[TensorFlow] 기초 개념 및 수행 결과 _ 4. 저장한 weight를 restore 해서 사용하기

import tensorflow as tf

input_data = [[1,5,3,7,8,10,12],

              [5,8,10,3,9,7,1]]

label_data = [[0,0,0,1,0],

              [1,0,0,0,0]]

​

INPUT_SIZE = 7 

HIDDEN_SIZE_1 = 10

HIDDEN_SIZE_2 = 8

CLASSES = 5

x = tf.placeholder(tf.float32, shape = [None, INPUT_SIZE], name="x" )

y = tf.placeholder(tf.float32, shape = [None, CLASSES], name="y" )

tensor_map = {x : input_data, y : label_data}

weight_hidden_1 = tf.Variable( tf.truncated_normal(shape=[INPUT_SIZE, HIDDEN_SIZE_1]) , dtype=tf.float32, name="weight_hidden_1" ) 

bias_hidden_1 = tf.Variable( tf.zeros(shape=[HIDDEN_SIZE_1]) , dtype=tf.float32, name = "bias_hidden_1" )

​

weight_hidden_2 = tf.Variable( tf.truncated_normal(shape=[HIDDEN_SIZE_1, HIDDEN_SIZE_2]) , dtype=tf.float32, name="weight_hidden_2" ) 

bias_hidden_2 = tf.Variable( tf.zeros(shape=[HIDDEN_SIZE_2]) , dtype=tf.float32, name = "bias_hidden_2" )

​

weight_output = tf.Variable( tf.truncated_normal(shape=[HIDDEN_SIZE_2, CLASSES]) , dtype=tf.float32, name="weight_output" ) 

bias_output = tf.Variable( tf.zeros(shape=[CLASSES]) , dtype=tf.float32, name = "bias_output" )

param_list = [weight_hidden_1, bias_hidden_1, weight_hidden_2, bias_hidden_2, weight_output, bias_output ]

saver = tf.train.Saver(param_list)

hidden_1 = tf.matmul( x , weight_hidden_1 , name="hidden_1" ) + bias_hidden_1

hidden_2 = tf.matmul( hidden_1 , weight_hidden_2, name="hidden_2" ) + bias_hidden_2

answer = tf.matmul( hidden_2 , weight_output, name="answer" ) + bias_output

sess = tf.Session() 

# init = tf.global_variables_initializer()

# sess.run(init) 

saver.restore(sess, './tensorflow_live.ckpt')

result = sess.run(y , tensor_map)

print (result)