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yusufunlu/copy-of-phase-1.ipynb

Last active May 22, 2022 04:27
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copy-of-phase-1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/yusufunlu/52cbdbed91448a6c2690dc56de72e9e7/copy-of-phase-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "headed-least"
},
"source": [
"## IMPORTS"
],
"id": "headed-least"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "armed-curve"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np \n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"import os\n",
"import seaborn as sns\n",
"sns.set(style = \"darkgrid\")"
],
"id": "armed-curve"
},
{
"cell_type": "code",
"source": [
"os.environ['KAGGLE_USERNAME'] = \"yusufunlu\" # username from the json file\n",
"os.environ['KAGGLE_KEY'] = \"6d464cc201a36e8743ed8fbaa39b235f\" # key from the json file"
],
"metadata": {
"id": "ZfUHdyP662Py"
},
"id": "ZfUHdyP662Py",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bTX7uZhvsQCD",
"outputId": "87222398-fbda-4965-b487-5aca630ff33a"
},
"id": "bTX7uZhvsQCD",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!kaggle datasets download -d hgunraj/covidxct"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3iZUhPTX3Mx4",
"outputId": "44b4c074-1ab4-4e4d-d12a-90ae3b6c8016"
},
"id": "3iZUhPTX3Mx4",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading covidxct.zip to /content\n",
"100% 28.6G/28.7G [03:22<00:00, 156MB/s]\n",
"100% 28.7G/28.7G [03:22<00:00, 152MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!unzip covidxct.zip"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X8Gs_m3H3RR5",
"outputId": "04e62f93-9eb4-46f3-8d3f-cdb09c8d4481"
},
"id": "X8Gs_m3H3RR5",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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" inflating: 2A_images/volume-covid19-A-0698_ct-0000.png \n",
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" inflating: 2A_images/volume-covid19-A-0698_ct-0002.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0003.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0004.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0005.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0006.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0007.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0008.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0009.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0010.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0011.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0012.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0013.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0014.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0015.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0016.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0017.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0018.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0019.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0020.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0021.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0022.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0023.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0024.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0025.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0026.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0027.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0028.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0029.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0030.png \n",
" inflating: 2A_images/volume-covid19-A-0698_ct-0031.png \n",
" inflating: 2A_images/volume-covid19-A-0699-0021.png \n",
" inflating: 2A_images/volume-covid19-A-0699-0023.png \n",
" inflating: 2A_images/volume-covid19-A-0699-0026.png \n",
" inflating: 2A_images/volume-covid19-A-0699-0040.png \n",
" inflating: 2A_images/volume-covid19-A-0699-0041.png \n",
