def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(
math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[9, int(hidden * output_shape),
output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None],
normalization=[0, 0, 0],
layer_types=['diff_of_gaussians', 'normal', 'normal'],
act_funcs=['lin', 'softplus', 'softplus'],
reg_list={
'l2': [None, reg_h, reg_l],
})
hsm_params['weights_initializers'] = ['random', 'normal', 'normal']
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(
math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[c_filters, c_filters, c_filters,
output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None, None],
normalization=[0, 0, 0, 0],
layer_types=['conv', 'conv', 'conv', 'sep'],
act_funcs=['softplus', 'softplus', 'softplus', 'softplus'],
shift_spacing=[2, 2, 2, 0],
conv_filter_widths=[13, 3, 3, 0],
reg_list={
'd2x': [cd2x, cd2x, cd2x, None],
'max_filt': [max_filt, max_filt, max_filt, None],
'l1': [None, None, None, l1]
})
hsm_params['weights_initializers'] = [
'normal', 'normal', 'normal', 'normal'
]
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[c_filters, int(0.2*output_shape), output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None],
normalization=[0, 0, 0],
layer_types=['conv_diff_of_gaussians', hidden_lt, 'normal'],
act_funcs=['lin', 'softplus','softplus'],
shift_spacing=[(c_size+1)//2, 0],
conv_filter_widths=[c_size, 0, 0],
reg_list={
hidden_t:[None, hidden_s, None],
'l2':[None, None, 0.1],
})
hsm_params['weights_initializers']=['random','normal','normal']
hsm_params['biases_initializers']=['trunc_normal','trunc_normal','trunc_normal']
return hsm_params
def get_params(self):
hsm_params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[
int(self.args['hidden']),
int(self.args['hidden']),
int(0.2 * self.out_num), self.out_num
], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None, None],
normalization=[0, 0, 0, 0],
layer_types=[
'var', 'conv_diff_of_gaussians', self.args['layer'], 'normal'
],
act_funcs=['lin', 'lin', 'softplus', 'softplus'],
shift_spacing=[1, (self.args['c_size'] + 1) // 2, 1, 1],
conv_filter_widths=[
self.args['c_size'], self.args['c_size'], 0, 0
],
reg_list={
'l2': [0.1, None, None, 0.1],
'l1': [None, None, self.args['reg_h'], None],
})
hsm_params['weights_initializers'] = [
'normal', 'random', 'normal', 'normal'
]
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_params(self):
params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[
self.args['channels'], self.args['channels'],
int(0.2 * self.out_num), self.out_num
], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None, None],
normalization=[0, 0, 0, 0],
layer_types=['var', 'conv', self.args['hidden_lt'], 'normal'],
act_funcs=['lin', 'softplus', 'softplus', 'softplus'],
shift_spacing=[1, (self.args['c_size'] + 1) // 2, 1, 1],
conv_filter_widths=[0, self.args['c_size'], 0, 0],
reg_list={
'd2x': [None, self.args['cd2x'], None, None],
self.args['hidden_t']:
[None, None, self.args['hidden_s'], None],
'l2': [0.1, None, None, 0.1],
})
params['weights_initializers'] = [
'normal', 'normal', 'normal', 'normal'
]
params['biases_initializers'] = [
'normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return params
def get_encoder(self,
noise_size,
input_shape,
ffnet_in,
generator_type='conv'):
if generator_type == 'conv':
params = NDNutils.ffnetwork_params(
input_dims=input_shape,
layer_sizes=[8, 8, 16, noise_size],
layer_types=['conv', 'conv', 'conv', 'normal'],
act_funcs=['relu', 'relu', 'relu', 'lin'],
conv_filter_widths=[5, 5, 7, None],
shift_spacing=[1, 2, 2, None],
reg_list={'d2x': [0.1, 0.1, None, None]},
verbose=False)
elif generator_type == 'lin':
params = NDNutils.ffnetwork_params(
input_dims=input_shape,
layer_sizes=[8, 8, 16, noise_size],
layer_types=['normal', 'normal', 'normal'],
act_funcs=[
'relu',
'relu',
'relu',
],
reg_list={'d2x': [0.1, 0.1, None, None]},
verbose=False)
elif generator_type == 'hybrid':
params = NDNutils.ffnetwork_params(
input_dims=input_shape,
layer_sizes=[8, 8, 16, noise_size],
layer_types=['conv', 'conv', 'normal', 'normal'],
act_funcs=['relu', 'relu', 'relu', 'lin'],
conv_filter_widths=[5, 5, 7, None],
shift_spacing=[2, 2, None, None],
reg_list={'d2x': [0.1, 0.1, None, None]},
verbose=False)
else:
raise ValueError(
f'Generator type \'{generator_type}\' not implemented.')
