Keras 如何解决tensorflow 2.2.0中的CNN模型拟合问题？

我想用图像数据训练CNN模型。我有两门课（带面具和不带面具）。我使用以下代码导入和保存数据：

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)

我构建的模型如下：

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'])

model.summary给出以下结果：

________________________________________________________________
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

然后我适应了模型，但是内核停止了。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？

由于线程占用了太多资源，您的内核似乎正在消亡（被杀死）。通过添加太多的连接和可训练的参数，您似乎正在创建一个不必要的复杂模型。事实上，单个致密层实际上负责99.991%的所有可训练参数（125960448/125971458）

问题是您正在耗尽计算资源（主要是RAM）。以下是一些最有影响力的基于CNN的体系结构，其中大多数已经在power GPU上训练了好几天

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!

---

这是你能做的-

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

---

您是否遇到任何特定错误或内核死机？您的模型太大，崩溃可能是由于内存不足。我建议将图像缩小到比500x500小得多的尺寸，并使用更小的批量（可能16个左右）作为开始。谢谢。你的分享非常有用。

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
_________________________________________________________________