add(Dropout(0.2))\nmodel.add(Flatten())\nmodel.add(Dense(32,activation=\"relu\"))\nmodel.add(Dropout(0.7))\nmodel.add(Dense(1,activation=\"softmax\"))\nmodel.summary()","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"model.compile(loss=\"binary_crossentropy\",optimizer=\"adam\",metrics=[\"accuracy\"])\nh=model.fit(X,Y,batch_size=256,validation_split=0.2,epochs=100)","execution_count":null,"outputs":[]},{"metadata":{"trusted":true},"cell_type":"code","source":"","execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.6.4","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat":4,"nbformat_minor":1}