params['xstim_n'] = None
params['ffnet_n'] = [ffnet_in]
return params
def get_params(self):
hsm_params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[
int(self.args['hidden'] * self.out_num),
int(self.args['hidden'] * self.out_num), self.out_num
], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None],
normalization=[0, 0, 0],
layer_types=['var', 'normal', 'normal'],
act_funcs=['lin', 'softplus', 'softplus'],
reg_list={
'l2': [0.1, None, self.args['reg_l']],
'd2x': [None, self.args['reg_h'], None],
})
hsm_params['weights_initializers'] = ['normal', 'normal', 'normal']
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_hsm_params_custom(input, output, i):
_, output_shape = output.shape
_, input_shape = input.shape
pprint(f"in: {input_shape} out: {output_shape}")
intput_w, input_h = int(math.sqrt(input_shape)), int(math.sqrt(input_shape))
hsm_params = NDNutils.ffnetwork_params(
verbose=False,
input_dims=[1, intput_w, input_h],
layer_sizes=[output_shape], # paper: 9, 0.2*output_shape
ei_layers=[None],
normalization=[0],
layer_types=['normal'],
act_funcs=['softplus'],
reg_list={
'd2x':[reg_l],
})
hsm_params['weights_initializers']=['normal']
hsm_params['biases_initializers']=['trunc_normal']
return hsm_params
def get_params(self):
hsm_params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[
self.args['filt_size'], self.args['filt_size'],
int(self.args['perc_output'] * self.out_num), self.out_num
], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None, None],
normalization=[0, 0, 0, 0],
layer_types=['var', 'diff_of_gaussians', 'normal', 'normal'],
act_funcs=['lin', 'lin', 'softplus', 'softplus'],
reg_list={
'l2': [0.1, None, None, 0.1],
})
hsm_params['weights_initializers'] = [
'normal', 'random', 'normal', 'normal'
]
hsm_params['biases_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return hsm_params
def get_params(self):
params = NDNutils.ffnetwork_params(
input_dims=[1, self.width, self.height],
layer_sizes=[
self.args['channels'], self.args['channels'],
self.args['channels'], self.out_num
],
layer_types=['conv', 'conv', 'conv', 'sep'],
act_funcs=['softplus', 'softplus', 'lin', 'softplus'],
shift_spacing=[1, 1, 1, None],
reg_list={
#'d2x': [0.03, 0.015, 0.015, None],
'l1': [None, None, None, 0.02]
})
params['conv_filter_widths'] = [13, 5, 5, None]
params['weights_initializers'] = [
'trunc_normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
params['bias_initializers'] = [
'zeros', 'zeros', 'zeros', 'trunc_normal'
]
params['pos_constraint'] = [False, False, False, True]
return params
def get_params(self):
params = NDNutils.ffnetwork_params(
input_dims=[10],
layer_sizes=[[31, 31], 30,
int(0.2 * self.out_num),
self.out_num], # paper: 9, 0.2*output_shape
ei_layers=[None, None, None, None],
normalization=[0, 0, 0, 0],
layer_types=['normal', 'conv', 'conv', 'normal'],
act_funcs=['lin', 'softplus', 'softplus', 'softplus'],
shift_spacing=[1, (7 + 1) // 2, 1, 1],
conv_filter_widths=[0, 7, 0, 0],
reg_list={
'd2x': [None, 0.2, None, None],
'l2': [0.1, None, None, 0.1],
})
params['weights_initializers'] = [
'normal', 'normal', 'normal', 'normal'
]
params['biases_initializers'] = [
'normal', 'trunc_normal', 'trunc_normal', 'trunc_normal'
