How can I know if my NN TensorFlow model is overfitted or not? I am new with TensorFlow (Python) and I can not juge my obtained results in terms of training and testing accuracy 

I am using the GradientDescentOptimizer with a learning coeff equal to 10^(-4) and I have executed the following code :

for gg  in range (1501):
    session.run(optimizer, feed_dict=train_data)
    train_accuracy = session.run(accuracy, feed_dict=train_data)
    if gg % 100 ==0 :
        #print(train_accuracy)
        session.run(optimizer, feed_dict=test_data)
        test_accuracy = session.run(accuracy, feed_dict=test_data)
        print(test_accuracy)  


One time I have commented the testing accuracy to print only the training accuracy and one time I did the contrary. 
I had stored them in the following table: 
   

My problem is that I am not able to detect overfitting if it exists or not
Can you help me please!
Short answer, no it does not look like you are overfitting. Your validation (you call this test) accuracy seems to follow your training accuracy quite closely. 

Easiest way to check for yourself is to plot the training and validation accuracy over each iteration, then look at how the curves behave. What you want to see is a test curve that closely follows your validation curve. 

Overfitting can come in too ways, your validation curve being quite a bit lower than your training curve and/or a validation curve that is decreasing when the training curve is increasing.

Both these behaviors can be seen in this picture:



Image taken from Stanford's CS231n

Edit: As gunes points out you seem to have a programming error that should be addressed before any checks for overfitting.