【keras】載入VGG16模型的預訓練權重
阿新 • • 發佈:2019-01-10
#!/usr/bin/python3 # -*- coding: utf-8 -*- # @Time: 2018/8/15 # @Author: xfLi #載入模型的預訓練權重 import numpy as np from keras.applications.vgg16 import VGG16 from keras.models import Model from keras.preprocessing import image from keras.applications.vgg16 import preprocess_input #載入VGG16預訓練 base_model = VGG16(weights='imagenet', include_top=True) for i, layer in enumerate(base_model.layers): print(i, layer.name, layer.output_shape) model = Model(inputs=base_model.inputs, outputs=base_model.get_layer('block4_pool').output) img_path = 'cat,jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) features = model.predict(x) print(features)