太强了，竟然可以根据指纹图像预测性别！

重磅干货，第一时间送达

https://www.kaggle.com/ruizgara/socofing

#import necessary librariesimport numpy as npimport pandas as pdimport seaborn as snsimport tensorflow as tfimport osimport cv2import matplotlib.pyplot as plt#Function to extract labels for both real and altered imagesdef extract_label(img_path,train = True): filename, _ = os.path.splitext(os.path.basename(img_path))
subject_id, etc = filename.split('__') #For Altered folder if train: gender, lr, finger, _, _ = etc.split('_') #For Real folder else: gender, lr, finger, _ = etc.split('_') gender = 0 if gender == 'M' else 1 lr = 0 if lr == 'Left' else 1
if finger == 'thumb': finger = 0 elif finger == 'index': finger = 1 elif finger == 'middle': finger = 2 elif finger == 'ring': finger = 3 elif finger == 'little': finger = 4 return np.array([gender], dtype=np.uint16)

img_size = 96#Function to iterate through all the imagesdef loading_data(path,train): print("loading data from: ",path) data = [] for img in os.listdir(path): try: img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE) img_resize = cv2.resize(img_array, (img_size, img_size)) label = extract_label(os.path.join(path, img),train) data.append([label[0], img_resize ]) except Exception as e: pass data    return data

Real_path = "../input/socofing/SOCOFing/Real"Easy_path = "../input/socofing/SOCOFing/Altered/Altered-Easy"Medium_path = "../input/socofing/SOCOFing/Altered/Altered-Medium"Hard_path = "../input/socofing/SOCOFing/Altered/Altered-Hard"

Easy_data = loading_data(Easy_path, train = True)Medium_data = loading_data(Medium_path, train = True)Hard_data = loading_data(Hard_path, train = True)test = loading_data(Real_path, train = False)
data = np.concatenate([Easy_data, Medium_data, Hard_data], axis=0)
del Easy_data, Medium_data, Hard_data

import randomrandom.shuffle(data)random.shuffle(test)

img, labels = [], []for label, feature in data: labels.append(label) img.append(feature)train_data = np.array(img).reshape(-1, img_size, img_size, 1)train_data = train_data / 255.0from keras.utils.np_utils import to_categoricaltrain_labels = to_categorical(labels, num_classes = 2)del data

#Import necessary librariesfrom tensorflow.keras import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flattenfrom tensorflow.keras import layersfrom tensorflow.keras import optimizers
model = Sequential([ Conv2D(32, 3, padding='same', activation='relu',kernel_initializer='he_uniform', input_shape = [96, 96, 1]), MaxPooling2D(2), Conv2D(32, 3, padding='same', kernel_initializer='he_uniform', activation='relu'), MaxPooling2D(2), Flatten(), Dense(128, kernel_initializer='he_uniform',activation = 'relu'), Dense(2, activation = 'softmax'), ])

model.compile(optimizer = optimizers.Adam(1e-3), loss = 'categorical_crossentropy', metrics = ['accuracy'])early_stopping_cb = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=10)

history = model.fit(train_data, train_labels, batch_size = 128, epochs = 30, validation_split = 0.2, callbacks = [early_stopping_cb], verbose = 1)

import pandas as pdimport matplotlib.pyplot as pltpd.DataFrame(history.history).plot(figsize = (8,5))plt.grid(True)plt.gca().set_ylim(0,1)

test_images, test_labels = [], []
for label, feature in test: test_images.append(feature) test_labels.append(label) test_images = np.array(test_images).reshape(-1, img_size, img_size, 1)test_images = test_images / 255.0del testtest_labels = to_categorical(test_labels, num_classes = 2)

model.evaluate(test_images, test_labels)

https://www.kaggle.com/abolarinbukola/fingerprint-gender-classification-cnn

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