모든 keras 레이어를 동결 시켰지만 fit_genereator를 사용할 때 모델이 변경되었습니다.

Question

모델을 재교육하기 위해 미세 조정 방법을 사용하려고합니다. 온전한 체크로서 나는 모든 레이어를 먼저 얼리는 동안 그것을 재 훈련하려고했습니다. 모델이 변경되지 않을 것으로 예상됩니다. 나는 이것을보고 놀랐습니다 :

from keras import backend as K 
import inception_v4 
import numpy as np 
import cv2 
import os 

import re 

from keras import optimizers 
from keras.preprocessing.image import ImageDataGenerator 
from keras.models import Sequential 
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D 
from keras.layers import Activation, Dropout, Flatten, Dense, Input 

from keras.models import Model 
os.environ['CUDA_VISIBLE_DEVICES'] = '' 




v4 = inception_v4.create_model(weights='imagenet') 


#v4.summary() 
my_batch_size=1 
train_data_dir ='//shared_directory/projects/try_CDFxx/data/train/' 
validation_data_dir ='//shared_directory/projects/try_CDFxx/data/validation/' 
top_model_weights_path= 'bottleneck_fc_model.h5' 
class_num=2 

img_width, img_height = 299, 299 
nbr_train_samples=16 
nbr_validation_samples=8 
num_classes=2 
nb_epoch=50 

main_input= v4.layers[1].input 
main_output=v4.layers[-1].output 
flatten_output= v4.layers[-2].output 


BN_model = Model(input=[main_input], output=[main_output, flatten_output]) 





### DEF 
train_datagen = ImageDataGenerator(
      rescale=1./255, 
      shear_range=0.1, 
      zoom_range=0.1, 
      rotation_range=10., 
      width_shift_range=0.1, 
      height_shift_range=0.1, 
      horizontal_flip=True) 

val_datagen = ImageDataGenerator(rescale=1./255) 



train_generator = train_datagen.flow_from_directory(
      train_data_dir, 
      target_size = (img_width, img_height), 
      batch_size = my_batch_size, 
      shuffle = True, 
      class_mode = 'categorical') 

validation_generator = val_datagen.flow_from_directory(
      validation_data_dir, 
      target_size=(img_width, img_height), 
      batch_size=my_batch_size, 
      shuffle = True, 
      class_mode = 'categorical') # sparse 


### 

def save_BN(BN_model): # but we will need to get the get_processed_image into it!!!! 
# 
    datagen = ImageDataGenerator(rescale=1./255) # here! 
# 
    generator = datagen.flow_from_directory(
      train_data_dir, 
      target_size=(img_width, img_height), 
      batch_size=my_batch_size, 
      class_mode='categorical', 
      shuffle=False) 
    nb_train_samples = generator.classes.size  
    bottleneck_features_train = BN_model.predict_generator(generator, nb_train_samples) 
# 
    np.save(open('bottleneck_flat_features_train.npy', 'wb'), bottleneck_features_train[1]) 

    np.save(open('bottleneck_train_labels.npy', 'wb'), generator.classes) 
    # generator is probably a tuple - and the second thing in it is a label! OKAY, its not :(
    generator = datagen.flow_from_directory(
      validation_data_dir, 
      target_size=(img_width, img_height), 
      batch_size=my_batch_size, 
      class_mode='categorical', 
      shuffle=False) 

    nb_validation_samples = generator.classes.size 
    bottleneck_features_validation = BN_model.predict_generator(generator, nb_validation_samples) 
    #bottleneck_features_validation = model.train_generator(generator, nb_validation_samples) 
# 
    np.save(open('bottleneck_flat_features_validation.npy', 'wb'), bottleneck_features_validation[1]) 

    np.save(open('bottleneck_validation_labels.npy', 'wb'), generator.classes) 



def train_top_model(): 
    train_data = np.load(open('bottleneck_flat_features_train.npy')) 
    train_labels = np.load(open('bottleneck_train_labels.npy')) 
# 
    validation_data = np.load(open('bottleneck_flat_features_validation.npy')) 
    validation_labels = np.load(open('bottleneck_validation_labels.npy')) 
    # 
    top_m = Sequential() 
    top_m.add(Dense(class_num,input_shape=train_data.shape[1:], activation='softmax', name='top_dense1')) 
    top_m.compile(optimizer='rmsprop', loss='sparse_categorical_crossentropy', metrics=['accuracy']) 
# 
    top_m.fit(train_data, train_labels, 
    nb_epoch=nb_epoch, batch_size=my_batch_size, 
    validation_data=(validation_data, validation_labels)) 
# 
# 
    #top_m.save_weights (top_model_weights_path) 
# validation_data[0] 
# train_data[0] 
    Dense_layer=top_m.layers[-1] 
    top_layer_weights=Dense_layer.get_weights() 
    np.save(open('retrained_top_layer_weight.npy', 'wb'), top_layer_weights) 


def fine_tune_model(): 

    predictions = Flatten()(v4.layers[-3].output) 
    predictions = Dense(output_dim=num_classes, activation='softmax', name="newDense")(predictions) 
    main_input= v4.layers[1].input 
    main_output=predictions 
    FT_model = Model(input=[main_input], output=[main_output]) 

    top_layer_weights = np.load(open('retrained_top_layer_weight.npy')) 
    Dense_layer=FT_model.layers[-1] 
    Dense_layer.set_weights(top_layer_weights) 

    for layer in FT_model.layers: 
     layer.trainable = False 
# FT_model.layers[-1].trainable=True 

    FT_model.compile(optimizer=optimizers.SGD(lr=1e-4, momentum=0.9), loss='categorical_crossentropy', metrics=['accuracy']) 


