import tensorflow as tf

weights = tf.Variable(tf.random_normal([784, 200]), stddev=0.35, name='weights')
biases = tf.Variable(tf.zeros([200]), name = 'biases')


#Variable initializers must be run explicitly before other ops in your model can be run
#The best way is to run a op to initialize all the variables initializers, and run that
#OP before using the model

#add an op to initialize the variables
init_op = tf.global_variables_initializer()

#Create another variable with the same value as 'weights'

w2 = tf.Variable(weights.initialized_value(), name = 'w2')
with tf.Session() as sess:
    sess.run(init_op)

# Use saver with tf.train.Saver() to manage all variables in the model
# use saver with tf.train.Saver() to manage all variables in the model
saver = tf.train.Saver()

v1 = tf.Variable(tf.random_normal([88,100]), stddev=0.35, mean=0.3, name='weights')

init_op = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init_op)
    save_path = saver.save(sess, "")


#share variables
tf.variable_scope()
tf.get_variable()

v = tf.get_variable(name, shape, dtype, initializer=)
