mult_and_shift = "post"
arch_dict = {
"D": system.D,
"latent_dynamics": latent_dynamics,
"mult_and_shift": mult_and_shift,
"TIF_flow_type": TIF_flow_type,
"repeats": nlayers,
}
k_max = 40
batch_size = 1000
lr_order = -3
min_iters = 1000
max_iters = 2000
train_dsn(
system,
batch_size,
arch_dict,
k_max=k_max,
sigma_init=sigma_init,
c_init_order=c_init_order,
lr_order=lr_order,
random_seed=random_seed,
min_iters=min_iters,
max_iters=max_iters,
check_rate=100,
dir_str="test",
)
c_init_order,
random_seed,
dir_str,
randsearch=randsearch)
print('-- Hyperparameters --')
print('batch_size:', batch_size)
print('sigma_init:', sigma_init)
print('c_init_order:', c_init_order)
print('AL_fac:', AL_fac)
print('epoch iters:', max_iters)
train_dsn(
system,
arch_dict,
batch_size,
AL_it_max=AL_it_max,
sigma_init=sigma_init,
c_init_order=c_init_order,
AL_fac=AL_fac,
min_iters=min_iters,
max_iters=max_iters,
random_seed=random_seed,
lr_order=lr_order,
check_rate=100,
dir_str='test',
savedir=savedir,
entropy=True,
db=False,
)
'repeats':repeats,
'nlayers':nlayers,
'upl':upl,
'sigma_init':sigma_init*np.ones((system.D,))
}
param_dict.update(arch_params)
arch_dict = get_arch_from_template(system, param_dict)
AL_it_max = 20
batch_size = 1000
lr_order = -3
AL_fac = 4.0
iters = 5000
train_dsn(
system,
arch_dict,
batch_size,
AL_it_max=AL_it_max,
c_init_order=c_init_order,
AL_fac=AL_fac,
min_iters=iters,
max_iters=iters,
random_seed=random_seed,
lr_order=lr_order,
check_rate=100,
dir_str="V1Circuit_test",
db=False
)
"d_var": d_vars,
}
# set model options
model_opts = {"g_FF": "c", "g_LAT": "linear", "g_RUN": "r"}
T = 40
dt = 0.25
init_conds = np.expand_dims(np.array([1.0, 1.1, 1.2, 1.3]), 1)
system = V1Circuit(fixed_params, behavior, model_opts, T, dt, init_conds)
k_max = 20
batch_size = 1000
lr_order = -3
train_dsn(
system,
batch_size,
arch_dict,
k_max=k_max,
sigma_init=sigma_init,
c_init_order=c_init_order,
lr_order=lr_order,
random_seed=random_seed,
min_iters=1000,
max_iters=2000,
check_rate=100,
dir_str="V1Circuit_S_same_V_inc",
)
mu = np.zeros((D, ))
df_fac = 15
df = df_fac * D
Sigma_dist = scipy.stats.invwishart(df=df, scale=df * np.eye(D))
Sigma = Sigma_dist.rvs(1)
behavior = {"mu": mu, "Sigma": Sigma}
print("Behavior:")
print("mu")
print(mu)
print("Sigma")
print(Sigma[0])
print(Sigma[1])
cost, phi, T_x = train_dsn(
system,
behavior,
n,
flow_dict,
k_max=k_max,
sigma_init=sigma_init,
c_init_order=c_init_order,
lr_order=lr_order,
random_seed=random_seed,
min_iters=min_iters,
max_iters=max_iters,
check_rate=check_rate,
dir_str=dir_str,
)
}
param_dict.update(arch_params)
arch_dict = get_arch_from_template(system, param_dict)
n = 1000
AL_it_max = 20
AL_fac = 4.0
lr_order = -3
min_iters = 2000
max_iters = 2000
check_rate = 100
dist_seed = 0
dir_str = "2DLDS_test"
np.random.seed(dist_seed)
cost, z = train_dsn(
system,
arch_dict,
n=n,
AL_it_max=AL_it_max,
c_init_order=c_init_order,
AL_fac=AL_fac,
min_iters=min_iters,
max_iters=max_iters,
random_seed=random_seed,
lr_order=lr_order,
check_rate=check_rate,
dir_str=dir_str,
db=False,
)
"sigma_init": sigma_init,
}
param_dict.update(arch_params)
arch_dict = get_arch_from_template(system, param_dict)
batch_size = 300
AL_it_max = 40
AL_fac = 4.0
min_iters = 5000
max_iters = 5000
lr_order = -3
train_dsn(
system,
arch_dict,
batch_size,
AL_it_max=AL_it_max,
c_init_order=c_init_order,
AL_fac=AL_fac,
min_iters=min_iters,
max_iters=max_iters,
random_seed=random_seed,
lr_order=lr_order,
check_rate=100,
dir_str="SC_WTA_NI",
savedir=None,
entropy=True,
db=False,
)