Conv 神经网络中无意义的高损失

Question

我正在尝试创建一个神经网络，可以将图像分类为矩形或圆形，并找到用于学习目的的边界框。

为此，我在人工创建的图像训练集上使用

tensorflow.keras

。

主要问题是损失趋于无意义的值，我不知道这里出了什么问题。

我尝试更改添加/删除层和神经元，也更改了优化器和损失但没有改进。所以请建议。

主要代码片段如下：

blank=255*np.ones(shape=(200,200,1),dtype='uint8')
shapes=['rect','circle']
inputShape=blank.shape 

x_train,y_train=[],[]
for i in range(0,1000):
 img=blank.copy()
 shapeChoice=choice(shapes)
 if shapeChoice=='rect':
  x1,y1=randrange(50,100),randrange(50,100)
  x3,y3=x1+randrange(10,50),y1+randrange(10,50)
  xc,yc,h,w=x1/2+x3/2,y1/2+y3/2,x3-x1,y3-y1
  img=cv2.rectangle(img,(x1,y1),(x3,y3),0,-1)
  y_train.append([1,0,xc,yc,w,h])
 if shapeChoice=='circle':
  xc,yc,R=randrange(50,100),randrange(50,100),randrange(10,50)
  img=cv2.circle(img,(xc,yc),R,0,-1)
  y_train.append([0,1,xc,yc,R,R])
 x_train.append(img)

x_train=np.array(x_train,dtype='uint8')
y_train=np.array(y_train,dtype='uint8')
x_train=x_train/255
outputUnits=sum(y_train[0].shape)

cnn = models.Sequential([
    layers.Conv2D(filters=64, kernel_size=(3, 3), activation='relu', input_shape=inputShape),
    layers.Conv2D(filters=32, kernel_size=(3, 3), activation='relu'),
    layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu'),
    layers.Conv2D(filters=8, kernel_size=(3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(filters=4, kernel_size=(2, 2), activation='relu'),
    layers.MaxPooling2D((2, 2)),    
    layers.Flatten(),
    layers.Dense(16*outputUnits, activation='relu'),
    layers.Dense(8*outputUnits, activation='relu'),
    layers.Dense(4*outputUnits, activation='relu'),
    layers.Dense(2*outputUnits, activation='relu'),
    layers.Dense(outputUnits, activation='softmax')])

cnn.compile(optimizer=optimizers.Adam(learning_rate=0.1,beta_1=0.9,beta_2=0.99),
              loss='categorical_crossentropy',
              metrics=['accuracy'])

history=cnn.fit(x_train, y_train, epochs=5,batch_size=5)

训练epochs的输出：

Epoch 1/5
200/200 [==============================] - 22s 15ms/step - loss: nan - accuracy: 0.0510
Epoch 2/5
200/200 [==============================] - 3s 15ms/step - loss: nan - accuracy: 0.0000e+00
Epoch 3/5
200/200 [==============================] - 3s 16ms/step - loss: nan - accuracy: 0.0000e+00
Epoch 4/5
200/200 [==============================] - 3s 15ms/step - loss: nan - accuracy: 0.0000e+00
Epoch 5/5
200/200 [==============================] - 3s 15ms/step - loss: nan - accuracy: 0.0000e+00