adam_params = NDN.NDN.optimizer_defaults(opt_params={'use_gpu': True},
learning_alg='adam')
lbfgs_params = NDN.NDN.optimizer_defaults(opt_params={
'use_gpu': True,
'display': True
},
learning_alg='lbfgs')
lbfgs_params['maxiter'] = 1000
# setup training indices
NT = Xstim.shape[0]
valdata = np.arange(0, NT, 1)
NC = 1
Ui, Xi = NDNutils.generate_xv_folds(NT, num_blocks=2)
# GLM
# NDN parameters for processing the stimulus
par = NDNutils.ffnetwork_params(input_dims=[1, NX, NY, num_lags],
layer_sizes=[NC],
layer_types=['normal'],
normalization=[0],
act_funcs=['softplus'],
verbose=True,
reg_list={
'd2x': [0.01],
'glocal': [0.1]
})
def get_stim_model(Stim,
Robs,
dims,
valid=None,
num_lags=10,
plot=True,
XTreg=0.05,
L1reg=5e-3,
MIreg=0.1,
MSCreg=10.0,
Greg=0.1,
Mreg=1e-4,
num_subs=36,
num_hid=24,
num_tkern=None,
Cindx=None,
base_mod=None,
cids=None,
autoencoder=False):
NX = dims[0]
NY = dims[1]
NT, NC = Robs.shape
if valid is None:
valid = np.arange(0, NT, 1)
# create time-embedded stimulus
Xstim, rinds = create_time_embedding_valid(Stim, [num_lags, NX, NY], valid)
Rvalid = deepcopy(Robs[rinds, :])
NTv = Rvalid.shape[0]
print('%d valid samples of %d possible' % (NTv, NT))
stas = Xstim.T @ (Rvalid - np.average(Rvalid, axis=0))
stas = np.reshape(stas, [NX * NY, num_lags, NC]) / NTv
if plot:
plt.figure(figsize=(10, 15))
sx, sy = U.get_subplot_dims(NC)
mu = np.zeros(NC)
for cc in range(NC):
if plot:
plt.subplot(sx, sy, cc + 1)
plt.plot(np.abs(stas[:, :, cc]).T, color=[.5, .5, .5])
tlevel = np.median(
np.abs(stas[:, :, cc] - np.average(stas[:, :, cc]))) * 4
mu[cc] = np.average(np.abs(stas[:, :, cc]) > tlevel)
if plot:
plt.axhline(tlevel, color='k')
plt.title(cc)
# threshold good STAS
thresh = 0.01
if plot:
plt.figure()
plt.plot(mu, '-o')
plt.axhline(thresh, color='k')
plt.show()
if cids is None:
cids = np.where(mu > thresh)[0] # units to analyze
print("found %d good STAs" % len(cids))
if plot:
plt.figure(figsize=(10, 15))
for cc in cids:
plt.subplot(sx, sy, cc + 1)
bestlag = np.argmax(np.max(abs(stas[:, :, cc]), axis=0))
plt.imshow(np.reshape(stas[:, bestlag, cc], (NY, NX)))
plt.title(cc)
# index into "good" units
Rvalid = Rvalid[:, cids]
NC = Rvalid.shape[1]
stas = stas[:, :, cids]
if Cindx is None:
print("Getting Crop Index")
# Crop stimulus to center around RFs
sumdensity = np.zeros([NX * NY])
for cc in range(NC):
bestlag = np.argmax(np.max(abs(stas[:, :, cc]), axis=0))
sumdensity += stas[:, bestlag, cc]**2
if plot:
plt.figure()
plt.imshow(np.reshape(sumdensity, [NY, NX]))
plt.title("Sum Density STA")
# get Crop indices (TODO: debug)
sumdensity = (sumdensity - np.min(sumdensity)) / (np.max(sumdensity) -
np.min(sumdensity))
I = np.reshape(sumdensity, [NY, NX]) > .3
xinds = np.where(np.sum(I, axis=0) > 0)[0]
yinds = np.where(np.sum(I, axis=1) > 0)[0]
NX2 = np.maximum(len(xinds), len(yinds))
x0 = np.min(xinds)
y0 = np.min(yinds)
xinds = range(x0, x0 + NX2)
