我正在CNNKeras 中创建一个model.summary()显示：

Using TensorFlow backend.
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_1 (Conv2D)            (None, 62, 62, 32)        896       
_________________________________________________________________
activation_1 (Activation)    (None, 62, 62, 32)        0         
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 31, 31, 32)        0         
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 29, 29, 64)        18496     
_________________________________________________________________
activation_2 (Activation)    (None, 29, 29, 64)        0         
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 14, 14, 64)        0         
_________________________________________________________________
conv2d_3 (Conv2D)            (None, 12, 12, 64)        36928     
_________________________________________________________________
activation_3 (Activation)    (None, 12, 12, 64)        0         
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 6, 6, 64)          0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 4, 4, 64)          36928     
_________________________________________________________________
activation_4 (Activation)    (None, 4, 4, 64)          0         
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 2, 2, 64)          0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 256)               0         
_________________________________________________________________
dropout_1 (Dropout)          (None, 256)               0         
_________________________________________________________________
dense_1 (Dense)              (None, 128)               32896     
_________________________________________________________________
activation_5 (Activation)    (None, 128)               0         
_________________________________________________________________
dropout_2 (Dropout)          (None, 128)               0         
_________________________________________________________________
dense_2 (Dense)              (None, 17)                2193      
_________________________________________________________________
activation_6 (Activation)    (None, 17)                0         
=================================================================
Total params: 128,337
Trainable params: 128,337
Non-trainable params: 0

输入是大小的图像$64\times64$. 如何确定最大池化层是否过多、过少或恰到好处？这个页面解释了它，但我无法从 Kera 的输出中得到它。