解决tensorflow 与keras 混用之坑

from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
 
#Каталог с данными для обучения
train_dir = 'train'
# Каталог с данными для проверки
val_dir = 'val'
# Каталог с данными для тестирования
test_dir = 'val'
 
# Размеры изображения
img_width, img_height = 800, 800
# Размерность тензора на основе изображения для входных данных в нейронную сеть
# backend Tensorflow, channels_last
input_shape = (img_width, img_height, 3)
# Количество эпох
epochs = 1
# Размер мини-выборки
batch_size = 4
# Количество изображений для обучения
nb_train_samples = 300
# Количество изображений для проверки
nb_validation_samples = 25
# Количество изображений для тестирования
nb_test_samples = 25
 
model = Sequential()
 
model.add(Conv2D(32, (7, 7), padding="same", input_shape=input_shape))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(10, 10)))
 
model.add(Conv2D(64, (5, 5), padding="same"))
model.add(BatchNormalization())
model.add(Activation('tanh'))
model.add(MaxPooling2D(pool_size=(10, 10)))
 
model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(10, activation='softmax'))
 
model.compile(loss='categorical_crossentropy',
  optimizer="Nadam",
  metrics=['accuracy'])
print(model.summary())
datagen = ImageDataGenerator(rescale=1. / 255)
 
train_generator = datagen.flow_from_directory(
 train_dir,
 target_size=(img_width, img_height),
 batch_size=batch_size,
 class_mode='categorical')
 
val_generator = datagen.flow_from_directory(
 val_dir,
 target_size=(img_width, img_height),
 batch_size=batch_size,
 class_mode='categorical')
 
test_generator = datagen.flow_from_directory(
 test_dir,
 target_size=(img_width, img_height),
 batch_size=batch_size,
 class_mode='categorical')
 
model.fit_generator(
 train_generator,
 steps_per_epoch=nb_train_samples // batch_size,
 epochs=epochs,
 validation_data=val_generator,
 validation_steps=nb_validation_samples // batch_size)
 
print('Сохраняем сеть')
 
model.save("grib.h5")
print("Сохранение завершено!")

from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Conv2D, MaxPooling2D, BatchNormalization
from tensorflow.python.keras.layers import Activation, Dropout, Flatten, Dense
from keras.models import load_model
 
print("Загрузка сети")
model = load_model("grib.h5")
print("Загрузка завершена!")

keras.preprocessing.image import ImageDataGenerator
keras.models import Sequential
keras.layers import Conv2D, MaxPooling2D, BatchNormalization
keras.layers import Activation, Dropout, Flatten, Dense

sess.run(tf.global_variables_initializer())

import cv2
import numpy as np
from keras.models import load_model
from utils.datasets import get_labels
from utils.preprocessor import preprocess_input
import time
import os
import tensorflow as tf
from tensorflow.python.platform import gfile
 
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
emotion_labels = get_labels('fer2013')
emotion_target_size = (64,64)
#emotion_model_path = './models/emotion_model.hdf5'
#emotion_classifier = load_model(emotion_model_path)
#emotion_target_size = emotion_classifier.input_shape[1:3]
 
path = '/mnt/nas/cv_data/emotion/test'
filelist = os.listdir(path)
total_num = len(filelist)
timeall = 0
n = 0
 
sess = tf.Session()
#sess.run(tf.global_variables_initializer())
with gfile.FastGFile("./trans_model/emotion_mode.pb", 'rb') as f:
 graph_def = tf.GraphDef()
 graph_def.ParseFromString(f.read())
 sess.graph.as_default()
 tf.import_graph_def(graph_def, name='')
 
 pred = sess.graph.get_tensor_by_name("predictions/Softmax:0")
 
 ######################img##########################
 for item in filelist:
  if (item == '.DS_Store') | (item == 'Thumbs.db'):
continue
  src = os.path.join(os.path.abspath(path), item)
  bgr_image = cv2.imread(src)
  gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
  gray_face = gray_image
  try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
  except:
continue
 
  gray_face = preprocess_input(gray_face, True)
  gray_face = np.expand_dims(gray_face, 0)
  gray_face = np.expand_dims(gray_face, -1)
 
  input = sess.graph.get_tensor_by_name('input_1:0')
  res = sess.run(pred, {input: gray_face})
  print("src:", src)
 
  emotion_probability = np.max(res[0])
  emotion_label_arg = np.argmax(res[0])
  emotion_text = emotion_labels[emotion_label_arg]
  print("predict:", res[0], ",prob:", emotion_probability, ",label:", emotion_label_arg, ",text:",emotion_text)