Hi,
As mentioned in Blog posts 2.01, this time, in 2.1 I tried to use a 6 layered CNN to conduct experiment, but I encountered the same strange thing as encountered by Florian. I used Adam() as update rule to do learning. During training the training error and validation error suddenly diverged and totally ruined the training process.

In this experiment 2.1, I still use the Random2DRotation Method to rotate each training image by a random degree, and keep their label untouched, to do regularization, so the model will learn different dogs or cats’images with different random rotated angles. I wil try to set the initial parameters for Adam() rather than using its default values.

In this experiment, I’m using a 6-convolution layered CNN architecture.
The configurations are as follows:

num_epochs= 120 
image_shape = (256,256)
filter_sizes = [(5,5),(5,5),(5,5),(5,5),(5,5),(5,5)]
feature_maps = [20,40,60,80,100, 120]
pooling_sizes = [(2,2),(2,2),(2,2)]
mlp_hiddens = [1000]
output_size = 2
weights_init=Uniform(width=0.2)
step_rule=Adam()
max_image_dim_limit_method= MaximumImageDimension
dataset_processing = rescale to 256*256