yinds = range(y0, y0 + NX2)
Cindx = crop_indx(NX, xinds, yinds)
if plot:
plt.figure()
plt.imshow(np.reshape(sumdensity[Cindx], [NX2, NX2]))
plt.title('Cropped')
plt.show()
NX2 = np.sqrt(len(Cindx)).astype(int)
# make new cropped stimulus
Xstim, rinds = create_time_embedding_valid(Stim[:, Cindx],
[num_lags, NX2, NX2], valid)
# index into Robs
Rvalid = deepcopy(Robs[rinds, :])
Rvalid = Rvalid[:, cids]
Rvalid = NDNutils.shift_mat_zpad(Rvalid, -1, dim=0) # get rid of first lag
NC = Rvalid.shape[1] # new number of units
NT = Rvalid.shape[0]
print('%d valid samples of %d possible' % (NT, Stim.shape[0]))
print('%d good units' % NC)
# double-check STAS work with cropped stimulus
stas = Xstim.T @ Rvalid
stas = np.reshape(stas, [NX2 * NX2, num_lags, NC]) / NT
if plot:
plt.figure(figsize=(10, 15))
for cc in range(NC):
plt.subplot(sx, sy, cc + 1)
bestlag = np.argmax(np.max(abs(stas[:, :, cc]), axis=0))
plt.imshow(np.reshape(stas[:, bestlag, cc], (NX2, NX2)))
plt.title(cc)
plt.show()
Ui, Xi = NDNutils.generate_xv_folds(NT)
# fit SCAFFOLD MODEL
try:
if len(XTreg) == 2:
d2t = XTreg[0]
d2x = XTreg[1]
else:
d2t = XTreg[0]
d2x = deepcopy(d2t)
except TypeError:
d2t = deepcopy(XTreg)
d2x = deepcopy(XTreg)
# optimizer parameters
adam_params = U.def_adam_params()
if not base_mod is None:
side2b = base_mod.copy_model()
side2b.set_regularization('d2t', d2t, layer_target=0)
side2b.set_regularization('d2x', d2x, layer_target=0)
side2b.set_regularization('glocal', Greg, layer_target=0)
side2b.set_regularization('l1', L1reg, layer_target=0)
side2b.set_regularization('max', MIreg, ffnet_target=0, layer_target=1)
side2b.set_regularization('max',
MSCreg,
ffnet_target=1,
layer_target=0)
if len(side2b.networks) == 4: # includes autoencoder network
input_data = [Xstim, Rvalid]
else:
input_data = Xstim
else:
# Best regularization arrived at
Greg0 = 1e-1
Mreg0 = 1e-6
L1reg0 = 1e-5
if not num_tkern is None:
ndn_par = NDNutils.ffnetwork_params(
input_dims=[1, NX2, NX2, num_lags],
layer_sizes=[num_tkern, num_subs, num_hid],
layer_types=['conv', 'normal', 'normal'],
ei_layers=[None, num_subs // 2, num_hid // 2],
conv_filter_widths=[1],
normalization=[1, 1, 1],
act_funcs=['lin', 'relu', 'relu'],
verbose=True,
reg_list={
'd2t': [1e-3],
'd2x': [None, XTreg],
'l1': [L1reg0, L1reg0],
'glocal': [Greg0, Greg0]
})
else:
ndn_par = NDNutils.ffnetwork_params(
input_dims=[1, NX2, NX2, num_lags],
layer_sizes=[num_subs, num_hid],
layer_types=['normal', 'normal'],
ei_layers=[num_subs // 2, num_hid // 2],
normalization=[1, 1],
act_funcs=['relu', 'relu'],
verbose=True,
reg_list={
'd2t': [d2t],
'd2x': [d2x],
'l1': [L1reg0, L1reg0],
'glocal': [Greg0]
})
side_par = NDNutils.ffnetwork_params(network_type='side',
xstim_n=None,
ffnet_n=0,
layer_sizes=[NC],
layer_types=['normal'],
normalization=[-1],
act_funcs=['softplus'],
verbose=True,
reg_list={'max': [Mreg0]})
side_par[
'pos_constraints'] = True # ensures Exc and Inh mean something