" inflating: metadata.csv \n",
" inflating: test_COVIDx_CT-2A.txt \n",
" inflating: train_COVIDx_CT-2A.txt \n",
" inflating: val_COVIDx_CT-2A.txt \n"
]
}
]
},
{
"cell_type": "markdown",
"source": [
"## SETTING UP PARAMETERS AND DIRECTORIES"
],
"metadata": {
"id": "4uwPrM5c65BS"
},
"id": "4uwPrM5c65BS"
},
{
"cell_type": "code",
"source": [
"#dataset_path= 'drive/MyDrive/covid'\n",
"dataset_path= '.'\n",
"SEED = 12\n",
"IMG_HEIGHT = 128\n",
"IMG_WIDTH = 128\n",
"DIM = (IMG_HEIGHT, IMG_WIDTH)\n",
"TRAIN_SET= 143778\n",
"VALID_SET= 25486\n",
"EPOCHS = 10\n",
"BS = 32\n",
"n = 10000\n",
"LR = 0.0001\n",
"label_file_train = dataset_path+\"/train_COVIDx_CT-2A.txt\"\n",
"label_file_valid = dataset_path+\"/val_COVIDx_CT-2A.txt\""
],
"metadata": {
"id": "B9h9QQCq60VR"
},
"id": "B9h9QQCq60VR",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "yyRoLVH6wa7g"
},
"outputs": [],
"source": [
"def load_labels(label_file):\n",
" \"\"\"Loads image filenames, classes, and bounding boxes\"\"\"\n",
" fnames, classes, bboxes = [], [], []\n",
" with open(label_file, 'r') as f:\n",
" for line in f.readlines():\n",
" fname, cls, xmin, ymin, xmax, ymax = line.strip('\\n').split()\n",
" fnames.append(fname)\n",
" classes.append(int(cls))\n",
" bboxes.append((int(xmin), int(ymin), int(xmax), int(ymax)))\n",
" fnames = [dataset_path + \"/2A_images/\" + fname for fname in fnames]\n",
" fnames = np.array(fnames)\n",
" classes = np.array(classes)\n",
" return fnames, classes, bboxes\n",
"\n",
"def index_generator(fnames , SET):\n",
" \"\"\"Genrated random index of a particular class\"\"\"\n",
" np.random.seed(SEED)\n",
" index = np.random.randint(1,len(fnames),size = SET)\n",
" return index \n",
"\n",
"def numberofclasses(classes, index):\n",
" class0 = len((np.where(classes[index]==0))[0])\n",
" class1 = len((np.where(classes[index]==1))[0])\n",
" class2 = len((np.where(classes[index]==2))[0])\n",
" return class0 , class1, class2\n",
"def dataframe_generator(train_index , valid_index , classes_train , classes_valid ):\n",
" \"\"\"Returns 1 dataframes of datasets distribution\"\"\"\n",
" index = [\"Normal\" , \"Pneumonia\" , \"COIVD -19\"]\n",
" train_DF = numberofclasses(classes_train, train_index)\n",
" valid_DF = numberofclasses(classes_valid, valid_index)\n",
" df = pd.DataFrame({'train': train_DF ,'valid' : valid_DF} , index = index)\n",
" return df\n",
"def train_index_updater(classes_train , train_index,n ):\n",
" \"\"\"Updates train_index for class balance\"\"\"\n",
" np.random.seed(SEED)\n",
" class0_train = np.where(classes_train[train_index]==0)[0]\n",
" class1_train = np.where(classes_train[train_index]==1)[0]\n",
" class2_train = np.where(classes_train[train_index]==2)[0]\n",
" class0 =train_index[class0_train]\n",
" class1 = train_index[class1_train]\n",
" np.random.seed(SEED)\n",
" class22 = np.random.choice(class2_train , n)\n",
" class2 = train_index[class22]\n",
" train_index_updated = np.concatenate((class0 , class1 , class2))\n",
" np.random.shuffle(train_index_updated)\n",
" return train_index_updated\n",
"\n",
"def data_constructor(filepath, classes , dim_size ,index ,bboxes , interpolation = cv2.INTER_AREA , intensify =True):\n",
" \"\"\"Constructs and splits X and Y for training , validtion and test\"\"\"\n",
" np.random.seed(SEED)\n",
" y = np.array(classes[index])\n",
" x = []\n",
" print(\"filepath out: \"+filepath[0])\n",
" for i in index:\n",
" #print(\"filepath in: \"+filepath[i])\n",
" img = cv2.imread(filepath[i])\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
" x1,y1,x2,y2 = bboxes[i]\n",
" img = img[y1:y2,x1:x2]\n",
" img = cv2.resize(img, dim_size , interpolation = interpolation)\n",
" x.append(img)\n",
" \n",
" x = np.array(x)\n",
" if intensify == True:\n",
" x= x/255\n",
" return x , y "
],
"id": "yyRoLVH6wa7g"
},
{
"cell_type": "markdown",
"metadata": {
"id": "little-summer"
},
"source": [
"## LOADING DATA"
],
"id": "little-summer"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "curious-shelf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 335
