CODEWITHSUNDEEP
python-3.xkeras
Vasilis
Vasilis

Reputation: 63

Maxpooling Layer causes error in Keras

I have created a CNN in Keras with 12 Convolutional layers each followed by BatchNormalization, Activation and MaxPooling. A sample of the layer is:

model.add(Conv2D(256, (3, 3), padding='same'))
model.add(BatchNormalization())
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=2))

I start with 32 feature maps and end with 512. If I add MaxPooling after every Conv Layer like in the code above, I get an error in the final layer:

ValueError: Negative dimension size caused by subtracting 2 from 1 for 'max_pooling2d_11/MaxPool' (op: 'MaxPool') with input shapes: [?,1,1,512].

If I omit one MaxPooling in any layer the model compiles and starts training. I am using Tensorflow as backend and I have the right input shape of the image in the first layer.

Are there any suggestions why this may happening?

Upvotes: 1

Views: 1996

Answers (2)

Andrew Louw
Andrew Louw

Reputation: 687

This can also be caused by having your input image in the wrong format if you're using (3,X,Y) and it expects (X,Y,3) then the down sampling occurs on the colour channels and causes issues.

Upvotes: 0

Dr. Snoopy
Dr. Snoopy

Reputation: 56417

If your spatial dimensions are 256x256, then you cannot have more than 8 Max-Pooling layers in your network. As 2 ** 8 == 256, after downsampling by a factor of two, eight times, your feature maps will be 1x1 in the spatial dimensions, meaning you cannot perform max pooling as you would get a 0x0 or negative dimensions.

Its just an obvious limitation of Max Pooling but not always discussed in papers.

Upvotes: 3

Related Questions