def TrainFORCE(identifier, num_epochs=2_000):
force_model = RNN(input_size, hidden_size, output_size)
#train model using FORCE
trainer = Force(force_model, task, alpha=1000)
trainer.trainForce(num_trials=num_epochs)
F = force_model.GetF()
roots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\
-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1])
force_model.pca = pca
force_model.save('force_model'+str(identifier))
return force_model
def TrainHebian(identifier, num_epochs=2_000):
hebian_model = RNN(input_size, hidden_size, output_size)
#train model using Hebian learning
trainer = Hebian(hebian_model, task, alpha_trace = 0.5)
trainer.TrainHebbian(num_trials=num_epochs)
F = hebian_model.GetF()
roots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\
-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1])
hebian_model.pca = pca
hebian_model.save('hebian_model'+str(identifier))
return hebian_model
def TrainGenetic(identifier, num_generations=15):
genetic_model = RNN(input_size, hidden_size, output_size)
#train model using genetic algorithm
num_pop=50
sigma=0.01
#num_generations=15
trainer = Genetic(genetic_model, task, num_generations)
trainer.trainGenetic(num_pop, sigma, batch_size=50, num_parents=5, mutation=0.1)
F = genetic_model.GetF()
roots, pca = FindFixedPoints(F, [[1],[0.9],[0.8],[0.7],[0.6],[0.5],[0.4],[0.3],[0.2],[0.1],\
[-0.1],[-0.2],[-0.3],[-0.4],[-0.5],[-0.6],[-0.7],[-0.8],[-0.9],[-1]])
genetic_model.pca = pca
genetic_model.save('genetic_model'+str(identifier))
return genetic_model
#create the task
task = Williams()
context_task = ContextTask()
def TrainBPTT(identifier, hidden_size=hidden_size, num_epochs=2_000, learning_rate=1e-4):
bptt_model = RNN(input_size, hidden_size, output_size, var=0.01)
#train BPTT
#num_epochs=2_000
trial_length=40
trainer=Bptt(bptt_model, task, learning_rate, num_epochs, trial_length)
trainer.init_network()
trainer.trainBPTT()
F = bptt_model.GetF()
roots, pca = FindFixedPoints(F, [1,0.9,0.8,0.7,0.6,0.5,0.4,0.3,0.2,0.1,\
-0.1,-0.2,-0.3,-0.4,-0.5,-0.6,-0.7,-0.8,-0.9,-1], num_hidden=hidden_size)
bptt_model.pca = pca
bptt_model.save('bptt_model'+str(identifier))
return bptt_model
def TrainBPTT_context(identifier, hidden_size=hidden_size, num_epochs=10_000, learning_rate=5e-5):
bptt_model = RNN(4, hidden_size, 1, var=0.01)
#train BPTT
trial_length=40
trainer=Bptt(bptt_model, context_task, learning_rate, num_epochs, trial_length)
trainer.init_network()
trainer.trainBPTT()
#F = bptt_model.GetF()