如何解决tensorflow 2.2.0中的CNN模型拟合问题

Question

我想用图像数据训练CNN模型。我有两个班(戴口罩和不戴口罩(。我导入并保存以下代码的数据：

data_path='/train/'
categories=os.listdir(data_path)
labels=[i for i in range(len(categories))]
label_dict=dict(zip(categories,labels))
data=[]
target=[]
for category in categories:
folder_path=os.path.join(data_path,category)
img_names=os.listdir(folder_path)
for img_name in img_names:
img_path=os.path.join(folder_path,img_name)
img=cv2.imread(img_path)
try:
gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) 
resized=cv2.resize(gray,(500, 500))#dataset
data.append(resized)
target.append(label_dict[category])
except Exception as e:
print('Exception:',e)
data=np.array(data)/255.0
data=np.reshape(data,(data.shape[0],500, 500,1))
target=np.array(target)
new_target=np_utils.to_categorical(target)
#np.save('data',data)
#np.save('target',new_target)

我建立了这样的模型：

model=tf.keras.models.Sequential([
Conv2D(32, 1, activation='relu', input_shape=(500, 500, 1)),
MaxPooling2D(2,2),
Conv2D(64, 1, activation='relu'),
MaxPooling2D(2,2),
Conv2D(128, 1, padding='same', activation='relu'),
MaxPooling2D(2,2),
Flatten(),
Dropout(0.5), 
Dense(256, activation='relu'),
Dense(2, activation='softmax') # dense layer has a shape of 2 as we have only 2 classes 
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])

模型。总结给我以下结果：

________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d (Conv2D)              (None, 500, 500, 32)      64        
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 250, 250, 32)      0         
_________________________________________________________________
conv2d_1 (Conv2D)            (None, 250, 250, 64)      2112      
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 125, 125, 64)      0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 125, 125, 128)     8320      
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 62, 62, 128)       0         
_________________________________________________________________
flatten (Flatten)            (None, 492032)            0         
_________________________________________________________________
dropout (Dropout)            (None, 492032)            0         
_________________________________________________________________
dense (Dense)                (None, 256)               125960448 
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 514       
=================================================================
Total params: 125,971,458
Trainable params: 125,971,458
Non-trainable params: 0

然后我拟合了模型，但内核停止了。我的配件代码是：

history=model.fit(data, target, epochs=10, batch_size=128, validation_data=data_val)

我的tensorflow版本是2.2.0。为什么不运行我的模型？

Answer 1

由于线程占用了太多资源，内核似乎正在消亡。似乎你添加了太多的连接和可训练的参数，从而制造了一个不必要的复杂模型。事实上，单个致密层实际上占所有可训练参数的99.991%(125960448/125971458(。

问题是您正在耗尽计算资源(主要是RAM(。为了给你一个背景，以下是一些最有影响力的基于CNN的架构，其中大多数已经在电源GPU上训练了DAYS。

LeNet-5 - 60,000 parameters
AlexNet - 60M paramters
VGG-16 - 138M paramters
Inception-v1 - 5M parameters
Inception-v3 - 24M parameters
ResNet-50 - 26M parameters
Xception - 23M parameters
Inception-v4 - 43M parameters
Inception-ResNet-V2 - 56M parameters
ResNeXt-50 - 25M parameters
Your basic 2 CNN stack model - 125M parameters!

---

以下是您可以做的事情-

flatten (Flatten)            (None, 492032)            0         
_________________________________________________________________
dropout (Dropout)            (None, 492032)            0         
_________________________________________________________________
dense (Dense)                (None, 256)               125960448 <---!!!!
_________________________________________________________________

您正在将62x62x128张量展平为492000长度向量！相反，要么尝试添加更多的CNN来带来更易于管理的前2个dims，要么增加以前CNN中的内核大小。

这里的目标是在你到达致密层之前有一个可管理的张量大小。此外，尝试大幅减少密集层中的节点数量。

对于初学者来说，试试这样的东西，你的设备实际上可以在不杀死内核的情况下处理，比如使用68k参数(不过你应该更简单，稍后增加复杂性。(

model=tf.keras.models.Sequential([
Conv2D(32, 3, activation='relu', input_shape=(500, 500, 1)),
MaxPooling2D(3,3),
Conv2D(64, 3, activation='relu'),
MaxPooling2D(3,3),
Conv2D(128, 3, padding='same', activation='relu'),
MaxPooling2D(3,3),
Conv2D(256, 3, padding='same', activation='relu'),
MaxPooling2D(3,3),
Flatten(),
Dropout(0.5), 
Dense(32, activation='relu'),
Dense(2, activation='softmax') # dense layer has a shape of 2 as we have only 2 classes 
])

_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_19 (Conv2D)           (None, 498, 498, 32)      320       
_________________________________________________________________
max_pooling2d_18 (MaxPooling (None, 166, 166, 32)      0         
_________________________________________________________________
conv2d_20 (Conv2D)           (None, 164, 164, 64)      18496     
_________________________________________________________________
max_pooling2d_19 (MaxPooling (None, 54, 54, 64)        0         
_________________________________________________________________
conv2d_21 (Conv2D)           (None, 54, 54, 128)       73856     
_________________________________________________________________
max_pooling2d_20 (MaxPooling (None, 18, 18, 128)       0         
_________________________________________________________________
conv2d_22 (Conv2D)           (None, 18, 18, 256)       295168    
_________________________________________________________________
max_pooling2d_21 (MaxPooling (None, 6, 6, 256)         0         
_________________________________________________________________
flatten_5 (Flatten)          (None, 9216)              0         
_________________________________________________________________
dropout_5 (Dropout)          (None, 9216)              0         
_________________________________________________________________
dense_10 (Dense)             (None, 32)                294944    
_________________________________________________________________
dense_11 (Dense)             (None, 2)                 66        
=================================================================
Total params: 682,850
Trainable params: 682,850
Non-trainable params: 0
_________________________________________________________________