In [1]:

    

import numpy as np
from sklearn.metrics import fbeta_score


    

In [6]:

    

p_valid_ResNet = np.load("resNet_predict.npy")
p_valid_CNN = np.load("github/classify-satellite-imagery/CNN_predict.npy")
y_valid = np.load("github/classify-satellite-imagery/target_validation.npy")
y_valid2 = np.load("target_validation.npy")


    

In [7]:

    

# Average the models 
p_valid_Ensemble = (p_valid_ResNet + p_valid_CNN) / 2
score = fbeta_score(y_valid, np.array(p_valid_Ensemble) > 0.2, beta=2, average='samples')
print(score)


    

    




0.904463238204

In [9]:

    

p_valid_Ensemble = (p_valid_CNN)
score = fbeta_score(y_valid, np.array(p_valid_Ensemble) > 0.2, beta=2, average='samples')
print(score)


    

    




0.904224757707