运维开发网
广告位招商联系QQ:123077622
 
广告位招商联系QQ:123077622

解决tensorflow 与keras 混用之坑

运维开发网 https://www.qedev.com 2021-05-18 11:35 出处:网络 作者: Elijah_Yi
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

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

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
 
#    
train_dir = 'train'
#     
val_dir = 'val'
#     
test_dir = 'val'
 
#  
img_width, img_height = 800, 800
#           
# backend Tensorflow, channels_last
input_shape = (img_width, img_height, 3)
#  
epochs = 1
#  -
batch_size = 4
#    
nb_train_samples = 300
#    
nb_validation_samples = 25
#    
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(' ')
 
model.save("grib.h5")
print(" !")

模型载入

from tensorflow.python.keras.preprocesswww.cppcns.coming.imageUqGlcIrxi 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(" ")
model = load_model("grib.h5")
print(" !")

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.

Traceback (most recent call last):

Fil编程客栈e "/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/http://www.cppcns.comdist-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

BatchNormalization . ? keras tensorflow .(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

keras.preprocessing.image impohttp://www.cppcns.comrt 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:

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

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)

以上为个人经验,希望能给大家一个参考,也希望大家多多支持我们。

扫码领视频副本.gif

0

精彩评论

暂无评论...
验证码 换一张
取 消

关注公众号