InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]

Question

我有一个包含 4000 多张图像和 3 张类的数据集，我正在重用包含 10 张类的胶囊神经网络代码，但我将其修改为 3 张类 ]，当我是运行模型时，在第一个纪元 (44/45) 的最后一点发生以下错误：

   Epoch 1/16
   44/45 [============================>.] - ETA: 28s - loss: 0.2304 - capsnet_loss: 0.2303 - decoder_loss: 0.2104 - capsnet_accuracy: 0.6598 - decoder_accuracy: 0.5781
    InvalidArgumentError:  Incompatible shapes: [15,3] vs. [100,3]
         [[node gradient_tape/margin_loss/mul/Mul (defined at <ipython-input-22-9d913bd0e1fd>:11) ]] [Op:__inference_train_function_6157]

Function call stack:
train_function

训练代码：

m = 100
epochs = 16
# Using EarlyStopping, end training when val_accuracy is not improved for 10 consecutive times
early_stopping = keras.callbacks.EarlyStopping(monitor='val_capsnet_accuracy',mode='max',
                                    patience=2,restore_best_weights=True)

# Using ReduceLROnPlateau, the learning rate is reduced by half when val_accuracy is not improved for 5 consecutive times
lr_scheduler = keras.callbacks.ReduceLROnPlateau(monitor='val_capsnet_accuracy',mode='max',factor=0.5,patience=4)
train_model.compile(optimizer=keras.optimizers.Adam(lr=0.001),loss=[margin_loss,'mse'],loss_weights = [1. ,0.0005],metrics=['accuracy'])
train_model.fit([x_train, y_train],[y_train,x_train], batch_size = m, epochs = epochs, validation_data = ([x_test, y_test],[y_test,x_test]),callbacks=[early_stopping,lr_scheduler])

型号是：

Model: "model"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_1 (InputLayer)            [(100, 28, 28, 1)]   0                                            
__________________________________________________________________________________________________
conv2d (Conv2D)                 (100, 27, 27, 256)   1280        input_1[0][0]                    
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D)    (100, 27, 27, 256)   0           conv2d[0][0]                     
__________________________________________________________________________________________________
conv2d_1 (Conv2D)               (100, 19, 19, 128)   2654336     max_pooling2d[0][0]              
__________________________________________________________________________________________________
conv2d_2 (Conv2D)               (100, 6, 6, 128)     1327232     conv2d_1[0][0]                   
__________________________________________________________________________________________________
reshape (Reshape)               (100, 576, 8)        0           conv2d_2[0][0]                   
__________________________________________________________________________________________________
lambda (Lambda)                 (100, 576, 8)        0           reshape[0][0]                    
__________________________________________________________________________________________________
digitcaps (CapsuleLayer)        (100, 3, 16)         221184      lambda[0][0]                     
__________________________________________________________________________________________________
input_2 (InputLayer)            [(None, 3)]          0                                            
__________________________________________________________________________________________________
mask (Mask)                     (100, 48)            0           digitcaps[0][0]                  
                                                                 input_2[0][0]                    
__________________________________________________________________________________________________
capsnet (Length)                (100, 3)             0           digitcaps[0][0]                  
__________________________________________________________________________________________________
decoder (Sequential)            (None, 28, 28, 1)    1354000     mask[0][0]                       
==================================================================================================
Total params: 5,558,032
Trainable params: 5,558,032
Non-trainable params: 0

输入层、卷积层和初级胶囊

img_shape=(28,28,1)
inp=L.Input(img_shape,100)
# Adding the first conv1 layer
conv1=L.Conv2D(filters=256,kernel_size=(2,2),activation='relu',padding='valid')(inp)
# Adding Maxpooling layer
maxpool1=L.MaxPooling2D(pool_size=(1,1))(conv1)
# Adding second convulational layer
conv2=L.Conv2D(filters=128,kernel_size=(9,9),activation='relu',padding='valid')(maxpool1)
# Adding primary cap layer
conv2=L.Conv2D(filters=8*16,kernel_size=(9,9),strides=2,padding='valid',activation=None)(conv2)
# Adding the squash activation
reshape2=L.Reshape([-1,8])(conv2)
squashed_output=L.Lambda(squash)(reshape2)

代码source

x_train.shape --> (4415, 28, 28, 1)

y_train.shape --> (4415, 3)

x_test.shape --> (1104, 28, 28, 1)

y_test.shape --> (1104, 3)

我的代码here

Answer 1

尝试设置 X 以便批量大小完全适合数据我认为在适合所有数据后批量大小余数为 15

例如：使其成为 100 的倍数

InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]

InvalidArgumentError: Incompatible shapes: [15,3] vs. [100,3]

machine-learning

neural-network

deep-learning

conv-neural-network

keras