},
"outputId": "bf822a4e-cc05-41c4-d1dc-33713e418b05"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"fnames_train , classes_train ,bboxes_train = load_labels(label_file_train) # load the training data\n",
"fnames_valid , classes_valid ,bboxes_valid = load_labels(label_file_valid) # load the validation data\n",
"train_index = index_generator(fnames_train , TRAIN_SET)\n",
"valid_index = index_generator(fnames_valid , VALID_SET)\n",
"train_index_updated = train_index_updater(classes_train,train_index,n )\n",
"df = dataframe_generator(train_index_updated , valid_index , classes_train , classes_valid)\n",
"df.plot.bar( title = \"Image Distribution\" );"
],
"id": "curious-shelf"
},
{
"cell_type": "markdown",
"metadata": {
"id": "neutral-wallace"
},
"source": [
"## Loading Training and Validation Images"
],
"id": "neutral-wallace"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "otherwise-market",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2cb4851c-e5e0-4825-a4ff-5bb9c6841ce9"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"filepath out: ./2A_images/NCP_96_1328_0032.png\n"
]
}
],
"source": [
"x_train, y_train = data_constructor(fnames_train,classes_train, DIM , index =train_index_updated ,bboxes = bboxes_train )"
],
"id": "otherwise-market"
},
{
"cell_type": "code",
"source": [
"x_valid , y_valid= data_constructor(fnames_valid,classes_valid, DIM, index =valid_index , bboxes = bboxes_valid)"
],
"metadata": {
"id": "mxSZwGIL6CGe",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d4ee990a-9627-40fb-9ccf-d7b8187b37a4"
},
"id": "mxSZwGIL6CGe",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"filepath out: ./2A_images/NCP_1013_2577_0000.png\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "according-affect"
},
"source": [
"## Pre-Processing"
],
"id": "according-affect"
},
{
"cell_type": "markdown",
"source": [
"Preprocesses a tensor or Numpy array encoding a batch of images.\n",
"Each Keras Application expects a specific kind of input preprocessing. For DenseNet, call tf.keras.applications.densenet.preprocess_input on your inputs before passing them to the model.\n",
"Mobinet scaled and returned to 0 between 1 the input with respect to the ImageNet dataset. I am not sure for scale limits of densenet.preprocess_input but I saw -2.1 to 2.1 values."
],
"metadata": {
"id": "EiNujtCG6pVc"
},
"id": "EiNujtCG6pVc"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "processed-replica"
},
"outputs": [],
"source": [
"#(16148, 128, 128, 3)\n",
"x_train = tf.keras.applications.efficientnet_v2.preprocess_input(x_train)\n",
"x_valid = tf.keras.applications.efficientnet_v2.preprocess_input(x_valid)"
],
"id": "processed-replica"
},
{
"cell_type": "code",
"source": [
"print(x_train.shape)"
],
"metadata": {
"id": "3UoU6the5b0m",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "0750f382-268d-4c52-f101-d7f4271755c0"
},
"id": "3UoU6the5b0m",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(16148, 128, 128, 3)\n"
]
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "d46a6f7b"
},
"outputs": [],
"source": [
"#number of classes reduced to 2\n",
"#sign class 2 as 1 and class 1 as 0\n",
"for i in range(len(y_train)):\n",
" if y_train[i] ==1:\n",
" y_train[i]=0\n",
" if y_train[i] ==2:\n",
" y_train[i]=1\n",
"for i in range(len(y_valid)):\n",
" if y_valid[i] ==1:\n",
" y_valid[i]=0\n",
" if y_valid[i]==2:\n",
" y_valid[i]=1"
],
"id": "d46a6f7b"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "irish-medicaid"
},
"outputs": [],
"source": [
"data_generator = tf.keras.preprocessing.image.ImageDataGenerator(horizontal_flip=True,zoom_range=0.05,width_shift_range=0.2 ,height_shift_range=0.1,shear_range=0.05)## ENTER YOUR AUG HERE\n",
"train_generator = data_generator.flow(x_train, y_train, BS)\n",
"spe = x_train.shape[0] // BS"
],
"id": "irish-medicaid"
},
{
"cell_type": "code",
"source": [
"spe"
],
"metadata": {
"id": "0ecBq2ACLqNk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c763b85e-ee76-4887-92f2-10af8380217e"
