During the run of the computational graph, we have to tell TensorFlow when to initialize the variables we have created. While each variable has an initializer method, the most common way to do this is with the helper function, that is, global_variables_initializer(). This function creates an operation in the graph that initializes all the variables we have created, as follows:
initializer_op = tf.global_variables_initializer()
But if we want to initialize a variable based on the results of initializing another variable, we have to initialize variables in the order we want, as follows:
sess = tf.Session() first_var = tf.Variable(tf.zeros([2,3])) sess.run(first_var.initializer) second_var = tf.Variable(tf.zeros_like(first_var)) ...