]
return params
},
learning_alg='lbfgs')
lbfgs_params['maxiter'] = 1000
# setup training indices
valdata = np.arange(0, NT, 1)
Ui, Xi = NDNutils.generate_xv_folds(NT, num_blocks=2)
# NDN parameters for processing the stimulus
par = NDNutils.ffnetwork_params(input_dims=[2, NX, NY, num_lags],
layer_sizes=[NC],
layer_types=['normal'],
normalization=[0],
act_funcs=['softplus'],
verbose=True,
reg_list={
'd2t': [.01],
'd2x': [0.01],
'glocal': [0.01]
})
# initialize GLM
glm0 = NDN.NDN([par], noise_dist='poisson')
v2f0 = glm0.fit_variables(fit_biases=False)
v2f0[-1][-1]['biases'] = True
R = Robs[:, cids].astype('float32')
# train initial model
_ = glm0.train(input_data=[Xstim],
plt.figure()
# f = plt.hist2d( eyeX[Ui], eyeY[Ui], bins=100)
sns.kdeplot(eyeX[Ui], eyeY[Ui], bw=.05, shade=True, shade_lowest=False)
plt.title('Eye Position Density')
rs = np.hypot(eyeX, eyeY)
Ui = np.intersect1d(Ui, np.where(np.logical_and(eyeX < 0, eyeY < 0))[0])
num_subs = NC // 2
nim_par = NDNutils.ffnetwork_params(input_dims=[1, NX, NY, num_lags],
layer_sizes=[num_subs, NC],
layer_types=['normal', 'normal'],
act_funcs=['relu', 'softplus'],
normalization=[0],
reg_list={
'l2': 1e-2,
'd2xt': 1e-5,
'glocal': 1e-1
})
nim0 = NDN.NDN([nim_par], tf_seed=seed, noise_dist=noise_dist)
v2f = nim0.fit_variables(fit_biases=True)
# train
_ = nim0.train(input_data=[Xstim],
output_data=Robs,
train_indxs=Ui,
test_indxs=Xi,
learning_alg=optimizer,
d2xs = 1e-2*10**np.arange(0, 5)
gqms = []
LLxs = []
for step in range(len(d2xs)):
d2t = .05
d2x = d2xs[step]
loc = 1e-5
num_lags = dims[0]
NX2 = dims[1]
NC = Rvalid.shape[1]
# NDN parameters for processing the stimulus
lin = NDNutils.ffnetwork_params(
input_dims=[1,NX2,NX2,num_lags],
layer_sizes=[NC],
layer_types=['readout'], normalization=[0],
act_funcs=['lin'], verbose=True,
reg_list={'d2t': [d2t], 'd2x':[d2x], 'l1':[loc]})
quad = NDNutils.ffnetwork_params(
input_dims=[1,NX2,NX2,num_lags],
layer_sizes=[NC],
layer_types=['readout'], normalization=[0],
act_funcs=['quad'], verbose=True,
reg_list={'d2t': [d2t], 'd2x':[d2x], 'l1':[loc]})
quad = NDNutils.ffnetwork_params(
input_dims=[1,NX2,NX2,num_lags],
layer_sizes=[NC],
layer_types=['readout'], normalization=[0],
act_funcs=['quad'], verbose=True,
if optimizer == 'adam':
opt_params = adam_params
else:
opt_params = lbfgs_params
# %% fit GLM
Ui = opts['Ui']
Xi = opts['Xi']
Ti = opts['Ti']
glm_par = NDNutils.ffnetwork_params(input_dims=[1, NX, NY, num_lags],
layer_sizes=[NC],
layer_types=['readout'],
act_funcs=['lin'],
normalization=[0],
reg_list={
'l2': 1e-2,
'd2xt': 1e-5,
'l1': 1e-5
})
autoencoder = 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-5, None]})
# 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]
})
# initialize GLM
glm0 = NDN.NDN([par], noise_dist='poisson')
# initialize weights with STA
# sta = (sta - np.min(sta)) / (np.max(sta) - np.min(sta))
# glm0.networks[0].layers[0].weights[:,0]=deepcopy((sta - np.min(sta)) / (np.max(sta) - np.min(sta)))
v2f0 = glm0.fit_variables(fit_biases=True)
# train initial model
saconshift = NDNutils.shift_mat_zpad(sacon, -back_shiftson, dim=0)
num_onlags = max(tspacing) + 1
Xdir = NDNutils.create_time_embedding(gratingDir, [num_lags, ND])
