Python tensorflow keras CNN VGG16 imagenet 预训练权重 人脸识别分类

from keras.applications.vgg16 import VGG16 import numpy as np from keras.preprocessing.image import ImageDataGenerator from keras.models import Sequential, load_model from keras.layers import Dropout, Flatten, Dense, Convolution2D, MaxPooling2D import os import cv2 from sklearn.model_selection import train_test_split from keras.utils.np_utils import to_categorical root_path = "D:/C Moving Destination/Documents/Tencent Files/2451360621/FileRecv/vggface/" dirs = os.listdir(root_path) img_paths = [] lens = [] for dir_ in dirs: img_filenames = os.listdir(root_path + dir_) lens.append(len(os.listdir(root_path + dir_))) for name in img_filenames: img_paths.append(root_path + dir_ + "/" + name) labels = [] for i in range(len(lens)): for l in range(lens[i]): labels.append(i) labels = np.array(labels) imgs = [] for path in img_paths: img = cv2.imread(path) img = cv2.resize(img, (150, 150)) img = img[:, :, ::-1] img = np.reshape(img, (1, img.shape[0], img.shape[1], img.shape[2])) imgs.append(img) imgs = np.concatenate(imgs, axis=0) labels = to_categorical(labels, len(np.unique(labels))) x_train, x_test, y_train, y_test = train_test_split(imgs, labels, test_size=0.1) model = Sequential() model.add( Convolution2D(input_shape=(150, 150, 3), filters=32, kernel_size=3, strides=1, padding='same', activation='relu')) model.add(Convolution2D(filters=32, kernel_size=3, strides=1, padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=2, strides=2, padding='valid')) model.add(Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu')) model.add(Convolution2D(filters=64, kernel_size=3, strides=1, padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=2, strides=2, padding='valid')) model.add(Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu')) model.add(Convolution2D(filters=128, kernel_size=3, strides=1, padding='same', activation='relu')) model.add(MaxPooling2D(pool_size=2, strides=2, padding='valid')) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(y_train.shape[1], activation='softmax')) model.compile(optimizer='adam', loss="categorical_crossentropy", metrics=['accuracy']) model.fit(x_train, y_train, epochs=20, validation_data=(x_test, y_test)) model.save("cnn.h5") img = cv2.imread("D:/C Moving Destination/Documents/Tencent Files/2451360621/FileRecv/vggface/n000009/0001_01.jpg") img = cv2.resize(img, (150, 150)) img = img[:, :, ::-1] img = np.reshape(img, (1, img.shape[0], img.shape[1], img.shape[2])) print(dirs[np.argmax(model.predict(img))])