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關(guān)于tensorflow和keras版本的對應(yīng)關(guān)系

更新時間：2023年06月25日 10:16:01 作者：StarkerRegen

這篇文章主要介紹了關(guān)于tensorflow和keras版本的對應(yīng)關(guān)系，具有很好的參考價值，希望對大家有所幫助。如有錯誤或未考慮完全的地方，望不吝賜教

tensorflow和keras版本對應(yīng)關(guān)系

Tensorflow版本	Keras版本
Tensorflow 2.1 Tensorflow 2.0 Tensorflow 1.15	Keras 2.3.1
Tensorflow 1.14	Keras 2.2.5
Tensorflow 1.13 Tensorflow 1.12 Tensorflow 1.11	Keras 2.2.4
Tensorflow 1.10 Tensorflow 1.9	Keras 2.2.0
Tensorflow 1.8 Tensorflow 1.7 Tensorflow 1.5	Keras 2.1.6
Tensorflow 1.4	Keras 2.0.8
Tensorflow 1.3 Tensorflow 1.2 Tensorflow 1.1 Tensorflow 1.0	Keras 2.0.6
Tensorflow 0.12	Keras 1.2.2

tensorflow與keras混用之坑

在使用tensorflow與keras混用是model.save 是正常的但是在load_model的時候報錯了在這里mark 一下

其中錯誤為：TypeError: tuple indices must be integers, not list

再一一番百度后無結(jié)果，上谷歌后找到了類似的問題。但是是一對鳥文不知道什么東西（翻譯后發(fā)現(xiàn)是俄文）。后來谷歌翻譯了一下找到了解決方法。

故將原始問題文章貼上來警示一下

原訓(xùn)練代碼

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!")

模型載入

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!")

報錯

/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

戰(zhàn)斗種族解釋

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.（譯文：整理BatchNormalization一切正常。不要告訴我錯誤是什么？我發(fā)現(xiàn)保存keras和規(guī)范化tensorflow不能一起工作;只需更改導(dǎo)入字符串即可。)

強調(diào)文本強調(diào)文本

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

##完美解決