tensorflow版本：t2.13.0-rc1

import os
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
import pytesseract as pt
import plotly.express as px
import matplotlib.pyplot as plt
import xml.etree.ElementTree as xet
from glob import glob
from skimage import io
from shutil import copy
from tensorflow.keras.models import Model
from tensorflow.keras.preprocessing.image import load_img, img_to_array
from sklearn.model_selection import train_test_split
from tensorflow.keras.applications import InceptionResNetV2
from tensorflow.keras.layers import Dense, Dropout, Flatten, Input
from tensorflow.keras.callbacks import TensorBoard

#Targeting all our values in array selecting all columns
labels = df.iloc[:,1:].values
data = []
output = []
for ind in range(len(image_path)):
    image = image_path[ind]
    img_arr = cv2.imread(image)
    h,w,d = img_arr.shape
    # Prepprocesing
    load_image = load_img(image,target_size=(224,224))
    load_image_arr = img_to_array(load_image)
    norm_load_image_arr = load_image_arr/255.0 # Normalization
    # Normalization to labels
    xmin,xmax,ymin,ymax = labels[ind]
    nxmin,nxmax = xmin/w,xmax/w
    nymin,nymax = ymin/h,ymax/h
    label_norm = (nxmin,nxmax,nymin,nymax) # Normalized output
    # Append
    data.append(norm_load_image_arr)
    output.append(label_norm)

# Convert data to array
X = np.array(data,dtype=np.float32)
y = np.array(output,dtype=np.float32)

# Split the data into training and testing set using sklearn.
x_train, x_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=0)
x_train.shape,x_test.shape,y_train.shape,y_test.shape

# My Model
inception_resnet = InceptionResNetV2(weights="imagenet",include_top=False, input_tensor=Input(shape=(224,224,3)))
# ---------------------
headmodel = inception_resnet.output
headmodel = Flatten()(headmodel)
headmodel = Dense(500,activation="relu")(headmodel)
headmodel = Dense(250,activation="relu")(headmodel)
headmodel = Dense(4,activation='sigmoid')(headmodel)

# ---------- model
model = Model(inputs=inception_resnet.input,outputs=headmodel)

model.compile(loss='mse', optimizer=tf.keras.optimizers.legacy.Adam(learning_rate=1e-4))
model.summary()`

# model.summary short output :(**I have shown you last few rows of model summary**)**

 conv2d_605 (Conv2D)         (None, 5, 5, 192)            399360    ['block8_9_ac[0][0]']         
                                                                                                  
 conv2d_608 (Conv2D)         (None, 5, 5, 256)            172032    ['activation_607[0][0]']      
                                                                                                  
 batch_normalization_605 (B  (None, 5, 5, 192)            576       ['conv2d_605[0][0]']          
 atchNormalization)                                                                               
                                                                                                  
 batch_normalization_608 (B  (None, 5, 5, 256)            768       ['conv2d_608[0][0]']          
 atchNormalization)                                                                               
                                                                                                  
 activation_605 (Activation  (None, 5, 5, 192)            0         ['batch_normalization_605[0][0
 )                                                                  ]']                           
                                                                                                  
 activation_608 (Activation  (None, 5, 5, 256)            0         ['batch_normalization_608[0][0
 )                                                                  ]']                           
                                                                                                  
 block8_10_mixed (Concatena  (None, 5, 5, 448)            0         ['activation_605[0][0]',      
 te)                                                                 'activation_608[0][0]']      
                                                                                                  
 block8_10_conv (Conv2D)     (None, 5, 5, 2080)           933920    ['block8_10_mixed[0][0]']     
                                                                                                  
 custom_scale_layer_119 (Cu  (None, 5, 5, 2080)           0         ['block8_9_ac[0][0]',         
 stomScaleLayer)                                                     'block8_10_conv[0][0]']      
                                                                                                  
 conv_7b (Conv2D)            (None, 5, 5, 1536)           3194880   ['custom_scale_layer_119[0][0]
                                                                    ']                            
                                                                                                  
 conv_7b_bn (BatchNormaliza  (None, 5, 5, 1536)           4608      ['conv_7b[0][0]']             
 tion)                                                                                            
                                                                                                  
 conv_7b_ac (Activation)     (None, 5, 5, 1536)           0         ['conv_7b_bn[0][0]']          
                                                                                                  
 flatten_2 (Flatten)         (None, 38400)                0         ['conv_7b_ac[0][0]']          
                                                                                                  
 dense_6 (Dense)             (None, 500)                  1920050   ['flatten_2[0][0]']           
                                                          0                                       
                                                                                                  
 dense_7 (Dense)             (None, 250)                  125250    ['dense_6[0][0]']             
                                                                                                  
 dense_8 (Dense)             (None, 4)                    1004      ['dense_7[0][0]']             
                                                                                                  
==================================================================================================
Total params: 73663490 (281.00 MB)
Trainable params: 73602946 (280.77 MB)
Non-trainable params: 60544 (236.50 KB)

tfb = TensorBoard('object_detection2')
history = model.fit(x=x_train,y=y_train,batch_size=10,epochs=180,
                    validation_data=(x_test,y_test),callbacks=[tfb])

model.save('./object_detection.h5')

模型训练后，加载模型

model = tf.keras.models.load_model('./object_detection.h5')
print('Model loaded Sucessfully')

我会得到这个错误

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[30], line 2
      1 # Load model
----> 2 model = tf.keras.models.load_model('./object_detection.h5')
      3 print('Model loaded Sucessfully')

File ~/anaconda3/lib/python3.10/site-packages/keras/src/saving/saving_api.py:238, in load_model(filepath, custom_objects, compile, safe_mode, **kwargs)
    230     return saving_lib.load_model(
    231         filepath,
    232         custom_objects=custom_objects,
    233         compile=compile,
    234         safe_mode=safe_mode,
    235     )
    237 # Legacy case.
--> 238 return legacy_sm_saving_lib.load_model(
    239     filepath, custom_objects=custom_objects, compile=compile, **kwargs
    240 )

File ~/anaconda3/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File ~/anaconda3/lib/python3.10/site-packages/keras/src/saving/legacy/serialization.py:365, in class_and_config_for_serialized_keras_object(config, module_objects, custom_objects, printable_module_name)
    361 cls = object_registration.get_registered_object(
    362     class_name, custom_objects, module_objects
    363 )
    364 if cls is None:
--> 365     raise ValueError(
    366         f"Unknown {printable_module_name}: '{class_name}'. "
    367         "Please ensure you are using a `keras.utils.custom_object_scope` "
    368         "and that this object is included in the scope. See "
    369         "https://www.tensorflow.org/guide/keras/save_and_serialize"
    370         "#registering_the_custom_object for details."
    371     )
    373 cls_config = config["config"]
    374 # Check if `cls_config` is a list. If it is a list, return the class and the
    375 # associated class configs for recursively deserialization. This case will
    376 # happen on the old version of sequential model (e.g. `keras_version` ==
    377 # "2.0.6"), which is serialized in a different structure, for example
    378 # "{'class_name': 'Sequential',
    379 #   'config': [{'class_name': 'Embedding', 'config': ...}, {}, ...]}".

ValueError: Unknown layer: 'CustomScaleLayer'. Please ensure you are using a `keras.utils.custom_object_scope` and that this object is included in the scope. See https://www.tensorflow.org/guide/keras/save_and_serialize#registering_the_custom_object for details.