def test_calculate(self):
n = BioNeuronTf(init_p=p)
nn = BioNeuronTf(init_p=[p for _ in range(8)])
i = np.array([2., 3., 0.])
ii = np.array([[2., 2.], [3., 3.], [0., 0.]])
x = n.calculate(i)
self.assertEqual(n._init_state.shape[0], x.shape[1])
self.assertEqual(x.shape[0], len(i))
x = n.calculate(ii)
self.assertEqual(ii.shape[1], x.shape[2])
self.assertEqual(x.shape[0], ii.shape[0]) # same time
self.assertEqual(x.shape[1], n._init_state.shape[0])
self.assertEqual(x.shape[2], ii.shape[1]) # same nb of batch
x = nn.calculate(i) #several neurons, one batch
self.assertEqual(x.shape[-1], nn.num)
self.assertEqual(x.shape[0], len(i))
self.assertEqual(x.shape[1], nn._init_state.shape[0])
xx2 = nn.calculate(ii) #several neurons, several batches
xx = nn.calculate(
np.stack([ii for _ in range(8)],
axis=ii.ndim)) # several neurons, several batches
self.assertEqual(xx.shape[-1], nn.num)
self.assertEqual(xx.shape[0], ii.shape[0]) # same time
self.assertEqual(xx.shape[1], nn._init_state.shape[0])
self.assertEqual(xx.shape[2], ii.shape[1]) # same nb of batch
self.assertEqual(xx.all(), xx2.all())
def test_build_graph(self):
n = BioNeuronTf(init_p=p)
nn = BioNeuronTf(init_p=[p for _ in range(8)])
i, res = n.build_graph()
self.assertEqual(i.get_shape().as_list(), [None])
i, res = n.build_graph(3)
self.assertEqual(i.get_shape().as_list(), [None, None])
i, res = n.build_graph(batch=1)
self.assertEqual(i.get_shape().as_list(), [None, None])
i, res = nn.build_graph()
self.assertEqual(i.get_shape().as_list(), [None, 8])
i, res = nn.build_graph(1)
self.assertEqual(i.get_shape().as_list(), [None, None, 8])
i, res = nn.build_graph(batch=4)
self.assertEqual(i.get_shape().as_list(), [None, None, 8])
def test_init(self):
hh = BioNeuronTf(init_p=[p for _ in range(10)])
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 10)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (10, ))
self.assertEqual(hh.init_params.keys(), p.keys())
self.assertEqual(hh.parameter_names, list(hh.default_params.keys()))
hh = BioNeuronTf(
init_p={var: [val for _ in range(10)]
for var, val in p.items()})
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 10)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (10, ))
self.assertEqual(hh.init_params.keys(), p.keys())
self.assertEqual(hh.parameter_names, list(hh.default_params.keys()))
hh = BioNeuronTf(init_p=[PyBioNeuron.get_random() for _ in range(13)])
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 13)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (13, ))
self.assertEqual(hh.init_params.keys(), p.keys())
self.assertEqual(hh.parameter_names, list(hh.default_params.keys()))
hh = BioNeuronTf(p)
self.assertEqual(hh.num, 1)
self.assertEqual(hh._init_state.shape, (len(hh.default_init_state), ))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(hh.init_params, p)
self.assertEqual(hh.parameter_names, list(hh.default_params.keys()))
hh = BioNeuronTf(n_rand=15)
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 15)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (15, ))
self.assertEqual(hh.parameter_names, list(hh.default_params.keys()))
def test_parallelize(self):
n = BioNeuronTf(init_p=p)
sh = list(n._init_state.shape)
n.parallelize(10)
sh.append(10)
shp = (10, )
self.assertEqual(n._init_state.shape, tuple(sh))
self.assertEqual(list(n.init_params.values())[0].shape, tuple(shp))
n = BioNeuronTf(init_p=[p for _ in range(8)])
sh = list(n._init_state.shape)
shp = list(list(n.init_params.values())[0].shape)
n.parallelize(11)
sh.append(11)
shp.append(11)
self.assertEqual(n._init_state.shape, tuple(sh))
self.assertEqual(list(n.init_params.values())[0].shape, tuple(shp))
def test_optimize(self):
print('LSTM'.center(40, '#'))
n = NeuronLSTM(dt=dt)
opt = NeuronOpt(neuron=n)
self.assertEqual(opt._parallel, 1)
w = [1 for _ in range(len(train[-1]))]
n = opt.optimize(dir, w=w, train=train, epochs=1, plot=plot)
print('LSTM, calcium None'.center(40, '#'))
train2 = train
train2[-1][-1] = None
n = opt.optimize(dir, w=w, train=train, epochs=1, plot=plot)
optim.get_model(dir)
optim.get_vars_all(dir)
t, tt = optim.get_data(dir)
self.assertEqual(t[0].all(), train[0].all())
self.assertEqual(t[1].all(), train[1].all())
for i, tt in enumerate(t[-1]):
if tt is not None:
self.assertEqual(tt.all(), train[-1][i].all())
print('One neuron'.center(40, '#'))
opt = NeuronOpt(nr)
self.assertEqual(opt._parallel, 1)