    FT_model.fit_generator(
      train_generator, 
      samples_per_epoch = nbr_train_samples, 
      nb_epoch = nb_epoch, 
      validation_data = validation_generator, 
      nb_val_samples = nbr_validation_samples)  

######################################################## 
      ########### 


save_BN(BN_model) 
train_top_model() 

fine_tune_model() 

감사 :

Epoch 1/50 
16/16 [==============================] - 25s - loss: 4.0006 - acc: 0.5000 - val_loss: 1.3748e-04 - val_acc: 1.0000 
Epoch 2/50 
16/16 [==============================] - 24s - loss: 3.8861 - acc: 0.5000 - val_loss: 1.7333e-04 - val_acc: 1.0000 
Epoch 3/50 
16/16 [==============================] - 25s - loss: 3.9560 - acc: 0.5000 - val_loss: 3.0870e-04 - val_acc: 1.0000 
Epoch 4/50 
16/16 [==============================] - 26s - loss: 3.9730 - acc: 0.5000 - val_loss: 7.5931e-04 - val_acc: 1.0000 
Epoch 5/50 
16/16 [==============================] - 26s - loss: 3.7195 - acc: 0.5000 - val_loss: 0.0021 - val_acc: 1.0000 
Epoch 6/50 
16/16 [==============================] - 25s - loss: 3.9514 - acc: 0.5000 - val_loss: 0.0058 - val_acc: 1.0000 
Epoch 7/50 
16/16 [==============================] - 26s - loss: 3.9459 - acc: 0.5000 - val_loss: 0.0180 - val_acc: 1.0000 
Epoch 8/50 
16/16 [==============================] - 26s - loss: 3.8744 - acc: 0.5000 - val_loss: 0.0489 - val_acc: 1.0000 
Epoch 9/50 
16/16 [==============================] - 27s - loss: 3.8914 - acc: 0.5000 - val_loss: 0.1100 - val_acc: 1.0000 
Epoch 10/50 
16/16 [==============================] - 26s - loss: 4.0585 - acc: 0.5000 - val_loss: 0.2092 - val_acc: 0.7500 
Epoch 11/50 
16/16 [==============================] - 27s - loss: 4.0232 - acc: 0.5000 - val_loss: 0.3425 - val_acc: 0.7500 
Epoch 12/50 
16/16 [==============================] - 25s - loss: 3.9073 - acc: 0.5000 - val_loss: 0.4566 - val_acc: 0.7500 
Epoch 13/50 
16/16 [==============================] - 27s - loss: 4.1036 - acc: 0.5000 - val_loss: 0.5454 - val_acc: 0.7500 
Epoch 14/50 
16/16 [==============================] - 26s - loss: 3.7854 - acc: 0.5000 - val_loss: 0.6213 - val_acc: 0.7500 
Epoch 15/50 
16/16 [==============================] - 27s - loss: 3.7907 - acc: 0.5000 - val_loss: 0.7120 - val_acc: 0.7500 
Epoch 16/50 
16/16 [==============================] - 27s - loss: 4.0540 - acc: 0.5000 - val_loss: 0.7226 - val_acc: 0.7500 
Epoch 17/50 
16/16 [==============================] - 26s - loss: 3.8669 - acc: 0.5000 - val_loss: 0.8032 - val_acc: 0.7500 
Epoch 18/50 
16/16 [==============================] - 28s - loss: 3.9834 - acc: 0.5000 - val_loss: 0.9523 - val_acc: 0.7500 
Epoch 19/50 
16/16 [==============================] - 27s - loss: 3.9495 - acc: 0.5000 - val_loss: 2.5764 - val_acc: 0.6250 
Epoch 20/50 
16/16 [==============================] - 25s - loss: 3.7534 - acc: 0.5000 - val_loss: 3.0939 - val_acc: 0.6250 
Epoch 21/50 
16/16 [==============================] - 29s - loss: 3.8447 - acc: 0.5000 - val_loss: 3.0467 - val_acc: 0.6250 
Epoch 22/50 
16/16 [==============================] - 28s - loss: 4.0613 - acc: 0.5000 - val_loss: 3.2160 - val_acc: 0.6250 
Epoch 23/50 
16/16 [==============================] - 28s - loss: 4.1428 - acc: 0.5000 - val_loss: 3.8793 - val_acc: 0.6250 
Epoch 24/50 
16/16 [==============================] - 27s - loss: 3.7868 - acc: 0.5000 - val_loss: 4.1935 - val_acc: 0.6250 
Epoch 25/50 
16/16 [==============================] - 28s - loss: 3.8437 - acc: 0.5000 - val_loss: 4.5031 - val_acc: 0.6250 
Epoch 26/50 
16/16 [==============================] - 28s - loss: 3.9798 - acc: 0.5000 - val_loss: 4.5121 - val_acc: 0.6250 
Epoch 27/50 
16/16 [==============================] - 28s - loss: 3.8727 - acc: 0.5000 - val_loss: 4.5341 - val_acc: 0.6250 
Epoch 28/50 
16/16 [==============================] - 28s - loss: 3.8343 - acc: 0.5000 - val_loss: 4.5198 - val_acc: 0.6250 
Epoch 29/50 
16/16 [==============================] - 28s - loss: 4.2144 - acc: 0.5000 - val_loss: 4.5341 - val_acc: 0.6250 
Epoch 30/50 
16/16 [==============================] - 28s - loss: 3.8348 - acc: 0.5000 - val_loss: 4.5684 - val_acc: 0.6250 

이 내가 사용하는 코드입니다.

P. 나는 케라를 사용하고 있습니다.

Answer 1

당신은 dropout을 사용하고 있습니다. 그래서 다른 단위가 꺼지면 다른 실행에 따라 통계가 다를 수 있습니다.