（接上文）
為了對神經網路的分類（擬合）效果進行測試，我們可以使用另一組訓練樣本，進行試分類，評價其代價函式的收斂程度。

1. 模型測試

該測試程式讀取測試資料，並應用當前訓練好的模型，進行分類，計算代價函式。如果模型奇異，則代價函式相較訓練集會較高，反之，較低（一致）：
執行結果：

Testing...
1024 0.0035852
2048 0.00231017
3072 0.00157589
4096 0.00172059
5120 0.00321012
6144 0.00346273
7168 0.00267906
8192 0.00247223
9216 0.00233935
10240 0.00288214
11264 0.002231
12288 0.00120241
 

測試程式的完整程式碼：

# -*- coding: utf-8 -*-
"""
Created on Sun Nov 26 15:24:50 2017
gn_test_model.py
@author: goldenhawking
"""
from __future__ import print_function
import tensorflow as tf
import numpy as np
import configparser
import re
import matplotlib.pyplot as mpl
trainning_task_file         = 'train_task.cfg'
 

testing_file                = 'test_set.txt'
model_path                  = './saved_model/'
#讀取配置
config = configparser.ConfigParser()
config.read(trainning_task_file)
n               = int(config['network']['input_nodes'])     # input vector size
K               = int(config['network']['output_nodes'
 
])     # output vector size
lam             = float(config['network']['lambda'])
#隱層規模 用逗號分開,類似 ”16,16,13“ 
hidden_layer_size = config['network']['hidden_layer_size'] 
#分離字元
reobj = re.compile('[\s,\"]')
ls_array        = reobj.split(hidden_layer_size);
ls_array        = [item for item in filter(lambda x:x != '', ls_array)] #刪空白
#隱層個數
hidden_layer_elems =  len(ls_array);

#轉為整形，並計入輸出層 
ns_array = []
for idx in range(0,hidden_layer_elems)    :
    ns_array.append(int(ls_array[idx]))
#Output is the last layer, append to last
ns_array.append(K)
#總層數（含有輸出層）
total_layer_size = len(ns_array)
#--------------------------------------------------------------
#create graph
graph = tf.Graph()
with graph.as_default():
    with tf.name_scope('network'):
        with tf.name_scope('input'):
            s = [n]
            a = [tf.placeholder(tf.float32,[None,s[0]],name="in")]
            W = []
            b = []
            z = []
            punish = tf.constant(0.0)
            for idx in range(0,total_layer_size)    :
                with tf.name_scope('layer'+str(idx+1)):
                    s.append(int(ns_array[idx]))
                    W.append(tf.Variable(tf.random_uniform([s[idx],s[idx+1]],0,1),name='W'+str(idx+1)))
                    b.append(tf.Variable(tf.random_uniform([1],0,1),name='b'+str(idx+1)))
                    z.append(tf.matmul(a[idx],W[idx]) + b[idx]*tf.ones([1,s[idx+1]],name='z'+str(idx+1)))
                    a.append(tf.nn.tanh(z[idx],name='a'+str(idx+1)))
                with tf.name_scope('regular'):
                    punish = punish + tf.reduce_sum(W[idx]**2) * lam

    #--------------------------------------------------------------
    with tf.name_scope('loss'):
        y_ = tf.placeholder(tf.float32,[None,K],name="tr_out")
        loss = tf.reduce_mean(tf.square(a[total_layer_size]-y_),name="loss") + punish
    with tf.name_scope('trainning'):
        optimizer = tf.train.AdamOptimizer(name="opt")
        train = optimizer.minimize(loss,name="train")

    init = tf.global_variables_initializer()
    #save graph to Disk
    saver = tf.train.Saver()
#--------------------------------------------------------------
### create tensorflow structure end ###
sess = tf.Session(graph=graph)
check_point_path = model_path # 儲存好模型的檔案路徑
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=check_point_path)
saver.restore(sess,ckpt.model_checkpoint_path)

#--------------------------------------------------------------
file_deal_times = int(config['performance']['file_deal_times'])
trunk           = int(config['performance']['trunk'])
train_step      = int(config['performance']['train_step'])
iterate_times   = int(config['performance']['iterate_times'])
print ("Testing...")
#testing
x_test = np.zeros([trunk,n]).astype(np.float32)
#read n features and K outputs
y_test = np.zeros([trunk,K]).astype(np.float32)
total_red = 0

plot_x = []
plot_y = []

with open(testing_file, 'rt') as testfile:
    while 1:
        lines = testfile.readlines()
        if not lines:
            break
        line_count = len(lines)
        for lct in range(line_count):
            x_arr = reobj.split(lines[lct]);
            x_arr = [item for item in filter(lambda x:x != '', x_arr)] #remove null strings
            for idx in range(n)    :
                x_test[total_red % trunk,idx] = float(x_arr[idx])
            for idx in range(K)    :    
                y_test[total_red % trunk,idx] = float(x_arr[idx+n])           
            total_red = total_red + 1
            #the trainning set run trainning
            if (total_red % train_step == 0):
                #print loss
                lss = sess.run(loss,feed_dict={a[0]:x_test[0:min(total_red,trunk)+1],y_:y_test[0:min(total_red,trunk)+1]})
                print(total_red,lss)
                plot_x.append(total_red)
                plot_y.append(lss)

mpl.plot(plot_x,plot_y)

