问题描述
我正在对ResNet50模型进行微调，以使用数据引证进行人脸识别，但是观察到模型的准确性正在提高，但是从一开始的验证准确性并没有提高，我没有弄错哪里出了问题，请查看我的代码.
I was doing fine-tuning with ResNet50 model for face recognition using data agumentation, but observed that the model accuracy was increasing but validation accuracy from the very starting point is not imporving, i am not getting where is it getting wrong, please review my code.
我尝试操纵添加的顶层，但没有帮助.
I have tried manipulating the top layers which i have added but it didn't helped.
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))
输出:
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
推荐答案
之所以发生这种情况，是因为我只是直接添加完全连接的层而没有先对其进行培训，如keras博客中所述， https://blog.keras. io/building-powerful-image-classification-models-using-very-little-data.html
It was happening because i was just directly adding the fully-connected layers without training it first, as mentioned in Blog of keras,https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html
所以答案是首先分别训练顶级模型，然后创建一个具有ResNet50模型的新模型，其权重，顶级模型及其权重都位于resnet模型(基础模型)的顶部，然后通过冻结对其进行首次训练基本模型(ResNet50)和基础模型的最后一层.
so answer is first train the top-model separately, then create a new model having ResNet50 model with its weight, with top-model and its weights on top of resnet model(base-model), then train it first by freezing the base-model(ResNet50) and the with the last layer of the base-model.
这篇关于验证准确性不会增加培训ResNet50的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！