},
"id": "0ecBq2ACLqNk",
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"504"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "polished-dealer"
},
"source": [
"## MODEL DESIGN"
],
"id": "polished-dealer"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "honest-declaration"
},
"outputs": [],
"source": [
"from tensorflow.keras.applications import DenseNet201,ResNet152,EfficientNetV2L\n",
"from tensorflow.keras.layers import GlobalAveragePooling2D\n",
"from tensorflow.keras.layers import Dropout\n",
"from tensorflow.keras.layers import Dense\n",
"from tensorflow.keras.layers import Input\n",
"from tensorflow.keras.models import Model\n",
"from tensorflow.keras.layers import Dropout\n",
"from tensorflow.keras.layers import Embedding, LSTM"
],
"id": "honest-declaration"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "religious-creator",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b572fab7-7409-47df-91b3-54c9fcdcf341"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-l_notop.h5\n",
"473178112/473176280 [==============================] - 7s 0us/step\n",
"473186304/473176280 [==============================] - 7s 0us/step\n"
]
}
],
"source": [
"#dense_base_model = DenseNet201(include_top=False,weights=\"imagenet\",input_shape=(IMG_HEIGHT,IMG_HEIGHT,3))\n",
"eff_base_model = EfficientNetV2L(include_top=False,weights=\"imagenet\",input_shape=(IMG_HEIGHT,IMG_HEIGHT,3))\n",
"\n",
"model= tf.keras.Sequential()\n",
"model.add(eff_base_model) \n",
"model.add(GlobalAveragePooling2D()) \n",
"model.add(Dense(64,activation=('relu'))) # 1x64\n",
"model.add(Dropout(.2)) # applies regularization, prevents overfitting\n",
"model.add(Dense(1,activation=('sigmoid'))) # classification layer"
],
"id": "religious-creator"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "joint-avenue",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fbd5bbf2-abb5-4b1d-a152-da45bde95f95"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Model: \"sequential\"\n",
"_________________________________________________________________\n",
" Layer (type) Output Shape Param # \n",
"=================================================================\n",
" efficientnetv2-l (Functiona (None, 4, 4, 1280) 117746848 \n",
" l) \n",
" \n",
" global_average_pooling2d (G (None, 1280) 0 \n",
" lobalAveragePooling2D) \n",
" \n",
" dense (Dense) (None, 64) 81984 \n",
" \n",
" dropout (Dropout) (None, 64) 0 \n",
" \n",
" dense_1 (Dense) (None, 1) 65 \n",
" \n",
"=================================================================\n",
"Total params: 117,828,897\n",
"Trainable params: 117,316,321\n",
"Non-trainable params: 512,576\n",
"_________________________________________________________________\n"
]
}
],
"source": [
"model.summary()"
],
"id": "joint-avenue"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "radio-anime"
},
"outputs": [],
"source": [
"#LR is learning rate\n",
"#if LR is smaller it get slower but can get into details of learning\n",
"#if LR is too big there is a posilibilty for reflecting forever and can't learn everything or can take too long time\n",
"model.compile(optimizer=tf.keras.optimizers.SGD(LR),\n",
" loss = \"binary_crossentropy\",\n",
" metrics = [\"accuracy\"]) # derive from binary crossentropy"
],
"id": "radio-anime"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aerial-attachment"
},
"outputs": [],
"source": [
"##stocastic gradient descent derive from binary_crossentropy, when it finds the direction it goes in that direction as LR size\n",
"from tensorflow.keras.callbacks import ModelCheckpoint\n",
"modelsdir = \"models\"\n",
"checkpoint = ModelCheckpoint(filepath = modelsdir + \"/SGD.{epoch:02d}-{val_accuracy:.4f}.h5\" ,\n",
" monitor= \"val_accuracy\" , \n",
" mode = \"max\",\n",
" verbose = 0,\n",
" save_best_only=True)"
],
"id": "aerial-attachment"
},
{
"cell_type": "code",
"source": [
"EPOCHS"
],
"metadata": {
"id": "fmJ0rQ1HOC0N",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "49d16347-f950-4532-ed27-16debb2a41cb"
