IOS、Python自学心得30天 Day-14 H5与Pb模型档案

前言:
因为内存不足以在训练的时候存 SavedModel模型
所以我分离出来
以.h5档案先做训练
再以.h5档案载入
并用成.pb档案类型的储存方式
来达成目的

程序码:

import os
import tensorflow as tf
from tensorflow import keras
from tensorflow.python.keras import backend as K
from tensorflow.python.keras.models import Model
from tensorflow.python.keras.layers import Flatten, Dense, Dropout
from tensorflow.python.keras.applications.resnet import ResNet50
from tensorflow.python.keras.optimizer_v2.adam import Adam
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models import Sequential, load_model
from tensorflow.python.keras.layers import LSTM, Dropout, Dense
from tensorflow.python.keras.callbacks import ModelCheckpoint
from tensorflow.python.framework.ops import disable_eager_execution


#解决内存配置不足的问题
from PIL import ImageFile
ImageFile.LOAD_TRUNCATED_IMAGES = True

# Allow memory growth for the GPU
physical_devices = tf.config.experimental.list_physical_devices('GPU')
tf.config.experimental.set_memory_growth(physical_devices[0], True)

disable_eager_execution()

tf.compat.v1.experimental.output_all_intermediates(True)

config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)

net_final = load_model('model-resnet50-final.h5')
net_final.save('saved_model/my_model')