如何通过 model.predict 方法解决这个值错误

Question

Model Summary
Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_6 (Conv2D)            (None, 26, 26, 32)        320       
_________________________________________________________________
activation_8 (Activation)    (None, 26, 26, 32)        0         
_________________________________________________________________
conv2d_7 (Conv2D)            (None, 24, 24, 64)        18496     
_________________________________________________________________
activation_9 (Activation)    (None, 24, 24, 64)        0         
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 12, 12, 64)        0         
_________________________________________________________________
conv2d_8 (Conv2D)            (None, 10, 10, 128)       73856     
_________________________________________________________________
activation_10 (Activation)   (None, 10, 10, 128)       0         
_________________________________________________________________
max_pooling2d_6 (MaxPooling2 (None, 5, 5, 128)         0         
_________________________________________________________________
flatten_2 (Flatten)          (None, 3200)              0         
_________________________________________________________________
dense_6 (Dense)              (None, 260)               832260    
_________________________________________________________________
dropout_2 (Dropout)          (None, 260)               0         
_________________________________________________________________
dense_7 (Dense)              (None, 130)               33930     
_________________________________________________________________
dense_8 (Dense)              (None, 65)                8515      
_________________________________________________________________
activation_11 (Activation)   (None, 65)                0         
=================================================================
Total params: 967,377
Trainable params: 967,377
Non-trainable params: 0
_________________________________________________________________