驗證准確度沒有增加訓練 ResNet50

Question

我正在使用 ResNet50 模型進行微調以使用數據 agumentation 進行人臉識別，但觀察到模型准確性正在提高，但從一開始的驗證准確性並沒有提高，我不知道哪里出了問題，請查看我的代碼.

我已經嘗試操作我添加的頂層，但它沒有幫助。

import os
os.environ['KERAS_BACKEND'] = 'tensorflow'
from keras.applications import ResNet50
from keras.models import Sequential
from keras.layers import Dense, Flatten, GlobalAveragePooling2D,Input,Dropout

num_classes = 13

base = ResNet50(include_top=False, weights='resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5',input_tensor=Input(shape=(100,100,3)))
from keras.models import Model

x = base.output

#x = GlobalAveragePooling2D()(x)

x = Flatten()(x)

x = Dense(1024, activation = 'relu')(x)

x = Dropout(0.5)(x)

predictions = Dense(13, activation='softmax')(x)

model = Model(inputs=base.input, outputs=predictions)

for layers in base.layers:
    layers.trainable= False

model.compile(optimizer='sgd', loss='categorical_crossentropy', metrics=['accuracy'])
from keras.preprocessing.image import ImageDataGenerator

train_generator = ImageDataGenerator(featurewise_center=True,
                                rotation_range=20,
                                rescale=1./255,
                                shear_range=0.2,
                                zoom_range=0.2,
                                width_shift_range=0.2,
                                height_shift_range=0.2,
                                horizontal_flip=True)

test_generator = ImageDataGenerator(rescale=1./255)

from sklearn.model_selection import train_test_split

x_train,x_test,y_train,y_test = train_test_split(image,label,test_size=0.2,shuffle=True,random_state=0)

train_generator.fit(x_train)
test_generator.fit(x_test)

model.fit_generator(train_generator.flow(x_train,y_train,batch_size=32),
                       steps_per_epoch =10,epochs=50, 
                    validation_data=test_generator.flow(x_test,y_test))

輸出 ：

Epoch 19/50
10/10 [==============================] - 105s 10s/step - loss: 1.9387 - acc: 0.3803 - val_loss: 2.6820 - val_acc: 0.0709
Epoch 20/50
10/10 [==============================] - 107s 11s/step - loss: 2.0725 - acc: 0.3230 - val_loss: 2.6689 - val_acc: 0.0709
Epoch 21/50
10/10 [==============================] - 103s 10s/step - loss: 1.8884 - acc: 0.3375 - val_loss: 2.6677 - val_acc: 0.0709
Epoch 22/50
10/10 [==============================] - 95s 10s/step - loss: 1.8265 - acc: 0.4051 - val_loss: 2.6799 - val_acc: 0.0709
Epoch 23/50
10/10 [==============================] - 100s 10s/step - loss: 1.8346 - acc: 0.3812 - val_loss: 2.6929 - val_acc: 0.0709
Epoch 24/50
10/10 [==============================] - 102s 10s/step - loss: 1.9547 - acc: 0.3352 - val_loss: 2.6952 - val_acc: 0.0709
Epoch 25/50
10/10 [==============================] - 104s 10s/step - loss: 1.9472 - acc: 0.3281 - val_loss: 2.7168 - val_acc: 0.0709
Epoch 26/50
10/10 [==============================] - 103s 10s/step - loss: 1.8818 - acc: 0.4063 - val_loss: 2.7071 - val_acc: 0.0709
Epoch 27/50
10/10 [==============================] - 106s 11s/step - loss: 1.8053 - acc: 0.4000 - val_loss: 2.7059 - val_acc: 0.0709
Epoch 28/50
10/10 [==============================] - 104s 10s/step - loss: 1.9601 - acc: 0.3493 - val_loss: 2.7104 - val_acc: 0.0709

Answer 1

之所以發生這種情況，是因為我只是直接添加完全連接的層而沒有首先進行培訓，正如keras博客中提到的那樣， https ：//blog.keras.io/building-powerful-image-classification-models-using-very- 小data.html

為了進行微調，所有層都應該從訓練有素的權重開始：例如，你不應該在預先訓練好的卷積基礎上打一個隨機初始化的全連接網絡。 這是因為由隨機初始化的權重觸發的大梯度更新將破壞卷積基礎中的學習權重。 在我們的例子中，這就是為什么我們首先訓練頂級分類器，然后才開始微調卷積權重。

所以答案首先分別訓練頂級模型，然后創建一個具有ResNet50模型及其重量的新模型，頂級模型及其重量位於resnet模型（基礎模型）之上，然后通過凍結基礎來訓練它 - 模型（ResNet50）和基礎模型的最后一層。

Answer 2

對於 resnet50 案例，這似乎對我有用，並且在應用此驗證后准確性開始提高。 https://github.com/fchollet/deep-learning-models/issues/96#issuecomment-456395511