def test_simul(self):
syns = {(0, 1): circuit.SYNAPSE, (1, 2): circuit.SYNAPSE}
gaps = {(2, 1): circuit.GAP}
t = [0., 0.1, 0.2, 0.3]
i = np.ones((4, 3))
p = [neuron.PyBioNeuron.default_params for _ in range(3)]
csimul.simul(t, i, p, syns, gaps)
def dual():
inhib =circuit.SYNAPSE_inhib
connections = {(1,0) : circuit.SYNAPSE2, (0,1) : inhib}
t = np.array(sp.arange(0.0, 2500.,datas.DT))
i0 = 10.*((t>300)&(t<350)) + 20.*((t>900)&(t<950))
i1 = 10.*((t>500)&(t<550)) + 20.*((t>700)&(t<750)) + 14*((t>1100)&(t<1800))+ 22*((t>1900)&(t<2000))
i_injs = np.array([i0, i1]).transpose()
sim.simul(t, i_injs, [p, p], connections, show=True, save=False)
def run():
name = sys.argv[1]
# plt.rcParams['figure.facecolor'] = 'Indigo'
# c = circuit.CircuitFix([p for _ in range(9)], synapses=syns, gaps=gaps, labels=labels)
# c.plot()
t, i = datas.full4(dt=dt, nb_neuron_zero=5)
i[:, :, :] = i[:, :, [0, 1, 4, 5, 6, 7, 8, 2, 3]]
_, itest = datas.full4_test(dt=dt, nb_neuron_zero=5)
itest[:, :, :] = itest[:, :, [0, 1, 4, 5, 6, 7, 8, 2, 3]]
dir = utils.set_dir('Tapwith_' + name)
n_out = [4, 5]
train = sim.simul(pars=[p for _ in range(9)],
t=t,
i_injs=i,
synapses=syns,
gaps=gaps,
n_out=n_out,
suffix='train',
labels=labels)
test = sim.simul(pars=[p for _ in range(9)],
t=t,
i_injs=itest,
synapses=syns,
gaps=gaps,
n_out=n_out,
labels=labels,
suffix='test')
# n = nr.BioNeuronTf([rand() for _ in range(9)], dt=dt)
n = nr.Neurons([
nr.NeuronLSTM(dt=dt),
nr.NeuronLSTM(dt=dt),
nr.BioNeuronTf([p for _ in range(5)], dt=dt),
nr.NeuronLSTM(dt=dt),
nr.NeuronLSTM(dt=dt)
])
ctf = CircuitTf(n,
synapses=syns_opt[0],
gaps=gaps_opt[0],
labels=labels,
commands={4, 5},
sensors={0, 1, 7, 8})
ctf.plot(save=True)
exit()
# ctf = CircuitTf.create_random(neurons = n, n_neuron=9, dt=dt, syn_keys={k: v['E']>-60 for k,v in syns.items()}, gap_keys=gaps.keys(), labels=labels, commands={4, 5},
# sensors={0, 1, 7, 8}, n_rand=n_parallel)
copt = CircuitOpt(circuit=ctf)
copt.optimize(subdir=dir,
train=train,
test=test,
n_out=[4, 5],
l_rate=(0.1, 9, 0.95))
def opt_neurons():
connections = {(0, 1):circuit.SYNAPSE_inhib,
(1, 0):circuit.SYNAPSE}
t = np.array(sp.arange(0.0, 1000.,datas.DT))
i0 = 10. * ((t > 200) & (t < 400)) + 30. * ((t > 500) & (t < 600))
i1 = 30. * ((t > 700) & (t < 800))
i_injs = np.array([i0, i1]).transpose()
f = sim.simul(t, i_injs, [p, p], connections, dump=True)
c = CircuitOpt([p, p], connections)
c.opt_neurons(f)
def analyse(dir):
from odynn import optim
import pandas as pd
import seaborn as sns
import pylab as plt
dir = utils.set_dir(dir)
dic = optim.get_vars(dir, loss=True)
train, test = optim.get_data(dir)
df = pd.DataFrame.from_dict({'loss': dic['loss']}) # .head(4)
df = df.sort_values('loss').reset_index(drop=True)
# df = df.dropna()
sns.barplot(x=df.index, y='loss', data=df)
plt.show()
dic = optim.get_best_result(dir)
ctf = optim.get_model(dir)
ctf.plot(save=True)
ctf.init_params = dic
sim.simul(t=train[0], i_injs=train[1], circuit=ctf, show=True, save=True)
sim.simul(t=test[0], i_injs=test[1], circuit=ctf, show=True, save=True)
def with_LSTM():