Xcon = NDNutils.create_time_embedding(gratingCon, [num_lags, 1])
Ui, Xi = NDNutils.generate_xv_folds(NT)
NC = Robs.shape[1]
Time = NDNutils.tent_basis_generate(xs=np.linspace(0, NT - 1, 20))
dc_shift = NDNutils.ffnetwork_params(
input_dims=[1, Time.shape[1]],
layer_sizes=[NC],
layer_types=['normal'], # readout for cell-specific regularization
act_funcs=['lin'],
normalization=[0],
reg_list={
'd2x': [1e-3],
'l2': [1e-4]
})
dir_tuning_par = NDNutils.ffnetwork_params(
input_dims=[num_lags, ND],
xstim_n=[1],
layer_sizes=[10, NC],
layer_types=['normal',
'normal'], # readout for cell-specific regularization
act_funcs=['lin', 'lin'],
normalization=[1, 0],
reg_list={
'd2x': [1e-5],
def make_model_convolutional(base_mod, dims):
NX = dims[0]
NY = dims[1]
NC = base_mod.output_sizes[0]
# find networks that process the stimulus
stim_nets = [
nn for nn in range(len(base_mod.network_list))
if base_mod.network_list[nn]['xstim_n'] is not None
]
par = []
for ss in stim_nets:
netlist_old = deepcopy(base_mod.network_list)[ss]
# convert stimulus network params into a convolutional network
conv_par = deepcopy(base_mod.network_list[ss])
conv_par['input_dims'] = [1, NX, NY] + [conv_par['input_dims'][-1]]
conv_par['layer_types'] = ['conv']
conv_par['conv_filter_widths'] = [netlist_old['input_dims'][1]]
par.append(deepcopy(conv_par))
out_net = deepcopy(base_mod.network_list[-1])
if out_net['layer_types'][0] == 'add':
add_par = NDNutils.ffnetwork_params(
xstim_n=None,
ffnet_n=stim_nets,
layer_sizes=[NX * NY * NC],
layer_types=['add'],
act_funcs=out_net['activation_funcs'])
par.append(add_par)
elif out_net['layer_types'][0] == 'side':
out_net['layer_types'] = ['conv']
out_net['conv_filter_widths'] = [1]
par.append(out_net)
cell_shift_mod = NDN.NDN(par)
num_space = np.prod(cell_shift_mod.input_sizes[0][:-1])
# copy stim networks verbatim (only thing diff is output is a convolution)
for ff in stim_nets:
for nl in range(len(cell_shift_mod.networks[ff].layers)):
cell_shift_mod.networks[ff].layers[nl].weights = deepcopy(
base_mod.networks[ff].layers[nl].weights)
cell_shift_mod.networks[ff].layers[nl].biases = deepcopy(
base_mod.networks[ff].layers[nl].biases)
if base_mod.networks[-1].layers[0].weights.shape[0] == len(stim_nets):
cell_shift_mod.networks[-1].layers[0].weights = conv_expand(
deepcopy(base_mod.networks[-1].layers[0].weights), num_space)
cell_shift_mod.networks[-1].layers[0].biases = conv_expand(
deepcopy(base_mod.networks[-1].layers[0].biases), num_space)
else: # convolutional output instead of add layer
# copy output weights
cell_shift_mod.networks[-1].layers[0].weights = deepcopy(
base_mod.networks[1].layers[0].weights)
cell_shift_mod.networks[-1].layers[0].biases = deepcopy(
base_mod.networks[1].layers[0].biases)
return cell_shift_mod
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
def disparity_predictions(Einfo,
resp,
indxs=None,
num_dlags=8,
fr1or3=None,
spiking=True,
rectified=True,
opt_params=None):
"""Calculates a prediction of the disparity (and timing) signals that can be inferred from the response
by the disparity input alone. This puts a lower bound on how much disparity is driving the response, although
practically speaking will generate the same disparity tuning curves.
