訓練されたH 5ファイルをkersでロードし、予測画像を実現します。

import matplotlib
matplotlib.use('Agg')
import os
from keras.models import load_model
import numpy as np
from PIL import Image
import cv2
#    h5  
model = load_model("C:\\python\\python3_projects\\cat_dog\\cats_dogs_fifty_thousand.h5")
model.summary()
#           
def get_inputs(src=[]):
  pre_x = []
  for s in src:
    input = cv2.imread(s)
    input = cv2.resize(input, (150, 150))
    input = cv2.cvtColor(input, cv2.COLOR_BGR2RGB)
    pre_x.append(input) # input    
  pre_x = np.array(pre_x) / 255.0
  return pre_x
#           
predict_dir = 'C:\python\python3_projects\cat_dog\pics'
#          ，   cat dog
test = os.listdir(predict_dir)
#   ：['cat', 'dog']
print(test)
#               
images = []
#         ，      images 
for testpath in test:
  for fn in os.listdir(os.path.join(predict_dir, testpath)):
    if fn.endswith('jpg'):
      fd = os.path.join(predict_dir, testpath, fn)
      print(fd)
      images.append(fd)
#    ，     
pre_x = get_inputs(images)
#  
pre_y = model.predict(pre_x)
print(pre_y)