ResNet50 From keras gives different results for predict and output

Question

I want to fine-tune the ResNet50 from Keras but first I found that given the same input, the prediction from ResNet50 is different from the output of the model. Actually, the value of the output seems to be 'random'. What am I doing wrong?

Thanks in advance!

Here it is my code:

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))

Here it is an example of the logs:

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

The problem was that session.run(tf.global_variables_initializer()) initializes your parameters to random values. The problem was solve by using:

session = K.get_session()

instead of:

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