marked rv_stage1_out as not trainable - smaller model size

If collection is not provided, it defaults to  [GraphKeys.GLOBAL_VARIABLES], and that in turn fails saver.restore() as this variable was not available in the trained model

"""

'''

#rv_stage1_out = resource_variable_ops.ResourceVariable(

rv_stage1_out = tf.Variable(

    np.zeros([HEIGHT * WIDTH, NN_LAYOUT1[-1]]),

    collections = [GraphKeys.LOCAL_VARIABLES],# Works, available with tf.local_variables()

    dtype=np.float32,

    name = 'rv_stage1_out')

'''

rv_stage1_out = tf.get_variable("rv_stage1_out",

                                shape=[HEIGHT * WIDTH, NN_LAYOUT1[-1]],

                                dtype=tf.float32,

                                initializer=tf.zeros_initializer,

                                collections = [GraphKeys.LOCAL_VARIABLES],trainable=False)

'''

#rv_stageX_out_init_placeholder = tf.placeholder(tf.float32, shape=[HEIGHT * WIDTH, NN_LAYOUT1[-1]])

#rv_stageX_out_init_op = rv_stageX_out.assign(rv_stageX_out_init_placeholder)

        that do not have this variable, add it to globals.

        Actually it could have been declared right here - this

        needs testing.

        NOTE: The line below makes the next script's, that saves 

        a Saved_Model MetaGraph, size of the Saved_Model significantly 

        bigger.

    '''

    tf.add_to_collection(GraphKeys.GLOBAL_VARIABLES, rv_stage1_out)