},
"id": "fmJ0rQ1HOC0N",
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"10"
]
},
"metadata": {},
"execution_count": 24
}
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "closed-wonder",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "23884b40-3d65-4327-e73c-c3f800a36b83"
},
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/10\n",
"504/504 [==============================] - 2267s 4s/step - loss: 0.7018 - accuracy: 0.5001 - val_loss: 0.6681 - val_accuracy: 0.6072\n",
"Epoch 2/10\n",
"504/504 [==============================] - 2228s 4s/step - loss: 0.6844 - accuracy: 0.5521 - val_loss: 0.6653 - val_accuracy: 0.6241\n",
"Epoch 3/10\n",
"504/504 [==============================] - 2304s 5s/step - loss: 0.6700 - accuracy: 0.5880 - val_loss: 0.6615 - val_accuracy: 0.6267\n",
"Epoch 4/10\n",
"504/504 [==============================] - 2370s 5s/step - loss: 0.6603 - accuracy: 0.6114 - val_loss: 0.6691 - val_accuracy: 0.6037\n",
"Epoch 5/10\n",
"504/504 [==============================] - 2331s 5s/step - loss: 0.6457 - accuracy: 0.6440 - val_loss: 0.6742 - val_accuracy: 0.5952\n",
"Epoch 6/10\n",
"504/504 [==============================] - 2399s 5s/step - loss: 0.6301 - accuracy: 0.6796 - val_loss: 0.6441 - val_accuracy: 0.6558\n",
"Epoch 7/10\n",
"504/504 [==============================] - 2631s 5s/step - loss: 0.6149 - accuracy: 0.6973 - val_loss: 0.6465 - val_accuracy: 0.6517\n",
"Epoch 8/10\n",
"504/504 [==============================] - 2534s 5s/step - loss: 0.5965 - accuracy: 0.7267 - val_loss: 0.6404 - val_accuracy: 0.6606\n",
"Epoch 9/10\n",
"504/504 [==============================] - 2587s 5s/step - loss: 0.5826 - accuracy: 0.7424 - val_loss: 0.6331 - val_accuracy: 0.6766\n",
"Epoch 10/10\n",
"504/504 [==============================] - 2590s 5s/step - loss: 0.5672 - accuracy: 0.7466 - val_loss: 0.6395 - val_accuracy: 0.6605\n"
]
}
],
"source": [
"#batch size is samples per iteration\n",
"#batch size could be 8,16,32,64,etc... depending on GPU RAM or TPU RAM. It is not always good for a bigger batch size even if we have a lot memory. Cuz if batch size is big sometimes it doesn't learn\n",
"r = model.fit(train_generator , validation_data=(x_valid, y_valid), steps_per_epoch = spe ,epochs=EPOCHS ,verbose = 1 )"
],
"id": "closed-wonder"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "transsexual-looking",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 268
},
"outputId": "33345b92-5795-4cc1-a575-8eaa5e96dfcd"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(r.history[\"loss\"], label= \"Loss\")\n",
"plt.plot(r.history[\"val_loss\"], label = \"val_Loss\")\n",
"plt.legend();"
],
"id": "transsexual-looking"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "another-general",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 268
},
"outputId": "bf905a60-e2cd-4cbd-d42c-027a9a24b916"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(r.history[\"accuracy\"] ,label = \"Accuracy\")\n",
"plt.plot(r.history[\"val_accuracy\"] ,label = \"Val_Accuracy\")\n",
"plt.legend();"
],
"id": "another-general"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "conditional-fitting"
},
"outputs": [],
"source": [
"#model fitting history\n",
"#we save as csv for later on investigating\n",
"pd.DataFrame(r.history).to_csv(\"eff-1-128.csv\",index=False)"
],
"id": "conditional-fitting"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "athletic-tribune"
},
"outputs": [],
"source": [
"model.save(\"eff-1-128.h5\") # save the weights"
],
"id": "athletic-tribune"
},
{
"cell_type": "markdown",
"metadata": {
"id": "adult-cincinnati"
},
"source": [
"## MORE TRAINING"
],
"id": "adult-cincinnati"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "illegal-termination"
},
"outputs": [],
"source": [
"LR = 0.001\n",
"EPOCHS = 3"
],
"id": "illegal-termination"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "unavailable-start"
},
"outputs": [],
"source": [
"#it sign as hasta for lest than 0.5\n",
"#we need binary results used binary_crossentropy\n",
