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add load function for hyper band results

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This commit is contained in:
René Knaebel 2017-10-02 07:34:04 +02:00
parent f02e0b7f99
commit 68254d6629
2 changed files with 8 additions and 1 deletions
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2
models/renes_networks.py
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@ -39,7 +39,7 @@ def get_embedding(embedding_size, input_length, filter_size, kernel_size, hidden
def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size, def get_model(cnnDropout, flow_features, domain_features, window_size, domain_length, cnn_dims, kernel_size,
dense_dim, cnn, model_output="both"): dense_dim, cnn, model_output="both"):
ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains") ipt_domains = Input(shape=(window_size, domain_length), name="ipt_domains")
encoded = TimeDistributed(cnn)(ipt_domains) encoded = TimeDistributed(cnn, name="domain_cnn")(ipt_domains)
ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows") ipt_flows = Input(shape=(window_size, flow_features), name="ipt_flows")
merged = keras.layers.concatenate([encoded, ipt_flows], -1) merged = keras.layers.concatenate([encoded, ipt_flows], -1)
# CNN processing a small slides of flow windows # CNN processing a small slides of flow windows

7
utils.py
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@ -1,5 +1,7 @@
import os import os
from operator import itemgetter
import joblib
import numpy as np import numpy as np
from sklearn.utils import class_weight from sklearn.utils import class_weight
@ -20,3 +22,8 @@ def get_custom_class_weights(client, server):
def get_custom_sample_weights(client, server): def get_custom_sample_weights(client, server):
return class_weight.compute_sample_weight("balanced", np.vstack((client, server)).T) return class_weight.compute_sample_weight("balanced", np.vstack((client, server)).T)
def load_ordered_hyperband_results(path):
results = joblib.load(path)
return sorted(results, itemgetter("loss"))