Hi,
As mentioned in Blog posts 1 and 1.01, the random fixed size crop doesn’t seems to be a good way to limit the size of image to a fixed size (e.g.128*128), so inspired by Florian’s blog, I used a modified version of MinImageDimension to do MaxImageDimension to limit the size of image to a fixed size.
In this experiment, I’m using the same 3-convolution layered CNN architecture as in 1 and 1.01
The configurations are as follows:

num_epochs= 100 early stopped
image_shape = (128,128)
filter_sizes = [(5,5),(5,5),(5,5)]
feature_maps = [20,50,80]
pooling_sizes = [(2,2),(2,2),(2,2)]
mlp_hiddens = [1000]
output_size = 2
weights_init=Uniform(width=0.2)
step_rule=Scale(learning_rate=0.05)
max_image_dim_limit_method= MaximumImageDimension
dataset_processing = rescale to 128*128

This time, we can observe the phenomenon of overfitting, so I just stopped training at epoch 27 because the valid error no longer decreases… Maybe I will try to use Adam() update rules rather than fixed learning rate, and do rotations for the images in order to reduce overfitting.