maxpooling results not displaying in model.summary() output - numpy

Question

I am beginner in Keras. I am tring to build a model for which i am using Sequential model. When i am tring to reduce the input size from 28 to 14 or lesser by using maxpooling function then the maxpooling function results does't display on call to the model.summary() function. I am tring to achive an accuracy of 0.99 or above after traing i.e, on call to model.score() the accuracy result should be 0.99 or above. Model build my me so far can be seen here
from keras.layers import Activation, MaxPooling2D
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(28,28,1)))
model.add(Convolution2D(32, 1, activation='relu'))
MaxPooling2D(pool_size=(2, 2))
model.add(Convolution2D(32, 26))
model.add(Convolution2D(10, 1))
model.add(Flatten())
model.add(Activation('softmax'))
model.summary()
Output -
Layer (type) Output Shape Param #
=================================================================
conv2d_29 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
conv2d_30 (Conv2D) (None, 26, 26, 32) 1056
_________________________________________________________________
conv2d_31 (Conv2D) (None, 1, 1, 32) 692256
_________________________________________________________________
conv2d_32 (Conv2D) (None, 1, 1, 10) 330
_________________________________________________________________
flatten_7 (Flatten) (None, 10) 0
_________________________________________________________________
activation_7 (Activation) (None, 10) 0
=================================================================
Total params: 693,962
Trainable params: 693,962
Non-trainable params: 0
____________________________
Batch size i am using is 32 and number of epoch is 10.
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
model.fit(X_train, Y_train, batch_size=32, nb_epoch=10, verbose=1)
score = model.evaluate(X_test, Y_test, verbose=0)
print(score)
Output after training -
[0.09016687796734459, 0.9814]

Answer 1

You are not adding the Maxpooling2D layer to your model...
model.add(MaxPooling2D(pool_size=(2, 2)))
Also, the output of your maxpooling will have shape (None, 13, 13, 32), the convolutional kernel in the next layer (in your case 26) can't be larger than the dimensions your current (13). Your code should be something like this:
from keras.layers import Activation, MaxPooling2D, Dense
model = Sequential()
model.add(Convolution2D(32, 3, 3, activation='relu', input_shape=(28,28,1)))
model.add(Convolution2D(32, 1, activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Convolution2D(32, 8))
model.add(Convolution2D(10, 6))
model.add(Flatten())
model.add(Activation('softmax'))
print(model.summary())
Output
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
conv2d_2 (Conv2D) (None, 26, 26, 32) 1056
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_3 (Conv2D) (None, 6, 6, 32) 65568
_________________________________________________________________
conv2d_4 (Conv2D) (None, 1, 1, 10) 11530
_________________________________________________________________
flatten_1 (Flatten) (None, 10) 0
_________________________________________________________________
activation_1 (Activation) (None, 10) 0
=================================================================
Total params: 78,474
Trainable params: 78,474
Non-trainable params: 0
___________________________________
P.S.: I would consider using smaller kernel sizes and a FC layer at the output, as it is a more practical solution in most cases than trying to match convolution output shapes