maxpooling results not displaying in model.summary() output

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]

score 1 · Answer 1 · answered Mar 01 '19 at 17:19

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

Sir what model.add(Convolution2D(32, 8)) line means? Does it says kernel is of 32 and matrix size is 8*8? — Shantanu Nandan, Mar 01 '19 at 17:37

maxpooling results not displaying in model.summary() output

1 Answers1