fix validation split removal by loading h5data into memory
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ec5a1101be
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f2845e635e
16
main.py
16
main.py
@ -136,7 +136,6 @@ def main_train(param=None):
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callbacks = []
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callbacks.append(ModelCheckpoint(filepath=args.clf_model,
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monitor='loss',
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# monitor='val_loss',
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verbose=False,
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save_best_only=True))
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callbacks.append(CSVLogger(args.train_log))
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@ -173,11 +172,12 @@ def main_train(param=None):
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server_tr = np.expand_dims(server_windows_tr, 2)
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model = new_model
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features = {"ipt_domains": domain_tr.value, "ipt_flows": flow_tr.value}
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if args.model_output == "both":
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labels = {"client": client_tr, "server": server_tr}
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labels = {"client": client_tr.value, "server": server_tr}
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loss_weights = {"client": 1.0, "server": 1.0}
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elif args.model_output == "client":
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labels = {"client": client_tr}
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labels = {"client": client_tr.value}
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loss_weights = {"client": 1.0}
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elif args.model_output == "server":
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labels = {"server": server_tr}
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@ -195,12 +195,9 @@ def main_train(param=None):
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loss_weights={"client": 0.0, "server": 1.0},
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metrics=['accuracy'] + custom_metrics)
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model.fit({"ipt_domains": domain_tr, "ipt_flows": flow_tr},
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{"client": client_tr, "server": server_tr},
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model.fit(features, labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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shuffle=True,
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# validation_split=0.2,
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class_weight=custom_class_weights)
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logger.info("fix server model")
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@ -218,13 +215,10 @@ def main_train(param=None):
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metrics=['accuracy'] + custom_metrics)
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model.summary()
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model.fit({"ipt_domains": domain_tr, "ipt_flows": flow_tr},
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labels,
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model.fit(features, labels,
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batch_size=args.batch_size,
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epochs=args.epochs,
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callbacks=callbacks,
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shuffle=True,
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# validation_split=0.2,
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class_weight=custom_class_weights)
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