"model.compile(optimizer=tf.keras.optimizers.SGD(LR),\n",
" loss = \"binary_crossentropy\",\n",
" metrics = [\"accuracy\"])"
],
"id": "unavailable-start"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "necessary-corrections"
},
"outputs": [],
"source": [
"#after each epoch if val_accuracy is max then save it.\n",
"#save accuracy to csv but callback weights too. \n",
"from tensorflow.keras.callbacks import ModelCheckpoint\n",
"modelsdir = \"models\"\n",
"checkpoint = ModelCheckpoint(filepath = modelsdir + \"/(mt)SGD.{epoch:02d}-{val_accuracy:.4f}.h5\" ,\n",
" monitor= \"val_accuracy\" , \n",
" mode = \"max\",\n",
" verbose = 0,\n",
" save_best_only=True)"
],
"id": "necessary-corrections"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "african-scroll",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d561b02f-c2ab-41ca-e005-c243b8b8c6b8"
},
"outputs": [
{
"metadata": {
"tags": null
},
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"504/504 [==============================] - 2755s 5s/step - loss: 0.4847 - accuracy: 0.7995 - val_loss: 125.5490 - val_accuracy: 0.5039\n",
"Epoch 2/3\n",
"504/504 [==============================] - 2636s 5s/step - loss: 0.3521 - accuracy: 0.8621 - val_loss: 0.9521 - val_accuracy: 0.4657\n",
"Epoch 3/3\n",
"504/504 [==============================] - 2585s 5s/step - loss: 0.2885 - accuracy: 0.8862 - val_loss: 385.8811 - val_accuracy: 0.4320\n"
]
}
],
"source": [
"s = model.fit(train_generator , validation_data=(x_valid, y_valid), steps_per_epoch = spe ,epochs=EPOCHS, callbacks = [checkpoint] ,verbose = 1 )"
],
"id": "african-scroll"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "executive-multiple",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "266b7aa5-bc92-4d83-f870-b700fb95b27d"
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(s.history[\"loss\"], label= \"Loss\")\n",
"plt.plot(s.history[\"val_loss\"], label = \"val_Loss\")\n",
"plt.legend();"
],
"id": "executive-multiple"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "incoming-communist",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 268
},
"outputId": "a1be7ddf-8215-40fa-bc25-1b1e84aa0af3"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(s.history[\"accuracy\"] ,label = \"Accuracy\")\n",
"plt.plot(s.history[\"val_accuracy\"] ,label = \"Val_Accuracy\")\n",
"plt.legend();"
],
"id": "incoming-communist"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "binding-retirement"
},
"outputs": [],
"source": [
"pd.DataFrame(s.history).to_csv(\"eff-2-128.csv\",index=False)"
],
"id": "binding-retirement"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "aquatic-chance"
},
"outputs": [],
"source": [
"model.save(\"eff-2-128.h5\")"
],
"id": "aquatic-chance"
},
{
"cell_type": "markdown",
"metadata": {
"id": "c79175c9"
},
"source": [
"### Loading Combined CSV for plots"
],
"id": "c79175c9"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "0hnO0u_p2Fya"
},
"outputs": [],
"source": [
"phase1_history = pd.read_csv(\"eff-1-128.csv\")\n",
"phase2_history = pd.read_csv(\"eff-2-128.csv\")\n",
"history = pd.concat([phase1_history,phase2_history],ignore_index=True)\n",
"pd.DataFrame(history).to_csv('eff-combined-128.csv',index=False)"
],
"id": "0hnO0u_p2Fya"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "senior-turkey"
},
"outputs": [],
"source": [
"history = pd.read_csv(\"eff-combined-128.csv\")"
],
"id": "senior-turkey"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "removed-preparation",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "24293016-6516-4c72-bfd1-7411b46d4b5c"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(history[\"loss\"], label= \"Loss\")\n",
"plt.plot(history[\"val_loss\"], label = \"val_Loss\")\n",
"plt.legend();"
],
"id": "removed-preparation"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "improving-bargain",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 268
},
"outputId": "eca5af3b-0dec-49ec-f09c-48c523ac1322"
