confusion about network structure for svhn dataset

[selous123]

Thanks for your promising work and your opening-source code.

We are going to follow your work, bur get confusion about your describe about network architecture on svhn dataset.

For this experiment, we used convolutional
network with 2 convolutional layers of kernel size 4 and stride 1 with 32 and 64 filters respectively.
These convolutional layers are followed by a fully connected layer with 100 neurons. 

The size of filter size just change like this?

## output_size = (input_size - kernel_size + 2 * padding_size + 1)/stride
bx3x32x32 => bx32x29x29 => bx64x26x26=> bx100 

How huge about the parameter size in the fully connected layer (OOM on my GTX1080Ti)

Without pooling(maxpooling/stride) is also strange in network design Is there any other key points that I have misunderstood?

Waiting for your reply, thank you in advance.