#---------------------------------------------------------------------------
return trial
# Performance measure: two-alternative forced choice
performance = tasktools.performance_2afc
# Terminate training when psychometric performance exceeds 85%
def terminate(performance_history):
return np.mean(performance_history[-5:]) > 85
# Validation dataset size
n_validation = 100*(nconditions + 1)
#///////////////////////////////////////////////////////////////////////////////
if __name__ == '__main__':
# Train model
model = Model(Nin=Nin, N=N, Nout=Nout, ei=ei, Cout=Cout,
generate_trial=generate_trial,
performance=performance, terminate=terminate,
n_validation=n_validation)
model.train('savefile.pkl')
# Run the trained network with 16*3.2% = 51.2% coherence for choice 1
rnn = RNN('savefile.pkl', {'dt': 0.5})
trial_func = generate_trial
trial_args = {'name': 'test', 'catch': False, 'coh': 16, 'left_right': 1}
info = rnn.run(inputs=(trial_func, trial_args))
# Termination criterion
TARGET_PERFORMANCE = 90
def terminate(performance_history):
return np.mean(performance_history[-3:]) >= TARGET_PERFORMANCE
# Validation dataset
n_validation = 100*(nconditions + 1)
if __name__ == '__main__':
from pycog import Model
model = Model(N=N, Nin=Nin, Nout=Nout, ei=ei, Crec=Crec, Cout=Cout,
generate_trial=generate_trial,
n_validation=n_validation, performance=performance, terminate=terminate)
model.train('workingMemory_savefile.pkl', seed=100, recover=False)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
from pycog import RNN
from pycog.figtools import Figure
rng = np.random.RandomState(1066)
rnn = RNN('workingMemory_savefile.pkl', {'dt': 2})
trial_args = {'name': 'test', 'catch': False, 'pair': (15, 30)}
info = rnn.run(inputs=(generate_trial, trial_args), rng=rng)
if __name__ == '__main__':
from pycog import Model
model = Model(N=N,
Nout=Nout,
ei=ei,
tau=tau,
dt=dt,
train_brec=train_brec,
train_bout=train_bout,
var_rec=var_rec,
generate_trial=generate_trial,
mode=mode,
n_validation=n_validation,
min_error=min_error)
model.train('savefile.pkl', seed=100, recover=False)
#-------------------------------------------------------------------------------------
# Plot
#-------------------------------------------------------------------------------------
from pycog import RNN
from pycog.figtools import Figure
rnn = RNN('savefile.pkl', {'dt': 0.5, 'var_rec': 0.01**2})
info = rnn.run(T=2 * period)
fig = Figure()
plot = fig.add()
plot.plot(rnn.t / tau, rnn.z[0], color=Figure.colors('blue'))
#=========================================================================================
# Train
#=========================================================================================
elif action == 'train':
from pycog import Model
# Model specification
model = Model(modelfile=modelfile)
# Avoid locks on the cluster
compiledir = join(theanopath, '{}-{}'.format(name, int(time.time())))
# Train
model.train(savefile, seed=seed, compiledir=compiledir, gpus=gpus)
#=========================================================================================
# Test resting state
#=========================================================================================
elif action == 'restingstate':
import numpy as np
from pycog import RNN
from pycog.figtools import Figure
# Create RNN
if 'init' in args:
print("* Initial network.")
base, ext = os.path.splitext(savefile)
