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import os
import tensorflow as tf
import matplotlib . pyplot as plt
from tensorflow . keras . models import load_model
import numpy as np
# 抑制tensorflow，
config = tf . compat . v1 . ConfigProto ( gpu_options = tf . compat . v1 . GPUOptions ( allow_growth = True ) )
sess = tf . compat . v1 . Session ( config = config )
checkpoint_path = " ./training_2/cp.ckpt "
model_path = " ./myModel/myModel.h5 "
# 读取手写数字数据集
num_mnist = tf . keras . datasets . mnist
( train_images , train_labels ) , ( test_images , test_labels ) = num_mnist . load_data ( )
# 读取训练好点模型
model = load_model ( model_path )
# 打印网络结构
model . summary ( )
# 评估
model . evaluate ( test_images . reshape ( - 1 , 28 , 28 , 1 ) , test_labels )
# 可视化预测效果
testShow = test_labels [ : 100 ]
pred = model . predict ( test_images . reshape ( - 1 , 28 , 28 , 1 ) )
predict = [ ]
for item in pred :
predict . append ( np . argmax ( item ) )
plt . figure ( )
plt . title ( ' Conv Predict ' )
plt . ylabel ( ' number ' )
plt . plot ( range ( testShow . size ) , predict [ : 100 ] , label = ' predict ' )
plt . plot ( range ( testShow . size ) , testShow , label = ' result ' )
plt . legend ( )
plt . show ( )
