J'ai gelé tous les calques keras, mais le modèle change lors de l'utilisation de fit_genereator

Question

J'essaie d'utiliser une approche de réglage fin pour recycler un modèle. Pour vérifier ma santé, j'ai essayé de le recycler tout en gelant toutes ses couches. Je m'attendais à ce que le modèle ne change pas; Je fus surpris de voir ceci:

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 

Ce code je:

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() 

Merci.

P.S. J'utilise keras 1.

Answer 1

Vous utilisez dropout alors les métriques peuvent varier entre les différentes exécutions lorsque les différentes unités sont désactivées.