学習済CNNモデルの中間層を抽出したい

Question

前提・実現したいこと

VGG16をファインチューニングして学習したモデルをロードし、
その中のfc層をoutputして画像の特徴量を抽出したいと考えています。

sequentialの最初を接続する部分でエラーが出ていることは分かるのですが、その解決方法が分からず詰まっています。
問題の解決方法や、その他改善点等教えていただけると助かります。よろしくお願いいたします。

発生している問題・エラーメッセージ

モデルの中間層をoutputに設定した新しいモデルを作成しようとすると、以下のようなエラーが出てしまいます。

ValueError: Graph disconnected: cannot obtain value for tensor Tensor("flatten_1_input:0", shape=(?, 8, 8, 512), dtype=float32) at layer "flatten_1_input". The following previous layers were accessed without issue: []

該当のソースコード

from keras.layers import Dense, Dropout, Activation, Flatten
from keras.preprocessing import image
from keras.models import model_from_json, load_model, Model
from keras import models
from keras import layers

import numpy as np
import pandas as pd
import sys
import cv2
import os
import shutil

model = load_model('./finetuning_model.h5')
layer_output = model.get_layer('sequential_1').get_layer('fc2').output
new_model = Model(inputs=model.input, outputs=layer_output)
print(activation_model.summary())

modelのsummaryは以下の通りです

Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 256, 256, 3)       0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 256, 256, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 256, 256, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 128, 128, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 128, 128, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 128, 128, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 64, 64, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 64, 64, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 64, 64, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 64, 64, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 32, 32, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 32, 32, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 32, 32, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 32, 32, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 16, 16, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 16, 16, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 8, 8, 512)         0         
_________________________________________________________________
sequential_1 (Sequential)    (None, 2)                 8455170   
=================================================================
Total params: 23,169,858
Trainable params: 23,169,858
Non-trainable params: 0
_________________________________________________________________

また、sequentialの内容は以下の通りです。

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
flatten_1 (Flatten)          (None, 32768)             0         
_________________________________________________________________
fc1 (Dense)                  (None, 256)               8388864   
_________________________________________________________________
dropout1 (Dropout)           (None, 256)               0         
_________________________________________________________________
fc2 (Dense)                  (None, 256)               65792     
_________________________________________________________________
dropout2 (Dropout)           (None, 256)               0         
_________________________________________________________________
output (Dense)               (None, 2)                 514       
=================================================================
Total params: 8,455,170
Trainable params: 8,455,170
Non-trainable params: 0
_________________________________________________________________

補足情報（FW/ツールのバージョンなど）

python 2.7
keras　2.2.4