if autoencoder: # capturea additional variability using autoencoder
auto_par = NDNutils.ffnetwork_params(
input_dims=[1, NC, 1],
xstim_n=[1],
layer_sizes=[2, 1, NC],
time_expand=[0, 15, 0],
layer_types=['normal', 'temporal', 'normal'],
conv_filter_widths=[None, 1, None],
act_funcs=['relu', 'lin', 'lin'],
normalization=[1, 1, 0],
reg_list={'d2t': [None, 1e-1, None]})
add_par = NDNutils.ffnetwork_params(xstim_n=None,
ffnet_n=[1, 2],
layer_sizes=[NC],
layer_types=['add'],
act_funcs=['softplus'])
side2 = NDN.NDN([ndn_par, side_par, auto_par, add_par],
ffnet_out=1,
noise_dist='poisson')
# set output regularization on the latent
side2.batch_size = adam_params['batch_size']
side2.initialize_output_reg(network_target=2,
layer_target=1,
reg_vals={'d2t': 1e-1})
input_data = [Xstim, Rvalid]
else:
side2 = NDN.NDN([ndn_par, side_par],
ffnet_out=1,
noise_dist='poisson')
input_data = Xstim
_ = side2.train(input_data=input_data,
output_data=Rvalid,
train_indxs=Ui,
test_indxs=Xi,
silent=False,
learning_alg='adam',
opt_params=adam_params)
side2.set_regularization('glocal', Greg, layer_target=0)
side2.set_regularization('l1', L1reg, layer_target=0)
side2.set_regularization('max', MIreg, ffnet_target=0, layer_target=1)
side2.set_regularization('max', MSCreg, ffnet_target=1, layer_target=0)
side2b = side2.copy_model()
_ = side2b.train(input_data=input_data,
output_data=Rvalid,
train_indxs=Ui,
test_indxs=Xi,
silent=False,
learning_alg='adam',
opt_params=adam_params)
LLs2n = side2b.eval_models(input_data=input_data,
output_data=Rvalid,
data_indxs=Xi,
nulladjusted=True)
print(np.mean(LLs2n))
if plot:
plt.hist(LLs2n)
plt.xlabel('Nats/Spike')
plt.show()
return side2b, Xstim, Rvalid, rinds, cids, Cindx
plt.subplot(sx,sy,cc+1)
bestlag = np.argmax(np.max(abs(stas[:,:,cc]),axis=0))
sta = stas[:,bestlag,cc]
plt.imshow(np.reshape(sta[Cxinds], [NX2,NX2]))
# %% prepare stimulus for model
Xstim, Rvalid, dims, CXinds, cids = ne.prep_stim_model(Stim, Robs, [NX,NY],
valid=valid_inds,
num_lags=10,
plot=True,
Cindx=Cxinds,
)
#%% fit GQM
NT = Xstim.shape[0]
Ui, Xi = NDNutils.generate_xv_folds(NT)
# optimizer parameters
adam_params = U.def_adam_params()
# d2ts = 1e-4*10**np.arange(0, 5)
d2xs = 1e-2*10**np.arange(0, 5)
gqms = []
LLxs = []
for step in range(len(d2xs)):
d2t = .05
d2x = d2xs[step]
loc = 1e-5
stas = Xstim.T@(Rvalid - np.mean(Rvalid))
stas = np.reshape(stas, [NX2*NX2, num_lags, NC])/NT
plt.figure(figsize=(10,15))
for cc in range(NC):
plt.subplot(sx,sy,cc+1)
bestlag = np.argmax(np.max(abs(stas[:,:,cc]),axis=0))
# plt.plot(stas[:,:,cc].T)
plt.imshow(np.reshape(stas[:,bestlag,cc], (NX2,NX2)))
plt.title(cc)
plt.show()
#%%
Rvalid = deepcopy(Robs[:,cids])
# train, test indices
Ui, Xi = NDNutils.generate_xv_folds(NT)
# fit SCAFFOLD MODEL
# Best regularization arrived at
Greg0 = 1e-1
Mreg0 = 1e-6
L1reg0 = 1e-5
XTreg=0.05
L1reg=5e-3
MIreg=0.1
MSCreg=10.0
Greg=0.1
Mreg=1e-4