Usage: Dpred, Tpred = disparity_predictions( Einfo, resp, indxs, num_dlags=8, spiking=True, rectified=True, opt_params=None )
Inputs: Indices gives data range to fit to.
Outputs: Dpred and Tpred will be length of entire experiment -- not just indxs
"""
# Process disparity into disparty and timing design matrix
dmat = disparity_matrix(Einfo['dispt'], Einfo['corrt'])
ND2 = dmat.shape[1]
if indxs is None:
indxs = range(dmat.shape[0])
# everything but blank
Xd = NDNutils.create_time_embedding(dmat[:, :-1], [num_dlags, ND2 - 1, 1])
# blank
Xb = NDNutils.create_time_embedding(dmat[:, -1], [num_dlags, 1, 1])
# timing
switches = np.expand_dims(np.concatenate(
(np.sum(abs(np.diff(dmat, axis=0)), axis=1), [0]), axis=0),
axis=1)
Xs = NDNutils.create_time_embedding(switches, [num_dlags, 1, 1])
tpar = NDNutils.ffnetwork_params(xstim_n=[0],
input_dims=[1, 1, 1, num_dlags],
layer_sizes=[1],
verbose=False,
layer_types=['normal'],
act_funcs=['lin'],
reg_list={
'd2t': [None],
'l1': [None]
})
bpar = deepcopy(tpar)
bpar['xstim_n'] = [1]
dpar = NDNutils.ffnetwork_params(xstim_n=[2],
input_dims=[1, ND2 - 1, 1, num_dlags],
layer_sizes=[1],
verbose=False,
layer_types=['normal'],
act_funcs=['lin'],
reg_list={
'd2xt': [None],
'l1': [None]
})
if rectified:
comb_parT = NDNutils.ffnetwork_params(xstim_n=None,
ffnet_n=[0, 1],
layer_sizes=[1],
verbose=False,
layer_types=['normal'],
act_funcs=['softplus'])
else:
comb_parT = NDNutils.ffnetwork_params(xstim_n=None,
ffnet_n=[0, 1],
layer_sizes=[1],
verbose=False,
layer_types=['normal'],
act_funcs=['lin'])
comb_par = deepcopy(comb_parT)
comb_par['ffnet_n'] = [0, 1, 2]
if spiking:
nd = 'poisson'
else:
nd = 'gaussian'
Tglm = NDN.NDN([tpar, bpar, comb_parT], noise_dist=nd, tf_seed=5)
DTglm = NDN.NDN([tpar, bpar, dpar, comb_par], noise_dist=nd, tf_seed=5)
v2fT = Tglm.fit_variables(layers_to_skip=[2], fit_biases=False)
v2fT[2][0]['fit_biases'] = True
v2f = DTglm.fit_variables(layers_to_skip=[3], fit_biases=False)
v2f[3][0]['fit_biases'] = True
if (fr1or3 == 3) or (fr1or3 == 1):
mod_indxs = np.intersect1d(indxs, np.where(Einfo['frs'] == fr1or3)[0])
#frs_valid = Einfo['frs'] == fr1or3
else:
mod_indxs = indxs
#frs_valid = Einfo['frs'] > 0
#to_use = frs_valid[indxs]
#r = deepcopy(resp[mod_indxs])
#if len(resp) > len(indxs):
# r = deepcopy(resp[indxs])
#else:
# r = deepcopy(resp)
_ = Tglm.train(
input_data=[Xs[mod_indxs, :], Xb[mod_indxs, :]],
output_data=resp[mod_indxs], # fit_variables=v2fT,
learning_alg='lbfgs',
opt_params=opt_params)
_ = DTglm.train(
input_data=[Xs[mod_indxs, :], Xb[mod_indxs, :],
Xd[mod_indxs, :]], # fit_variables=v2f,
output_data=resp[mod_indxs],
learning_alg='lbfgs',
opt_params=opt_params)
#p1 = Tglm.eval_models(input_data=Xs[indxs,:], output_data=r)[0]
#p2 = DTglm.eval_models(input_data=[Xs[indxs,:], Xd[indxs,:]], output_data=r)[0]
#print( "Model performances: %0.4f -> %0.4f"%(p1, p2) )
# make predictions of each
predT = Tglm.generate_prediction(input_data=[Xs, Xb])