2. 模型應用

下面這個程式，讀取給定的特徵，產生分類結果。我們把分類器的輸出，存為一個文字檔案。
這個文字檔案每一行為一個結果，由兩部分組成，特徵、分類（或者擬合）結果。

[-0.24751600623130798, -0.9268109798431396] [0.9986907243728638, -0.000654876115731895, -0.00044381615589372814]
[0.045763999223709106, 0.5164780020713806] [0.9986994862556458, -0.0026147901080548763, -0.001965639414265752]
[-0.6250460147857666, -0.8338379859924316] [-0.00046735999058000743, -0.0015115130227059126, 0.9921404719352722]
[0.6993309855461121, -0.042775001376867294] [0.9986986517906189, -0.0005539059056900442, -0.00046229359577409923]
[0.9839800000190735, 0.19465599954128265] [0.9986998438835144, -0.0009445545147173107, -0.0008026955765672028]
[-0.12072400003671646, 0.5291630029678345] [0.9986990690231323, 6.365776062011719e-05, -4.45246696472168e-05]
[0.11185800284147263, 0.20474199950695038] [0.9986990690231323, -0.00044244524906389415, -0.0004038810438942164]

可以使用最大值判決，來對輸出的浮點型判決結果進行分類。同時，通過比值，可以看出分類的區分度。

附帶原始碼：

# -*- coding: utf-8 -*-
"""
Created on Sun Nov 26 15:24:50 2017
gn_run_model.py
@author: goldenhawking
"""
from __future__ import print_function
import tensorflow as tf
import numpy as np
import configparser
import re
import matplotlib.pyplot as mpl
trainning_task_file         = 'train_task.cfg'
input_file                  = 'test_set.txt'
output_file                 = 'result.txt'
model_path                  = './saved_model/'
#讀取配置
config = configparser.ConfigParser()
config.read(trainning_task_file)
n               = int(config['network']['input_nodes'])     # input vector size
K               = int(config['network']['output_nodes'])     # output vector size
lam             = float(config['network']['lambda'])
#隱層規模 用逗號分開,類似 ”16,16,13“ 
hidden_layer_size = config['network']['hidden_layer_size'] 
#分離字元
reobj = re.compile('[\s,\"]')
ls_array        = reobj.split(hidden_layer_size);
ls_array        = [item for item in filter(lambda x:x != '', ls_array)] #刪空白
#隱層個數
hidden_layer_elems =  len(ls_array);

#轉為整形，並計入輸出層 
ns_array = []
for idx in range(0,hidden_layer_elems)    :
    ns_array.append(int(ls_array[idx]))
#Output is the last layer, append to last
ns_array.append(K)
#總層數（含有輸出層）
total_layer_size = len(ns_array)
#--------------------------------------------------------------
#create graph
graph = tf.Graph()
with graph.as_default():
    with tf.name_scope('network'):
        with tf.name_scope('input'):
            s = [n]
            a = [tf.placeholder(tf.float32,[None,s[0]],name="in")]
            W = []
            b = []
            z = []
            punish = tf.constant(0.0)
            for idx in range(0,total_layer_size)    :
                with tf.name_scope('layer'+str(idx+1)):
                    s.append(int(ns_array[idx]))
                    W.append(tf.Variable(tf.random_uniform([s[idx],s[idx+1]],0,1),name='W'+str(idx+1)))
                    b.append(tf.Variable(tf.random_uniform([1],0,1),name='b'+str(idx+1)))
                    z.append(tf.matmul(a[idx],W[idx]) + b[idx]*tf.ones([1,s[idx+1]],name='z'+str(idx+1)))
                    a.append(tf.nn.tanh(z[idx],name='a'+str(idx+1)))
                with tf.name_scope('regular'):
                    punish = punish + tf.reduce_sum(W[idx]**2) * lam

    #--------------------------------------------------------------
    with tf.name_scope('loss'):
        y_ = tf.placeholder(tf.float32,[None,K],name="tr_out")
        loss = tf.reduce_mean(tf.square(a[total_layer_size]-y_),name="loss") + punish
    with tf.name_scope('trainning'):
        optimizer = tf.train.AdamOptimizer(name="opt")
        train = optimizer.minimize(loss,name="train")

    init = tf.global_variables_initializer()
    #save graph to Disk
    saver = tf.train.Saver()
#--------------------------------------------------------------
### create tensorflow structure end ###
sess = tf.Session(graph=graph)
check_point_path = model_path # 儲存好模型的檔案路徑
ckpt = tf.train.get_checkpoint_state(checkpoint_dir=check_point_path)
saver.restore(sess,ckpt.model_checkpoint_path)

#--------------------------------------------------------------
print ("Running...")
with open(input_file, 'rt') as testfile:
    with open(output_file, 'wt') as resultfile:    
        while 1:
            lines = testfile.readlines()
            if not lines:
                break
            line_count = len(lines)
            x_test = np.zeros([line_count,n]).astype(np.float32)
            for lct in range(line_count):
                x_arr = reobj.split(lines[lct]);
                x_arr = [item for item in filter(lambda x:x != '', x_arr)] #remove null strings
                for idx in range(n)    :
                    x_test[lct,idx] = float(x_arr[idx])
            #the trainning set run trainning
            result = sess.run(a[total_layer_size],feed_dict={a[0]:x_test})
            for idx in range(line_count):
                print(x_test[idx].tolist(),result[idx].tolist(),file = resultfile)

mpl.plot(x_test[result[:,1]>=0.9,0],x_test[result[:,1]>=0.9,1],'b.');
mpl.plot(x_test[result[:,2]>=0.9,0],x_test[result[:,2]>=0.9,1],'r.');
mpl.plot(x_test[result[:,0]>=0.9,0],x_test[result[:,0]>=0.9,1],'g.');