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(history[\"accuracy\"] ,label = \"Accuracy\")\n",
"plt.plot(history[\"val_accuracy\"] ,label = \"Val_Accuracy\")\n",
"plt.legend();"
],
"id": "improving-bargain"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "intelligent-skill",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
},
"outputId": "7a3af098-0122-429e-aae6-1cdbf5e28128"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
],
"source": [
"history.plot();"
],
"id": "intelligent-skill"
},
{
"cell_type": "code",
"source": [
"#!mkdir drive/MyDrive/covid/128"
],
"metadata": {
"id": "66N787sc-v-G"
},
"id": "66N787sc-v-G",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"#!cp *128* drive/MyDrive/covid/128"
],
"metadata": {
"id": "ETCt6GwO-p9O"
},
"id": "ETCt6GwO-p9O",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
""
],
"metadata": {
"id": "vDhs_050Aqmx"
},
"id": "vDhs_050Aqmx",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "golden-thread"
},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import itertools\n",
"SEED =12\n",
"\n",
"def load_labels_test(label_file):\n",
" \"\"\"Loads image filenames, classes, and bounding boxes\"\"\"\n",
" fnames, classes, bboxes = [], [], []\n",
" with open(label_file, 'r') as f:\n",
" for line in f.readlines():\n",
" #typical line is like \"NCP_341_1834_0022.png 2 0 130 512 429\"\n",
" #clean \"\\n\" character from line(it doesn't change original line of file)\n",
" #split the line into chunks, default delimiter is \" \" space character\n",
" fname, cls, xmin, ymin, xmax, ymax = line.strip('\\n').split()\n",
" #As an example below\n",
" #fname=NCP_341_1834_0022.png,cls=2,xmin=0,ymin=130,xmax=512,ymax=429\n",
" fnames.append(fname)\n",
" classes.append(int(cls))\n",
" bboxes.append((int(xmin), int(ymin), int(xmax), int(ymax)))\n",
" #Below line was different as including windows dataset path in original code\n",
" #by dataset_path it is externalized\n",
" #fnames include path+filename not only file names\n",
" fnames = [dataset_path + \"/2A_images/\" + fname for fname in fnames]\n",
" #create and numy array\n",
" fnames = np.array(fnames)\n",
" classes = np.array(classes)\n",
" #return 3 values as tuple\n",
" return fnames, classes, bboxes\n",
"def index_generator_test(fnames , SET):\n",
" \"\"\"Generated random index of a particular class\"\"\"\n",
" np.random.seed(SEED)\n",
" index = np.random.randint(1,len(fnames),size = SET)\n",
" return index \n",
"def numberofclasses_test(classes, index):\n",
" class0 = len((np.where(classes[index]==0))[0])\n",
" class1 = len((np.where(classes[index]==1))[0])\n",
" class2 = len((np.where(classes[index]==2))[0])\n",
" return class0 , class1, class2\n",
"def dataframe_generator_test(test_index ,classes_test ):\n",
" \"\"\"Returns 1 dataframes of datasets distribution\"\"\"\n",
" index = [\"Normal\" , \"Pneumonia\" , \"COVID -19\"]\n",
" test_DF = numberofclasses_test(classes_test, test_index)\n",
" #\"Normal\",\"Pneumonia\",\"COVID -19\" used as index\n",
" df = pd.DataFrame({'test': test_DF } , index = index)\n",
" return df\n",
"def data_constructor_test(filepath, classes , dim_size ,index ,bboxes , interpolation = cv2.INTER_AREA , intensify =False):\n",
" \"\"\"Constructs and splits X and Y for training , validtion and test\"\"\"\n",
" np.random.seed(SEED)\n",
" y = np.array(classes[index])\n",
" x = []\n",
" for i in index:\n",
" img = cv2.imread(filepath[i])\n",
" img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n",
" x1,y1,x2,y2 = bboxes[i]\n",
" img = img[y1:y2,x1:x2]\n",
" img = cv2.resize(img, dim_size , interpolation = interpolation)\n",
" x.append(img)\n",
" \n",
" x = np.array(x)\n",
" if intensify == True:\n",
" x= x/255\n",
" return x , y \n",
"def phase1_labels_updater_test(efficient_y):\n",
" \"\"\"\n",
" Updates labels for Phase-1 testing\n",
" \"\"\"\n",
" for i in range(len(efficient_y)):\n",
" if efficient_y[i] == 2:\n",
" efficient_y[i] =1 \n",
" elif efficient_y[i]==1:\n",
" efficient_y[i] = 0 \n",
" return efficient_y \n",
