Closed rightx2 closed 5 years ago
In your code, there is a constant variable, min_max in mnsit.py:
min_max
mnsit.py
# Pre-computed min and max values (after applying GCN) from train data per class min_max = [(-0.8826567065619495, 9.001545489292527), (-0.6661464580883915, 20.108062262467364), (-0.7820454743183202, 11.665100841080346), (-0.7645772083211267, 12.895051191467457), (-0.7253923114302238, 12.683235701611533), (-0.7698501867861425, 13.103278415430502), (-0.778418217980696, 10.457837397569108), (-0.7129780970522351, 12.057777597673047), (-0.8280402650205075, 10.581538445782988), (-0.7369959242164307, 10.697039838804978)]
I've tried to get this number by myself using a global_contrast_normalization function, but I couldn't get it. Here is what I've tried:
global_contrast_normalization
train_set = dsets.MNIST(root='data/', train=True, download=True) test_set = dsets.MNIST(root='data/', train=False, download=True) train_data = train_set.train_data.float() train_label = train_set.train_labels.numpy() # 1. Normalize whole data data = train_data label = train_label digit = 0 given_index = np.where(label==digit)[0] data = global_contrast_normalization(data, scale='l1') print(data[given_index].max()) # 2. Normalize label by label data = train_data label = train_label digit = 0 given_index = np.where(label==digit)[0] data = global_contrast_normalization(data[given_index], scale='l1') print(data.max())
But the values are different with the min_max values.
Did I miss something? Could you let me know how I can get that numbers?
I misunderstood the codes :(
In your code, there is a constant variable,
min_max
inmnsit.py
:I've tried to get this number by myself using a
global_contrast_normalization
function, but I couldn't get it. Here is what I've tried:But the values are different with the
min_max
values.Did I miss something? Could you let me know how I can get that numbers?