我正在尝试保存一个Sequential CNN模型。我发现我可以使用model.save()保存它，但是在我尝试使用keras.models.load_model()重新加载它之后，它又开始训练自己。
如何保存我的模型，以便不需要再次训练它？
在训练中，我也多次收到以下警告，内容如下：

/15 [=>............................] - ETA: 39s - loss: 0.6936 - accuracy: 0.50782022-10-11 
17:31:06.794142: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] 
Allocation of 358875136 exceeds 10% of free system memory.

这可能是一个原因吗？
下面是此模型的代码：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
import os
import numpy as np
import matplotlib.pyplot as plt
import pickle
PATH = 'cats_and_dogs\cats_and_dogs'

train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')
test_dir = os.path.join(PATH, 'test')


# Variables for pre-processing and training.
batch_size = 128
epochs =1
IMG_HEIGHT = 150
IMG_WIDTH = 150

train_image_generator = ImageDataGenerator(rescale=1./255)
validation_image_generator =ImageDataGenerator(rescale=1./255)
test_image_generator = ImageDataGenerator(rescale=1./255)

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                     directory=train_dir,
                                                     target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                     class_mode='binary')

val_data_gen = validation_image_generator.flow_from_directory(validation_dir, target_size=(IMG_HEIGHT, IMG_WIDTH),  class_mode='categorical',batch_size=batch_size)
test_data_gen = test_image_generator.flow_from_directory(test_dir, target_size=(IMG_HEIGHT, IMG_WIDTH), classes ='.',class_mode='categorical',  batch_size=batch_size, shuffle = False)
#I,ve found that you can use classes = ".", to get test data labels (labels when there are no subdirectories ))
from tensorflow.python.framework.func_graph import flatten

model = tf.keras.Sequential()   
model.add(tf.keras.layers.Conv2D(32, (3,3) , input_shape = (150,150,3)))
model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Conv2D(64, (3,3),activation = 'relu'))
model.add(tf.keras.layers.MaxPool2D(pool_size=(2, 2)))
model.add(tf.keras.layers.Conv2D(64, (3,3),activation = 'relu'))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(64))
model.add(tf.keras.layers.Dense(1,activation = 'sigmoid'))
model.compile(optimizer='adam',
              loss=tf.keras.losses.BinaryCrossentropy(),
              metrics=['accuracy'])
model.fit(train_data_gen,
            epochs=epochs, 
            batch_size = batch_size,
            validation_data=val_data_gen,
            steps_per_epoch =2000//batch_size, 
            validation_steps=800//batch_size)


model.save('CatDog.h5')

和代码的另一个文件，我试图上传模型：

import tensorflow as tf
import pandas
import tkinter
import os
from CNNmodel import IMG_HEIGHT, IMG_WIDTH
from tensorflow.keras.preprocessing.image import ImageDataGenerator #type: ignore
from tensorflow import keras
model = keras.models.load_model('CatDog.h5')```