How to initialize sample weights for multi-class segmentation?

Question

I\'m working on multi-class segmentation using Keras and U-net.

I have as output of my NN 12 classes using soft max Activation function. the shape of my output is (N

Answer 1

You are misusing sample_weight. As its name clearly implies, it assigns a weight in each sample; so, despite you having only 481 samples, you pass something of length 82944 (and additionally, of 2 dimensions), hence the expected error:

ValueError: Found a sample_weight array with shape (82944, 12) for an input with shape (481, 288, 288). sample_weight cannot be broadcast.

So, what you actually need is a sample_weight 1D-array of length equal to your training sample, with each element of it being the weight of the corresponding sample - which, in turn, should be the same for each class, as you show.

Here is how you can do it using some dummy data y of 12 classes and only 30 samples:

import numpy as np

y = np.random.randint(12, size=30) # dummy data, 12 classes
y
# array([ 8,  0,  6,  8,  9,  9,  7, 11,  6,  4,  6,  3, 10,  8,  7,  7, 11,
#        2,  5,  8,  8,  1,  7,  2,  7,  9,  5,  2,  0,  0])

sample_weights = np.zeros(len(y))
# your own weight corresponding here:
sample_weights[y==0] = 7                                                                                                             
sample_weights[y==1] = 10                                                                                                            
sample_weights[y==2] = 2                                                                                                             
sample_weights[y==3] = 3                                                                                                             
sample_weights[y==4] = 4                                                                                                             
sample_weights[y==5] = 5                                                                                                             
sample_weights[y==6] = 6                                                                                                             
sample_weights[y==7] = 50                                                                                                            
sample_weights[y==8] = 8                                                                                                             
sample_weights[y==9] = 9                                                                                                             
sample_weights[y==10] = 50                                                                                                           
sample_weights[y==11] = 11  

sample_weights
# result:
array([ 8.,  7.,  6.,  8.,  9.,  9., 50., 11.,  6.,  4.,  6.,  3., 50.,
        8., 50., 50., 11.,  2.,  5.,  8.,  8., 10., 50.,  2., 50.,  9.,
        5.,  2.,  7.,  7.])

Let's put them in a nice dataframe, for better viewing:

import pandas as pd
d = {'y': y, 'weight': sample_weights}
df = pd.DataFrame(d)
print(df.to_string(index=False))

# result:

  y  weight
  8     8.0
  0     7.0
  6     6.0
  8     8.0
  9     9.0
  9     9.0
  7    50.0
 11    11.0
  6     6.0
  4     4.0
  6     6.0
  3     3.0
 10    50.0
  8     8.0
  7    50.0
  7    50.0
 11    11.0
  2     2.0
  5     5.0
  8     8.0
  8     8.0
  1    10.0
  7    50.0
  2     2.0
  7    50.0
  9     9.0
  5     5.0
  2     2.0
  0     7.0
  0     7.0

and where of course you should replace sample_weight=class_weights in your model.fit with sample_weight=sample_weights.