dir = utils.set_dir('withLSTM')
conns = {(0, 1): circuit.SYNAPSE}
conns_opt = {(0, 1): circuit.get_syn_rand(True)}
dt = 0.5
t, i = datas.give_train(dt=dt)
i_1 = np.zeros(i.shape)
i_injs = np.stack([i, i_1], axis=2)
train = sim.simul(t, i_injs, [p, p], conns, n_out=[0, 1], show=False)
neurons = nr.Neurons([nr.BioNeuronTf(PyBioNeuron.get_random(), fixed=[], dt=dt), nr.BioNeuronTf(p, fixed='all', dt=dt)])
c = CircuitTf(neurons=neurons, synapses=conns_opt)
co = CircuitOpt(circuit=c)
co.optimize(dir, train=train, n_out=[0, 1], l_rate=(0.01, 9, 0.95))
def test_opt_circuits(self):
print('one target'.center(40, '#'))
n_out = [1]
train = csim.simul(t=t,
i_injs=i_injs,
pars=pars,
synapses=conns,
n_out=n_out,
show=False)
co = CircuitOpt(
cr.CircuitTf(nr.BioNeuronTf(pars, dt=dt), synapses=conns_opt))
co.optimize(dir, n_out=n_out, train=train, epochs=1, plot=plot)
optim.get_best_result(dir)
self.assertEqual(co._loss.shape, ())
print('one target parallel'.center(40, '#'))
co = CircuitOpt(
cr.CircuitTf(nr.BioNeuronTf(n_rand=n_neuron, dt=dt),
synapses=conns_opt_parallel))
co.optimize(dir,
n_out=n_out,
train=train,
test=train,
epochs=1,
plot=plot,
evol_var=evol_var)
self.assertEqual(co._loss.shape, (10, ))
print('several targets'.center(40, '#'))
n_out = [0, 1]
train = csim.simul(t=t,
i_injs=i_injs,
pars=pars,
synapses=conns,
n_out=n_out,
show=False)
co = CircuitOpt(
cr.CircuitTf(nr.BioNeuronTf(pars, dt=dt), synapses=conns_opt))
co.optimize(dir,
n_out=n_out,
train=train,
epochs=1,
w_n=(1, 2),
plot=plot,
evol_var=evol_var)
self.assertEqual(co._loss.shape, ())
print('several targets parallel'.center(40, '#'))
co = CircuitOpt(
cr.CircuitTf.create_random(
n_neuron=2, syn_keys={k: False
for k in conns.keys()}, dt=dt))
co.optimize(dir,
n_out=n_out,
train=train,
epochs=1,
w_n=(1., 0.2),
plot=plot,
evol_var=evol_var)
self.assertEqual(co._loss.shape, (10, ))
print('1 LSTM'.center(40, '#'))
neurons = nr.Neurons(
[nr.NeuronLSTM(dt=dt),
nr.BioNeuronTf(p, fixed='all', dt=dt)])
c = cr.CircuitTf(neurons=neurons, synapses=conns_opt)
co = CircuitOpt(circuit=c)
co.optimize(dir,
train=train,
n_out=[0, 1],
l_rate=(0.01, 9, 0.95),
epochs=1,
plot=plot,
evol_var=evol_var)
print('2 LSTM'.center(40, '#'))
neurons = nr.Neurons([nr.NeuronLSTM(dt=dt), nr.NeuronLSTM(dt=dt)])
c = cr.CircuitTf(neurons=neurons, synapses=conns_opt)
co = CircuitOpt(circuit=c)
co.optimize(dir,
train=train,
n_out=[0, 1],
l_rate=(0.01, 9, 0.95),
epochs=1,
plot=plot,
evol_var=evol_var)
conns_opt[(0, 2)] = odynn.circuit.get_syn_rand()
with self.assertRaises(AttributeError):
c = cr.CircuitTf(neurons=neurons, synapses=conns_opt)
co = CircuitOpt(circuit=c)
co.optimize(dir,
train=train,
n_out=[0, 1],
l_rate=(0.01, 9, 0.95),
epochs=1,
plot=plot,
evol_var=evol_var)
print('2 bio + 1 LSTM'.center(40, '#'))
neurons = nr.Neurons([
nr.BioNeuronTf(init_p=[p for _ in range(2)], dt=dt),
nr.NeuronLSTM(dt=dt)
])
c = cr.CircuitTf(neurons=neurons, synapses=conns_opt)
co = CircuitOpt(circuit=c)
train[1] = i_injs3
co.optimize(dir,
train=train,
n_out=[0, 1],
l_rate=(0.01, 9, 0.95),
epochs=1,
plot=plot,
evol_var=evol_var)
