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  • 吴裕雄--天生自然 PYTHON数据分析:糖尿病视网膜病变数据分析

    # This Python 3 environment comes with many helpful analytics libraries installed
    # It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
    # For example, here's several helpful packages to load in 
    
    import numpy as np # linear algebra
    import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
    
    # Input data files are available in the "../input/" directory.
    # For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
    import os, sys
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import skimage.io
    from skimage.transform import resize
    from imgaug import augmenters as iaa
    from tqdm import tqdm
    import PIL
    from PIL import Image, ImageOps
    import cv2
    from sklearn.utils import class_weight, shuffle
    from keras.losses import binary_crossentropy
    from keras.applications.resnet50 import preprocess_input
    import keras.backend as K
    import tensorflow as tf
    from sklearn.metrics import f1_score, fbeta_score
    from keras.utils import Sequence
    from keras.utils import to_categorical
    from sklearn.model_selection import train_test_split

    WORKERS = 2
    CHANNEL = 3
    
    import warnings
    warnings.filterwarnings("ignore")
    IMG_SIZE = 512
    NUM_CLASSES = 5
    SEED = 77
    TRAIN_NUM = 1000 # use 1000 when you just want to explore new idea, use -1 for full train
    df_train = pd.read_csv('F:\kaggleDataSet\diabeticRetinopathy\trainLabels19.csv')
    df_test = pd.read_csv('F:\kaggleDataSet\diabeticRetinopathy\testImages19.csv')
    
    x = df_train['id_code']
    y = df_train['diagnosis']
    
    x, y = shuffle(x, y, random_state=SEED)
    train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.15,stratify=y, random_state=SEED)
    print(train_x.shape, train_y.shape, valid_x.shape, valid_y.shape)
    train_y.hist()
    valid_y.hist()

    %%time
    fig = plt.figure(figsize=(25, 16))
    # display 10 images from each class
    for class_id in sorted(train_y.unique()):
        for i, (idx, row) in enumerate(df_train.loc[df_train['diagnosis'] == class_id].sample(5, random_state=SEED).iterrows()):
            ax = fig.add_subplot(5, 5, class_id * 5 + i + 1, xticks=[], yticks=[])
            path="F:\kaggleDataSet\diabeticRetinopathy\resized train 19\"+str(row['id_code'])+".jpg"
            image = cv2.imread(path)
            image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
            image = cv2.resize(image, (IMG_SIZE, IMG_SIZE))
            plt.imshow(image)
            ax.set_title('Label: %d-%d-%s' % (class_id, idx, row['id_code']) )

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  • 原文地址:https://www.cnblogs.com/tszr/p/11237537.html
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