使用 ResNet50，验证准确性和损失不会改变

Question

我正在尝试使用ResNet50Python ( keras)进行图像识别。我试图用 做同样的任务VGG16，我得到了一些这样的结果（对我来说似乎没问题）： resultsVGG16。训练和验证的准确率/损失函数每一步都在变好，因此网络必须学习。

然而，随着ResNet50训练函数的投注更好，而验证函数没有改变：resultsResNet

我两次都使用了相同的代码和数据，只是模型发生了变化。

那么ResNet50只在训练数据上学习的原因是什么？

我的 ResNet 模型如下所示：

'''Python

model = Sequential()
base_model = VGG16(weights='imagenet', include_top=False,input_shape= 
(image_size,image_size,3))
for layer in base_model.layers[:-4]:
    layer.trainable=False

model.add(base_model)
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(NUM_CLASSES, activation='softmax'))

VGG 非常相似：

model = Sequential()
base_model = ResNet50(include_top=False, weights='imagenet', input_shape= 
(image_size,image_size,3))
for layer in base_model.layers[:-8]:
     layer.trainable=False

model.add(base_model)
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(0.4))
model.add(Dense(NUM_CLASSES, activation='softmax'))

Answer 1

您的模型中没有错误，但这可能是问题所在ResNet，因为在 Github 和 Stack Overflow 中提出了许多问题1、2、3，已经与此预训练模型有关。

话虽如此，我找到了一种解决方法，该方法对我有用，希望也对您有用。

解决方法是替换数据增强步骤，

Train_Datagen = ImageDataGenerator(rescale=1./255,  rotation_range=40, width_shift_range=0.2,
    height_shift_range=0.2, brightness_range=(0.2, 0.7), shear_range=45.0, zoom_range=60.0,
    horizontal_flip=True, vertical_flip=True)

Val_Datagen = ImageDataGenerator(rescale=1./255,  rotation_range=40, width_shift_range=0.2,
    height_shift_range=0.2, brightness_range=(0.2, 0.7), shear_range=45.0, zoom_range=60.0,
    horizontal_flip=True, vertical_flip=True)

与tf.keras.applications.resnet.preprocess_input，如下图所示：

Train_Datagen = ImageDataGenerator(dtype = 'float32', preprocessing_function=tf.keras.applications.resnet.preprocess_input)
Val_Datagen = ImageDataGenerator(dtype = 'float32', preprocessing_function=tf.keras.applications.resnet.preprocess_input)

通过修改Data Augmentation如上所示，我的验证准确率（卡在 50%）逐渐提高到 97%。原因可能是 ResNet 可能需要特定的预处理操作（不太确定）。

使用 ResNet50 实现超过 95% 的训练和验证准确度（针对猫和狗数据集）的完整工作代码如下所示：

import tensorflow as tf
from tensorflow.keras.applications import ResNet50
import os
import numpy as np
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.layers import Dense, Dropout, Flatten
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential

# The Convolutional Base of the Pre-Trained Model will be added as a Layer in this Model
Conv_Base = ResNet50(include_top = False, weights = 'imagenet', input_shape = (150,150, 3))

for layer in Conv_Base.layers[:-8]:
    layer.trainable = False

model = Sequential()
model.add(Conv_Base)
model.add(Flatten())
model.add(Dense(units = 256, activation = 'relu'))
model.add(Dropout(0.5))
model.add(Dense(units = 1, activation = 'sigmoid'))

model.summary()

base_dir = 'Deep_Learning_With_Python_Book/Dogs_Vs_Cats_Small'
if os.path.exists(base_dir):    
    train_dir = os.path.join(base_dir, 'train')
    validation_dir = os.path.join(base_dir, 'validation')
    test_dir = os.path.join(base_dir, 'test')
else:
    print("The Folder, {}, doesn't exist'".format(base_dir))

batch_size = 20

Train_Datagen = ImageDataGenerator(dtype = 'float32', preprocessing_function=tf.keras.applications.resnet.preprocess_input)
Val_Datagen = ImageDataGenerator(dtype = 'float32', preprocessing_function=tf.keras.applications.resnet.preprocess_input)

train_gen = Train_Datagen.flow_from_directory(directory = train_dir, target_size = (150,150), 
                                       batch_size = batch_size, class_mode = 'binary')

val_gen = Val_Datagen.flow_from_directory(directory = validation_dir, target_size = (150,150), 
                                       batch_size = batch_size, class_mode = 'binary')

epochs = 15
Number_Of_Training_Images = train_gen.classes.shape[0]
steps_per_epoch = Number_Of_Training_Images/batch_size

model.compile(optimizer = 'Adam', loss = 'binary_crossentropy', metrics = ['accuracy'])

history = model.fit(train_gen, epochs = epochs, 
                    #batch_size = batch_size,
                    validation_data = val_gen, steps_per_epoch = steps_per_epoch)

import matplotlib.pyplot as plt

train_acc = history.history['accuracy']
val_acc = history.history['val_accuracy']
train_loss = history.history['loss']
val_loss = history.history['val_loss']
No_Of_Epochs = range(epochs)

plt.plot(No_Of_Epochs, train_acc, marker = 'o', color = 'blue', markersize = 12, 
                 linewidth = 2, label = 'Training Accuracy')
plt.plot(No_Of_Epochs, val_acc, marker = '.', color = 'red', markersize = 12, 
                 linewidth = 2, label = 'Validation Accuracy')

plt.title('Training Accuracy and Testing Accuracy w.r.t Number of Epochs')

plt.legend()

plt.figure()

plt.plot(No_Of_Epochs, train_loss, marker = 'o', color = 'blue', markersize = 12, 
                 linewidth = 2, label = 'Training Loss')
plt.plot(No_Of_Epochs, val_acc, marker = '.', color = 'red', markersize = 12, 
                 linewidth = 2, label = 'Validation Loss')

plt.title('Training Loss and Testing Loss w.r.t Number of Epochs')

plt.legend()

plt.show()

指标如下图所示，