"def index_extractor_test(y_preds, y_test, test_index ,classes_test):\n",
" \"\"\"\n",
" Extracts indexs of all the Original classes and Predicted classes by Phase-1 Model \n",
" \"\"\"\n",
" yclass_0 = np.where(y_preds==0)[0]\n",
" yclass_1 = np.where(y_preds==1)[0]\n",
" class_0 = np.where(y_test==0)[0]\n",
" class_1 = np.where(y_test==1)[0]\n",
" yclass0 = test_index[yclass_0]\n",
" yclass1 = test_index[yclass_1]\n",
" class0 = test_index[class_0]\n",
" class1 =test_index[class_1]\n",
" y00=np.where(classes_test[yclass0]==0)[0]\n",
" y01=np.where(classes_test[yclass0]==1)[0]\n",
" y02=np.where(classes_test[yclass0]==2)[0]\n",
" y20=np.where(classes_test[yclass1]==0)[0]\n",
" y21=np.where(classes_test[yclass1]==1)[0]\n",
" index_00 = yclass0[y00]\n",
" index_01 = yclass0[y01]\n",
" index_02 = yclass0[y02]\n",
" index_20 = yclass1[y20]\n",
" index_21 = yclass1[y21]\n",
" index_0 = np.concatenate((index_00,index_01))\n",
" return index_0,index_02,index_20,index_21\n",
"def plot_confusion_matrix_test(cm, classes,normalize=False,title='Confusion matrix',cmap=plt.cm.Blues):\n",
" \"\"\"\n",
" This function prints and plots the confusion matrix.\n",
" \"\"\"\n",
" print(cm)\n",
" plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" plt.title(title)\n",
" plt.colorbar()\n",
" tick_marks = np.arange(len(classes))\n",
" plt.xticks(tick_marks, classes, rotation=45)\n",
" plt.yticks(tick_marks, classes)\n",
" fmt = '.2f' if normalize else 'd'\n",
" thresh = cm.max() / 2.\n",
" for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
" plt.text(j, i, format(cm[i, j], fmt), horizontalalignment=\"center\",color=\"white\" if cm[i, j] > thresh else \"black\")\n",
" plt.tight_layout()\n",
" plt.ylabel('True label')\n",
" plt.xlabel('Predicted label')\n",
" plt.show()"
],
"id": "golden-thread"
}
],
"metadata": {
"accelerator": "TPU",
"colab": {
"collapsed_sections": [],
"machine_shape": "hm",
"name": "Phase-1-eff.ipynb",
"provenance": [],
"background_execution": "on",
"include_colab_link": true
},
"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": "3.6.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
Display the source blob
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Raw
phase-1-eff-10e-reduceddataset.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/yusufunlu/52cbdbed91448a6c2690dc56de72e9e7/copy-of-phase-1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "headed-least"
},
"source": [
"## IMPORTS"
],
"id": "headed-least"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "armed-curve"
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np \n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"import os\n",
"import seaborn as sns\n",
"sns.set(style = \"darkgrid\")"
],
"id": "armed-curve"
},
{
"cell_type": "code",
"source": [
"os.environ['KAGGLE_USERNAME'] = \"yusufunlu\" # username from the json file\n",
"os.environ['KAGGLE_KEY'] = \"6d464cc201a36e8743ed8fbaa39b235f\" # key from the json file"
],
"metadata": {
"id": "ZfUHdyP662Py"
},
"id": "ZfUHdyP662Py",
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from google.colab import drive\n",
"drive.mount('/content/drive')"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bTX7uZhvsQCD",
"outputId": "87222398-fbda-4965-b487-5aca630ff33a"
},
"id": "bTX7uZhvsQCD",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!kaggle datasets download -d hgunraj/covidxct"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3iZUhPTX3Mx4",
"outputId": "44b4c074-1ab4-4e4d-d12a-90ae3b6c8016"
},
"id": "3iZUhPTX3Mx4",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Downloading covidxct.zip to /content\n",
"100% 28.6G/28.7G [03:22<00:00, 156MB/s]\n",
"100% 28.7G/28.7G [03:22<00:00, 152MB/s]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!unzip covidxct.zip"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "X8Gs_m3H3RR5",
"outputId": "04e62f93-9eb4-46f3-8d3f-cdb09c8d4481"
},
"id": "X8Gs_m3H3RR5",
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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