n = opt.optimize(dir, w=w, train=train, epochs=1, plot=plot)
print('One neuron reload'.center(40, '#'))
n = opt.optimize(dir,
w=w,
reload=True,
train=train,
epochs=1,
plot=plot)
print('One neuron with test'.center(40, '#'))
n = opt.optimize(dir,
w=w,
train=train,
test=train,
epochs=1,
plot=plot)
print('Parallel'.center(40, '#'))
pars = [PyBioNeuron.get_random() for _ in range(2)]
opt = NeuronOpt(BioNeuronTf(init_p=pars, dt=dt))
self.assertEqual(opt._parallel, 2)
n = opt.optimize(dir, w=w, train=train, epochs=1, plot=plot)
self.assertEqual(opt._loss.shape[0], opt._parallel)
def test_pickle(self):
hh1 = BioNeuronTf(init_p=[p for _ in range(10)])
with open(self.dir + 'yeee', 'wb') as f:
pickle.dump(hh1, f)
with open(self.dir + 'yeee', 'rb') as f:
hh = pickle.load(f)
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 10)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (10, ))
self.assertEqual(hh.init_params.keys(), p.keys())
self.assertEqual(hh1.parameter_names, hh.parameter_names)
hh = BioNeuronTf(
init_p={var: [val for _ in range(10)]
for var, val in p.items()})
with open(self.dir + 'yeee', 'wb') as f:
pickle.dump(hh, f)
with open(self.dir + 'yeee', 'rb') as f:
hh = pickle.load(f)
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 10)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (10, ))
self.assertEqual(hh.init_params.keys(), p.keys())
hh = BioNeuronTf(init_p=[PyBioNeuron.get_random() for _ in range(13)])
with open(self.dir + 'yeee', 'wb') as f:
pickle.dump(hh, f)
with open(self.dir + 'yeee', 'rb') as f:
hh = pickle.load(f)
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 13)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (13, ))
self.assertEqual(hh.init_params.keys(), p.keys())
hh = BioNeuronTf(n_rand=15)
with open(self.dir + 'yeee', 'wb') as f:
pickle.dump(hh, f)
with open(self.dir + 'yeee', 'rb') as f:
hh = pickle.load(f)
self.assertEqual(len(hh._init_state), len(hh.default_init_state))
self.assertEqual(hh.num, 15)
self.assertEqual(hh._init_state.shape,
(len(hh.default_init_state), hh.num))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(list(hh.init_params.values())[0].shape, (15, ))
hh = BioNeuronTf(p)
with open(self.dir + 'yeee', 'wb') as f:
pickle.dump(hh, f)
with open(self.dir + 'yeee', 'rb') as f:
hh = pickle.load(f)
self.assertEqual(hh.num, 1)
self.assertEqual(hh._init_state.shape, (len(hh.default_init_state), ))
self.assertIsInstance(hh.init_params, dict)
self.assertEqual(hh.init_params, p)
def test_init_groups(self):
hh = BioNeuronTf(n_rand=15, groups=np.zeros(15, dtype=np.int32))
hh.reset()
sess = tf.Session()
sess.run(tf.global_variables_initializer())
pars = sess.run(hh._param['C_m'])
for i in range(1, 15):
self.assertEqual(pars[i - 1], pars[i])
hh = BioNeuronTf(n_rand=6, groups=[1, 0, 0, 0, 2, 2])
hh = BioNeuronTf(n_rand=3, groups=[1, 0, 0, 0, 2, 2])
self.assertEqual(hh.parameter_names, list(hh.default_params.keys()))
hh.reset()
loss = tf.square(hh._param['C_m'] - [1., 20., 80., -10., -7., 90.])
train = tf.train.AdamOptimizer(0.1).minimize(loss)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
pars = sess.run(hh._param['C_m'])
self.assertEqual(pars[1], pars[2])
self.assertEqual(pars[1], pars[3])
self.assertEqual(pars[4], pars[5])
sess.run(train)
pars = sess.run(hh._param['C_m'])
self.assertEqual(pars[1], pars[2])
self.assertEqual(pars[1], pars[3])
self.assertEqual(pars[4], pars[5])
with self.assertRaises(ValueError):
hh = BioNeuronTf(n_rand=2, groups=[1, 0, 0, 0, 2, 2])
from odynn import utils, datas
from odynn.neuron import NeuronLSTM, BioNeuronTf, PyBioNeuron
from odynn.noptim import NeuronOpt
from odynn.nsimul import simul
from odynn import optim
import tensorflow as tf
import numpy as np
dir = utils.set_dir('unittest')
dt = 0.5
t, i = datas.give_train(dt=dt, max_t=5.)
default = PyBioNeuron.default_params
pars = PyBioNeuron.get_random()
train = simul(p=default, dt=dt, i_inj=i, show=False, suffix='train')
plot = False
nr = BioNeuronTf(init_p=pars, dt=dt)
class TestNeuronOpt(TestCase):
def test_init(self):
with self.assertRaises(TypeError):
no = NeuronOpt(5)
def test_loss(self):
co = NeuronOpt(nr)
res = tf.zeros((len(t), len(PyBioNeuron.default_init_state), 3))
ys_ = [
tf.placeholder(shape=(len(t), 3), dtype=tf.float32, name="test")
] + [
tf.placeholder(shape=(len(t), 3), dtype=tf.float32, name="test")
for _ in PyBioNeuron.ions.items()