解决tensorflow 与keras 混用之坑_Python

在使用tensorflow与keras混用是model.save 是正常的但是在load_model的时候报错了在这里mark 一下

其中错误为：TypeError: tuple indices must be integers, not list

再一一番百度后无结果，上谷歌后找到了类似的问题。但是是一对鸟文不知道什么东西（翻译后发现是俄文）。后来谷歌翻译了一下找到了解决方法。故将原始问题文章贴上来警示一下

原训练代码

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									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!")

模型载入

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									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!")

报错

/usr/bin/python3.5 /home/disk2/py/neroset/do.py
/home/mama/.local/lib/python3.5/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
from ._conv import register_converters as _register_converters
Using TensorFlow backend.
Загрузка сети
Traceback (most recent call last):
File "/home/disk2/py/neroset/do.py", line 13, in <module>
model = load_model("grib.h5")
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 243, in load_model
model = model_from_config(model_config, custom_objects=custom_objects)
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 317, in model_from_config
return layer_module.deserialize(config, custom_objects=custom_objects)
File "/usr/local/lib/python3.5/dist-packages/keras/layers/__init__.py", line 55, in deserialize
printable_module_name='layer')
File "/usr/local/lib/python3.5/dist-packages/keras/utils/generic_utils.py", line 144, in deserialize_keras_object
list(custom_objects.items())))
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 1350, in from_config
model.add(layer)
File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 492, in add
output_tensor = layer(self.outputs[0])
File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 590, in __call__
self.build(input_shapes[0])
File "/usr/local/lib/python3.5/dist-packages/keras/layers/normalization.py", line 92, in build
dim = input_shape[self.axis]
TypeError: tuple indices must be integers or slices, not list

Process finished with exit code 1

战斗种族解释

убераю BatchNormalization всё работает хорошо. Не подскажите в чём ошибка?Выяснил что сохранение keras и нормализация tensorflow не работают вместе нужно просто изменить строку импорта.（译文：整理BatchNormalization一切正常。不要告诉我错误是什么？我发现保存keras和规范化tensorflow不能一起工作;只需更改导入字符串即可。)

强调文本强调文本

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									keras.preprocessing.image import ImageDataGenerator

									keras.models import Sequential

									keras.layers import Conv2D, MaxPooling2D, BatchNormalization

									keras.layers import Activation, Dropout, Flatten, Dense

##完美解决

##附上原文链接

https://qa-help.ru/questions/keras-batchnormalization

补充：keras和tensorflow模型同时读取要慎重

项目中，先读取了一个keras模型获取模型输入size，再加载keras转tensorflow后的pb模型进行预测。

报错：

Attempting to use uninitialized value batch_normalization_14/moving_mean

逛论坛，有建议加上初始化：

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									sess.run(tf.global_variables_initializer())

但是这样的话，会导致模型参数全部变成初始化数据。无法使用预测模型参数。

最后发现，将keras模型的加载去掉即可。

猜测原因：keras模型和tensorflow模型同时读取有坑

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									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)