解決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
##附上原文鏈接
https://qa-help.ru/questions/keras-batchnormalization
補充：keras和tensorflow模型同時讀取要慎重
項目中，先讀取了一個keras模型獲取模型輸入size，再加載keras轉(zhuǎn)tensorflow后的pb模型進(jìn)行預(yù)測。
報錯：
Attempting to use uninitialized value batch_normalization_14/moving_mean
逛論壇，有建議加上初始化：
sess.run(tf.global_variables_initializer())
但是這樣的話，會導(dǎo)致模型參數(shù)全部變成初始化數(shù)據(jù)。無法使用預(yù)測模型參數(shù)。
最后發(fā)現(xiàn)，將keras模型的加載去掉即可。
猜測原因：keras模型和tensorflow模型同時讀取有坑
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)
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