使用Keras训练好的.h5模型来测试一个实例

import cv2
import numpy as np
from keras.models import load_model

model = load_model('fm_cnn_BN.h5') #选取自己的.h模型名称
image = cv2.imread('6_b.png')
img = cv2.cvtColor(image,cv2.COLOR_RGB2GRAY) # RGB图像转为gray

#需要用reshape定义出例子的个数，图片的 通道数，图片的长与宽。具体的参加keras文档
img = (img.reshape(1, 1, 28, 28)).astype('int32')/255 
predict = model.predict_classes(img)
print ('识别为：')
print (predict)

cv2.imshow("Image1", image)
cv2.waitKey(0)

python keras_to_tensorflow.py 
 --input_model="path/to/keras/model.h5" 
 --output_model="path/to/save/model.pb"

begin====================================================
I1229 14:29:44.819010 140709034264384 keras_to_tf.py:119] Input nodes names are: [u'input_1']
I1229 14:29:44.819385 140709034264384 keras_to_tf.py:137] Converted output node names are: [u'dense_2/Sigmoid']
INFO:tensorflow:Froze 322 variables.
I1229 14:29:47.091161 140709034264384 tf_logging.py:82] Froze 322 variables.
Converted 322 variables to const ops.
I1229 14:29:48.504235 140709034264384 keras_to_tf.py:170] Saved the freezed graph at /path/to/save/model.pb

#!/usr/bin/env python
# -*- coding: utf-8 -*-
import tensorflow as tf
import numpy as np
from tensorflow.python.platform import gfile
import cv2
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
 
# img = cv2.imread(os.path.expanduser('/test_imgs/img_1.png'))
# img = cv2.resize(img, dsize=(1000, 1000), interpolation=cv2.INTER_LINEAR)
# img = img.astype(float)
# img /= 255
# img = np.array([img])
 
# 初始化TensorFlow的session
with tf.Session() as sess:
 # 读取得到的pb文件加载模型
 with gfile.FastGFile("/path/to/save/model.pb",'rb') as f:
 graph_def = tf.GraphDef()
 graph_def.ParseFromString(f.read())
 # 把图加到session中
 tf.import_graph_def(graph_def, name='')
 
 # 获取当前计算图
 graph = tf.get_default_graph()
 
 # 从图中获输出那一层
 pred = graph.get_tensor_by_name("dense_2/Sigmoid:0")
 
 # 运行并预测输入的img
 res = sess.run(pred, feed_dict={"input_1:0": img})
 
 # 执行得到结果
 pred_index = res[0][0]
 print('Predict:', pred_index)