ResNet50从keras给出了不同的预测结果和输出结果

Question

我想微调来自Keras的ResNet50，但是首先我发现给定相同的输入，ResNet50的预测与模型的输出不同。实际上，产出的价值似乎是“随机的”。我究竟做错了什么？

在此先感谢！

这是我的代码：

import tensorflow as tf 
from resnet50 import ResNet50 
from keras.preprocessing import image 
from imagenet_utils import preprocess_input 
import numpy as np 
from keras import backend as K 

img_path = 'images/tennis_ball.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_image = preprocess_input(x) 

#Basic prediction 
model_basic = ResNet50(weights='imagenet', include_top=False) 
x_prediction = model_basic.predict(x_image) 

#Using tensorflow to obtain the output 
input_tensor = tf.placeholder(tf.float32, shape=[None, 224,224, 3], name='input_tensor') 
model = ResNet50(weights='imagenet', include_top=False, input_tensor=input_tensor) 
x = model.output 

# Tensorflow session 
session = tf.Session() 
session.run(tf.global_variables_initializer()) 
K.set_session(session) 
feed_dict = {input_tensor: x_image, K.learning_phase(): 0} 

# Obatin the output given the same input 
x_output = session.run(x, feed_dict=feed_dict) 

# Different results 
print('Value of the prediction: {}'.format(x_prediction)) 
print('Value of the output: {}'.format(x_output)) 

这是日志的例子：

Value of the prediction: [[[[ 1.26408589e+00 3.91489342e-02 8.43058806e-03 ..., 
     5.63185453e+00 4.49339962e+00 5.13037841e-04]]]] 
Value of the output: [[[[ 2.62883282 2.20199227 9.46755123 ..., 1.24660134 1.98682189 
    0.63490123]]]] 

Answer 1

问题是session.run(tf.global_variables_initializer())初始化参数以随机值。 该问题解决通过使用：

session = K.get_session() 

代替：

session = tf.Session() 
session.run(tf.global_variables_initializer())