predD = DTglm.generate_prediction(input_data=[Xs, Xb, Xd])
return predD, predT
def get_gan_subnet(self,
input_noise_size,
output_shape,
generator_type='conv'):
output_shape = output_shape[1:]
layers = 5
if generator_type == 'conv':
params = NDNutils.ffnetwork_params(
input_dims=[1, input_noise_size],
layer_sizes=[[64, 8, 8], 32, 16, 1, 1],
layer_types=['normal', 'deconv', 'deconv', 'deconv', 'mask'],
act_funcs=['relu', 'relu', 'relu', 'tanh', 'lin'],
conv_filter_widths=[None, 5, 5, 5, None],
shift_spacing=[None, 2, 2, 1, None],
reg_list={'d2x': [None, None, 0.01, 0.01, None]},
verbose=False)
params['output_shape'] = [
None, None, output_shape, output_shape, None
]
elif generator_type == 'deepconv':
params = NDNutils.ffnetwork_params(
input_dims=[1, input_noise_size],
layer_sizes=[[512, 4, 4], 256, 128, 1, 1],
layer_types=['normal', 'deconv', 'deconv', 'deconv', 'mask'],
act_funcs=['relu', 'relu', 'relu', 'tanh', 'lin'],
conv_filter_widths=[None, 5, 5, 5, None],
shift_spacing=[None, 2, 2, 2, None],
reg_list={'d2x': [None, None, None, 0.01, None]},
verbose=False)
params['output_shape'] = [None, None, None, output_shape, None]
elif generator_type == 'lin' or generator_type == 'lin_tanh':
act = 'lin' if generator_type == 'lin' else 'tanh'
params = NDNutils.ffnetwork_params(
input_dims=[1, input_noise_size],
layer_sizes=[512, 1024, [1, 31, 31], 1],
layer_types=['normal', 'normal', 'normal', 'mask'],
act_funcs=['tanh', 'tanh', act, 'lin'],
reg_list={
'l2': [0.01, 0.01, 0.01, None],
},
verbose=False)
layers = 4
elif generator_type == 'hybrid':
params = NDNutils.ffnetwork_params(
input_dims=[1, input_noise_size],
layer_sizes=[256, [16, 16, 16], 8, 1, 1],
layer_types=['normal', 'normal', 'deconv', 'deconv', 'mask'],
act_funcs=['relu', 'relu', 'relu', 'tanh', 'lin'],
conv_filter_widths=[None, 5, 5, 5, None],
shift_spacing=[None, 2, 2, 1, None],
reg_list={'d2x': [None, None, 0.01, 0.01, None]},
verbose=False)
params['output_shape'] = [
None, None, output_shape, output_shape, None
]
else:
raise ValueError(
f'Generator type \'{generator_type}\' not implemented.')
params['xstim_n'] = [0]
params['normalize_output'] = [None] * layers
params['weights_initializers'] = ['normal'] * (layers - 1) + ['ones']
params['biases_initializers'] = ['zeros'] * layers
return params
num_subs=NC
num_hid=num_subs//2
# 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={'d2xt':[XTreg], 'l1':[L1reg0, L1reg0], 'glocal':[Greg0]})
t_layer = NDNutils.ffnetwork_params(
input_dims=[1,NX2,NX2], time_expand=num_lags,
layer_sizes=[1],
layer_types=['temporal'],
normalization=[1],
act_funcs=['lin'],
verbose=True,
reg_list={'d2t':[0.05]})
ndn_par = NDNutils.ffnetwork_params(
xstim_n=None,
ffnet_n=0,
input_dims=[1,NX2,NX2],
layer_sizes=[num_subs, num_hid],
layer_types=['normal','normal'],
normalization=[1, 1],
act_funcs=['relu', 'relu'],
verbose=True,
reg_list={'d2x':[XTreg], 'l1':[L1reg0, L1reg0], 'glocal':[Greg0]})