我冻结了所有 keras 层，但使用 fit_generator 时模型会发生变化

Question

我正在尝试使用微调方法来重新训练模型。 作为一个健全的检查，我尝试重新训练它，同时首先冻结它的所有层。 我预计模型不会改变；看到这个我很惊讶：

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

这是我使用的代码：

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()

谢谢。

附： 我正在使用 keras 1。

Answer 1

训练更改是正常的，因为您使用的是图像数据增强，因此每个 epoch 的每个数据集都会有所不同。要冻结所有层，请尝试将模型的可训练参数直接更改为 False：

FT_model.trainable = False
print('This is the number of trainable weights ''after freezing the conv base:', len(FT_model.trainable_weights))

Answer 2

您使用的是dropout，因此当不同的单元关闭时，不同运行的指标可能会有所不同。