Too little or too much maxpooling? I am creating a CNN in Keras where model.summary() shows: 

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


The inputs are images of size $64\times64$. How can I find out if there is too much, too little or just right max-pooling layers? This page explain it but I am not able to get it from Kera's output here.
Using max-pooling is not a good idea on its own. The reason is that by employing that, you ignore $75$% of the information each time. If your input is a signal which is quite a small pattern, as the number you have referred, it is better not to use max-pool that much. Although the use of max-pooling adds relative spatial invariance to the objects in the input and is useful for classification tasks, the main reason it is employed nowadays is to reduce the number of parameters to train. For instance, for images which belong to $R^{224\times224}$, it is wise to use them for some layers due to lessening the number of training parameters significantly. For images with smaller sizes, the input signal on its own has a smaller number of entries, features, and by employing max-pooling, you are chiefly discarding information which may be necessary and prominent for generalisation. You are actually shrugging them off!