import tensorflow as tf

    def get_fc_var(self, in_size, out_size, name):
    	"""
    	in_size : number of input feature size
    	out_size : number of output feature size
    	name : block_layer name
    	"""
    	initial_value = tf.truncated_normal([in_size, out_size], 0.0, stddev = 1 / math.sqrt(float(in_size)))
    	weights = self.get_var(initial_value, name, 0, name + "_weights")

    	initial_value = tf.truncated_normal([out_size], 0.0, 1.0)
    	biases = self.get_var(initial_value, name, 1, name + "_biases")

    	return weights, biases


    def get_var(self, initial_value, name, idx, var_name):
        if self.data_dict is not None and idx in self.data_dict[name]:
            value = self.data_dict[name][idx]
