Tensor flowで複数のモデルの読込および推論

Question

前提・実現したいこと

Tensor flowで複数のモデルを読込み、推論を実行させたい。

発生している問題・エラーメッセージ

name 'model2' is not defined

該当のソースコード

import os
import sys
from keras.applications.vgg16 import VGG16, preprocess_input, decode_predictions
from keras.models import Sequential, Model
from keras.layers import Input, Activation, Dropout, Flatten, Dense
from keras.preprocessing import image
from keras.preprocessing.image import array_to_img
from pathlib import Path
import glob
import pathlib
import numpy as np
from PIL import Image
from keras.models import load_model
import csv
import cv2
from matplotlib import pyplot as plt
from keras import backend as K
from keras.preprocessing import image
import tensorflow as tf
from tensorflow.python.framework import ops
import gc
import tracemalloc
import time
#import objgraph
#import pdb; pdb.set_trace()


file =glob.glob(r"E:\Deep Learning用\VGG16 finetuning\image\1. Marking\test\test2*.bmp")

result_dir = "Result"

img_height, img_width = 224, 224
channels = 3

input_tensor = Input(shape=(img_height, img_width, 3))
vgg16_model = VGG16(include_top=False, weights="imagenet", input_tensor= input_tensor)
top_model = Sequential()
top_model.add(Flatten(input_shape=vgg16_model.output_shape[1:]))
top_model.add(Dense(256, activation="relu"))
top_model.add(Dropout(0.3))
top_model.add(Dense(1, activation="sigmoid"))

model1 = Model(input=vgg16_model.input, output=top_model(vgg16_model.output))
model1.save(r"E:\Deep Learning用\VGG16 finetuning\Result\finetuning_model.h5")
model1.load_weights(r"E:\Deep Learning用\VGG16 finetuning\Result\VGG16_finetuning(marking).h5")
model1.compile(loss="binary_crossentropy",optimizer="adam", metrics=["accuracy"] )

model2.load_model(r"E:\Deep Learning用\VGG16 finetuning\Result\finetuning_model.h5")
model2.load_weights(r"E:\Deep Learning用\VGG16 finetuning\Result\VGG16_finetuning(marking).h5")
model2.compile(loss="binary_crossentropy",optimizer="adam", metrics=["accuracy"] )

K.set_learning_phase(1)

with open("test_result.csv","wt",newline="", encoding="utf-8") as out:
    writer = csv.writer(out)
    writer.writerow(["name", "Probability","time"])
    
    for i in file:
        t1 = time.perf_counter()
        img = Image.open(i)
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = x.astype("float32")
        preprocessed_input = x/255.0
        
        pred = sess.model.predict(preprocessed_input)[0]    
        pred1 = sess.model1.predict(preprocessed_inputnput[0])
        t2 = time.perf_counter()
        sub_time = (t2-t1)*1000
        print(i,pred,sub_time)
        writer.writerow([i, pred, sub_time])
        K.clear_session()
        

試したこと

補足情報（FW/ツールのバージョンなど）

Answer 1

#model2.load_model(r"E:\Deep Learning用\VGG16 finetuning\Result\finetuning_model.h5")  # 間違い 
model2 = load_model(r"E:\Deep Learning用\VGG16 finetuning\Result\finetuning_model.h5") # 正しい