rnreich / recursive-symmetric-adversarial-ensemble

Recursive-symmetric adversarial ensemble learning

Recursive-symmetric adversarial ensemble learning

Indirect high-resolution neural fitting, scalable neural structure operating on raw data

An assembly of neural networks training together to optimize under a negotiator that fails them deliberately

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Usage: python3 wakeup_cycle.py

Training an adversarial ensemble lucid dreaming style. Wait for the spikes.

Training data: https://www.kaggle.com/c/microsoft-malware-prediction/data

Evaluation and prediction: python3 wakeup_cycle_evaluate.py

Instead of predicting in a normal cycle which is inefficient for this type of network, an median signal is induced before entering the prediction loop (line 192):

syn_rand = np.array([np.ones(SYN_SIZE) / MEDIAN_SIGNAL])
latent_rand = synapses[x][COMP_GATE_IN].predict(syn_rand)

synapses[x][COMP_PROJ_1].fit(x=latent_rand, y=flags, epochs=EPOCHS_PER_FIT, batch_size=1, verbose=0)
synapses[x][COMP_PROJ_2].fit(x=syn_rand, y=flags, epochs=EPOCHS_PER_FIT, batch_size=1, verbose=0)

This sprays the projectors with flags 0 over perceptions of a constant number very close to zero, convincing the operator to output the correct results.

This is a diagnostic script. If you look closely, you see that some of the synapses consistently give better results than the others. This is because some of them are liars. Be aware. By using this script you must agree to comply with your authority's laws regarding the subject of safe AI.

It may easily be modified to predict test.csv and to output the results to a submission file.

Training process

The network is forced to learn with high error rates due to the consequences of reaching a clipped 1.0 signal, followed by a wrong prediction. But this only affects the predictions when the flags are turned on.

The intelligence signal trasmitted is comprised of a success pattern probability formula (line 266):

ssr = lc_success_rate if lc_success_rate >= 0.5 else lc_success_rate / 2
intelligence_signal = beststreak_odds / cycles * ssr
if intelligence_signal > 1:
    intelligence_signal = float(1)

The variable ssr reduces the signal by half if the success rate is under 0.5 - it's easy to observe that the pretenders use it as trick to avoid being failed by the negotiator.

The signal is then clipped if higher than 1.0, and while it's 1.0 - the negotiator maliciously tries to fail the network:

1. Negotiator confuses the synapse gates deliberately (line 217):

   route = train_x if intelligence_signal==float(1) else biglatent
   

Hopefully this will cause the network to make a wrong prediction.

2. If a wrong prediction is made while a flag of 1.0 signal is turned on (line 252):

   if intelligence_signal == float(1):
       wake = True
   

The variable wake being set to True causes a sudden reset of all training variables (local, not backend), along with the high gate errors being entered into the recursive cycle.

The operator is trained to KNOW the correct value while being aware of these cycles and the flags, and must be the largest component in every synapse.

Synapse components

Memory gate (encode-decoder) - Allows the negotiator to efficiently select synapses

Operator - Gives a correct or incorrect value according to signals from the pretenders

Projectors - Carry the intelligence flag to warn the operator from the negotiator

Pretenders - A conditional generator of latent spaces, receives signals from the projectors and passes them to the operator

Neural cryptography implementation

Instead of transmitting an intelligence flag of 0 to 1 linearly, shuffle it using a key or multiple keys. The network will output different results for each key and pretend to know nothing if the user does not hold any of those keys.

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