Multi tool use

why keras model param values change when it is accessed in a tensorflow session?

I was having trouble with my transfer learning implementation. I guess I found the root cause but it is not clear to me why it works like that. Here is the explanation...

If I create a model (e.g. resnet50 from keras.applications), and then try to use it in a tensorflow session, weights all of a sudden change.
Here is a simple example:

First import the necessary libraries:

import tensorflow as tf
from keras.applications.resnet50 import ResNet50
from keras.models import Model


Then define the model as following:

model = ResNet50(weights='imagenet')


Now print out parameters from one of the layers as following:

model.get_layer('conv1').get_weights()


The output is long but it starts as following:

[array([[[[ 2.82526277e-02, -1.18737184e-02, 1.51488732e-03, ...,
-1.07003953e-02, -5.27982824e-02, -1.36667420e-03],
[ 5.86827798e-03, 5.04415408e-02, 3.46324709e-03, ...,
1.01423981e-02, 1.39493728e-02, 1.67549420e-02],
[-2.44090753e-03, -4.86173332e-02, 2.69966386e-03, ...,
-3.44439060e-04, 3.48098315e-02, 6.28910400e-03]],


Later in the program, I need to read some data from csv files. I try to read the data by using the dataset API from tensorflow. For this purpose, I create a tensorflow session. And if I want to use the model within the tensorflow session, I see that model parameters change.

Here is a code example:

init_global_var = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init_global_var)
print(model.get_layer('conv1').get_weights())


And the output starts as following:

[array([[[[ 3.95432524e-02, -2.38095019e-02, -1.64129660e-02, ...,
-2.83494107e-02, 2.25975104e-02, -1.48569904e-02],
[ 2.40861587e-02, 1.48933977e-02, -4.10864130e-02, ...,
-3.18703875e-02, -9.43836942e-03, 1.18204653e-02],
[ 2.99405716e-02, 1.69009715e-03, -1.43084712e-02, ...,
-2.93575712e-02, 2.70796008e-02, -3.17203328e-02]],


Since I have not trained the model yet, I expect to see the same parameter values but they are not the same!

So the question is: Do I have to create my model within the tensorflow session? Why is my resnet50 model accessible but with different parameter values in the tensorflow session?

save_weights

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