Softmax関数を用いる前の値を出力したい

Question

前提・実現したいこと

現在、kerasを用いてVggfaceというものに画像を通して似ている確率を出力しています。
この時に、Softmax関数を用いて確率に変換する前の値を出力したいのですがうまく行きません。
どのようなコードを追加すれば良いでしょうか。

該当のソースコード

import numpy as np
import sys

from keras.preprocessing import image
from keras_vggface.vggface import VGGFace
from keras_vggface import utils
from keras import backend as K

# Based on VGG16 architecture -> old paper(2015)
model = VGGFace(model='vgg16') # or VGGFace() as default
# Based on RESNET50 architecture -> new paper(2017)
#vggface = VGGFace(model='resnet50')
# Based on SENET50 architecture -> new paper(2017)
#vggface = VGGFace(model='senet50')
model.summary()

# Change the image path with yours.

img = image.load_img(sys.argv[1], target_size=(224, 224))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = utils.preprocess_input(x, version=1) # or version=2
#model_extractfeatures = model(inputs=model.input, output=model.get_layer('fc8').output)
#fc8_features = model_extractfeatures.predict(x)
preds = model.predict(x)
np.set_printoptions(threshold=np.inf)
#print (fc8_features.transpose())
print (preds.transpose())
#print('Predicted:', utils.decode_predictions(preds))

試したこと

以下のコードを追加してみました。

model_extractfeatures = model(inputs=model.input, output=model.get_layer('fc8').output)
fc8_features = model_extractfeatures.predict(x)
print (fc8_features.transpose())

補足情報（FW/ツールのバージョンなど）

出したい部分はいかのLayerのfc８の値です。

Layer (type)                 Output Shape              Param #   
=================================================================
input_1 (InputLayer)         (None, 224, 224, 3)       0         
_________________________________________________________________
conv1_1 (Conv2D)             (None, 224, 224, 64)      1792      
_________________________________________________________________
conv1_2 (Conv2D)             (None, 224, 224, 64)      36928     
_________________________________________________________________
pool1 (MaxPooling2D)         (None, 112, 112, 64)      0         
_________________________________________________________________
conv2_1 (Conv2D)             (None, 112, 112, 128)     73856     
_________________________________________________________________
conv2_2 (Conv2D)             (None, 112, 112, 128)     147584    
_________________________________________________________________
pool2 (MaxPooling2D)         (None, 56, 56, 128)       0         
_________________________________________________________________
conv3_1 (Conv2D)             (None, 56, 56, 256)       295168    
_________________________________________________________________
conv3_2 (Conv2D)             (None, 56, 56, 256)       590080    
_________________________________________________________________
conv3_3 (Conv2D)             (None, 56, 56, 256)       590080    
_________________________________________________________________
pool3 (MaxPooling2D)         (None, 28, 28, 256)       0         
_________________________________________________________________
conv4_1 (Conv2D)             (None, 28, 28, 512)       1180160   
_________________________________________________________________
conv4_2 (Conv2D)             (None, 28, 28, 512)       2359808   
_________________________________________________________________
conv4_3 (Conv2D)             (None, 28, 28, 512)       2359808   
_________________________________________________________________
pool4 (MaxPooling2D)         (None, 14, 14, 512)       0         
_________________________________________________________________
conv5_1 (Conv2D)             (None, 14, 14, 512)       2359808   
_________________________________________________________________
conv5_2 (Conv2D)             (None, 14, 14, 512)       2359808   
_________________________________________________________________
conv5_3 (Conv2D)             (None, 14, 14, 512)       2359808   
_________________________________________________________________
pool5 (MaxPooling2D)         (None, 7, 7, 512)         0         
_________________________________________________________________
flatten (Flatten)            (None, 25088)             0         
_________________________________________________________________
fc6 (Dense)                  (None, 4096)              102764544 
_________________________________________________________________
fc6/relu (Activation)        (None, 4096)              0         
_________________________________________________________________
fc7 (Dense)                  (None, 4096)              16781312  
_________________________________________________________________
fc7/relu (Activation)        (None, 4096)              0         
_________________________________________________________________
fc8 (Dense)                  (None, 2622)              10742334  
_________________________________________________________________
fc8/softmax (Activation)     (None, 2622)              0         
=================================================================

Answer 1

from keras.models import Model


model_extractfeatures = Model(inputs=model.input, output=model.get_layer('fc8').output)
fc8_features = model_extractfeatures.predict(x)
print (fc8_features.transpose())

に変えれば行けます。
変更点はimportの追加とmodel_extractfeaturesのところでModelに変えている部分です。

Comments

[satoUmino]

無事出力することが出来ました。
ありがとうございました。