Recently I want to use DenseNet for feature extraction, but I don't know the specific structure of DenseNet, so I did a visualization of DenseNet structure.
# -*- coding: utf-8 -*-
"""
Created on Tue Feb 19 13:35:11 2019
@author: 13260
"""
from keras.applications.densenet import DenseNet201,preprocess_input
from keras.models import Model,load_model
import numpy as np
from keras.layers import Dense, GlobalAveragePooling2D
from keras.preprocessing import image
#base_model = DenseNet(weights='imagenet', include_top=False)
base_model = DenseNet201(weights='imagenet', include_top=False)
#base_model = load_model("F:/python/python-GenerRec/src/1019Resnet_gener_model_weights.h5")
#base_model.get_layer()
model = Model(inputs=base_model.input, outputs=base_model.output)
model.summary()
print ('the number of layers in this model:' + str (len (model.layers)))
code running results are shown in the figure:
