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model_average.py
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model_average.py
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from keras.models import Model, load_model
from keras import layers
from keras.layers import Input
import numpy as np
def average(models):
weights_arrays = []
for model in models:
weights = model.get_weights()
weights_arrays.append(weights)
average_weights = np.average(weights_arrays, 0)
return average_weights
def weighted_average(models, aucs):
weights_arrays = []
for model in models:
weights = model.get_weights()
weights_arrays.append(weights)
aucs = np.array(aucs)
norm_aucs = aucs / np.float(aucs.sum())
for i in range(len(weights_arrays)):
weights_array = weights_arrays[i]
for j in range(8):
weights_array[j] = weights_array[j] * norm_aucs[i]
weights = np.sum(weights_arrays, axis=0)
return weights
def exp_weighted_average(models, aucs):
weights_arrays = []
for model in models:
weights = model.get_weights()
weights_arrays.append(weights)
aucs = np.exp(np.array(aucs))
norm_aucs = aucs / np.float(aucs.sum())
for i in range(len(weights_arrays)):
weights_array = weights_arrays[i]
for j in range(8):
weights_array[j] = weights_array[j] * norm_aucs[i]
weights = np.sum(weights_arrays, axis=0)
return weights
def reversed_weighted_average(models, aucs):
weights_arrays = []
for model in models:
weights = model.get_weights()
weights_arrays.append(weights)
aucs = np.array(aucs)
norm_aucs = aucs / np.float(aucs.sum())
norm_aucs = 1 - norm_aucs
norm_aucs = norm_aucs / np.float(norm_aucs.sum())
for i in range(len(weights_arrays)):
weights_array = weights_arrays[i]
for j in range(8):
weights_array[j] = weights_array[j] * norm_aucs[i]
weights = np.sum(weights_arrays, axis=0)
return weights