def vary_weight_loss(i):
root_dir = '/Users/peterwang/Desktop/PYTHON/Neural_head_direction/experiments/vary_weight_loss'
stationary_opts = config.stationary_model_config()
stationary_opts.save_path = os.path.join(root_dir, 'files',
str(i).zfill(2), 'stationary')
stationary_opts.rnn_size = 60
stationary_opts.load_checkpoint = False
stationary_opts.epoch = 700
stationary_opts.time_steps = 25
stationary_opts.time_loss_start = 5
stationary_opts.time_loss_end = 25
stationary_opts.activity_alpha = .1
moving_opts = config.non_stationary_model_config()
moving_opts.save_path = os.path.join(root_dir, 'files',
str(i).zfill(2), 'moving')
moving_opts.load_checkpoint = False
moving_opts.dir_weights = os.path.join(stationary_opts.save_path,
'weight.pkl')
moving_opts.initialize_W_ab_diagonal = False
moving_opts.initialize_W_ba_diagonal = False
moving_opts.epoch = 700
moving_opts.velocity_use = 2
moving_opts.velocity_max = 2
moving_opts.weight_alpha = 1
moving_opts.activity_alpha = .1
hp_ranges = OrderedDict()
hp_ranges['weight_alpha'] = [0, 1]
argWhich = 0
return stationary_opts, moving_opts, hp_ranges, argWhich
def stationary_curriculum():
st_model_opts = config.stationary_model_config()
st_model_opts.weight_alpha = .5
st_model_opts.activity_alpha = .1
st_model_opts.losses = 'full'
st_model_opts.activation_fn = 'relu'
st_model_opts.mask = True
def vary_batch(i):
opts = config.stationary_model_config()
opts.save_path = '/Users/peterwang/Desktop/PYTHON/Neural_head_direction/experiments' \
'/vary_batch/files/' + str(i).zfill(2)
opts.rnn_size = 60
opts.load_checkpoint = False
opts.epoch = 300
opts.time_steps = 25
opts.time_loss_start = 5
opts.time_loss_end = 25
hp_ranges = OrderedDict()
hp_ranges['batch_size'] = [5, 10, 20, 25, 40, 50, 100]
return opts, hp_ranges
'[*] Epoch %d total_loss=%.2f xe_loss=%.2f a_loss=%.2f, w_loss=%.2f'
% (ep, cur_loss, xe_loss, activity_loss, weight_loss))
#save latest
model.save()
utils.save_parameters(opts,
os.path.join(save_path, opts.parameter_name))
model.save_weights()
save_activity(model, next_element[0], next_element[1])
with open(os.path.join(save_path, opts.log_name + '.pkl'),
'wb') as f:
pkl.dump(logger, f)
if __name__ == '__main__':
st_model_opts = config.stationary_model_config()
opts = st_model_opts
opts.save_path = './test/stationary'
opts.epoch = 300
opts.load_checkpoint = True
train(opts)
non_st_model_opts = config.non_stationary_model_config()
opts = non_st_model_opts
opts.save_path = './test/non_stationary'
opts.load_checkpoint = True
opts.load_weights = False
opts.dir_weights = './test/stationary/weight.pkl'
opts.epoch = 500
opts.velocity_use = 2
opts.velocity_max = 2
def curriculum(params):
# quick settings
def fast_hd_trig(opts):
opts.input_mode = 'trig'
opts.output_mode = 'trig'
opts.grid_input = False
opts.linear_track = False
return opts
def fast_hd_bump(opts):
opts.input_mode = 'bump'
opts.output_mode = 'bump'
opts.grid_input = False
opts.linear_track = False
return opts
def fast_grid(opts):
opts.input_mode = 'bump'
opts.output_mode = 'bump'
opts.grid_input = True
opts.linear_track = True
return opts
rnn_size, state_size = params
parser = arg_parser()
opts = parser.parse_args()
opts.rnn_size = rnn_size
linear_track = False
if linear_track:
track = 'linear'
else:
track = 'circular'
loss = 'error' # ['error', 'activity', 'weights', 'full']
# loss = 'activity' # ['error', 'activity', 'weights', 'full']
# loss = 'full' # ['error', 'activity', 'weights', 'full']
fn = 'relu' # [relu, tanh]
st_model_opts = config.stationary_model_config()
# st_model_opts.output_mode = output_mode
st_model_opts.weight_alpha = .5
st_model_opts.activity_alpha = .1
st_model_opts.rnn_size = rnn_size
st_model_opts.state_size = state_size
st_model_opts.linear_track = linear_track
st_model_opts.losses = loss
st_model_opts.activation_fn = fn
st_model_opts.mask = True
velocity_min = 1
velocity_max = 3
nonst_model_opts = config.non_stationary_model_config()
# nonst_model_opts.output_mode = output_mode
# nonst_model_opts.input_mode = input_mode
nonst_model_opts.linear_track = linear_track
nonst_model_opts.velocity_max = 3
nonst_model_opts.weight_alpha = .5
nonst_model_opts.activity_alpha = .1
nonst_model_opts.rnn_size = rnn_size
nonst_model_opts.state_size = state_size
nonst_model_opts.losses = loss
nonst_model_opts.activation_fn = fn
nonst_model_opts.mask = True
nonst_model_opts.boundary_velocity = False
nonst_model_opts.correlated_path = False
# get all present dirs
modeldir = os.path.join(os.getcwd(), 'training')
exp = re.compile('model([0-9]+)')
used_ix = []
with os.scandir(modeldir) as models:
for m in models:
rg = exp.match(m.name)
if rg:
used_ix.append(int(rg.group(1)))
max_ix = np.amax(used_ix)
full_ix = np.arange(max_ix + 2) # include the next index above the highest
free_ix = [a for a in full_ix if a not in used_ix]
new_ix = np.amin(free_ix)
nonst_model_opts.save_path = os.path.join(os.getcwd(), 'training',
f'model{str(new_ix).zfill(3)}')
nonst_model_opts.dir_weights = os.path.join(
nonst_model_opts.save_path, nonst_model_opts.model_name + '.pkl')
# first = copy.deepcopy(st_model_opts)
# # first.epoch = int(201)
# first.epoch = int(1)
# first.load_checkpoint = False
#
# first_more = copy.deepcopy(st_model_opts)
# # first_more.epoch = int(101)
# first_more.epoch = int(201)
# first_more.learning_rate = 1e-4
# first_more.load_checkpoint = True
second = copy.deepcopy(nonst_model_opts)
second.epoch = int(300)
# second.epoch = int(1)
second.load_checkpoint = False
second.load_weights = False
second.time_steps = 50
second.time_loss_start = 5
second.time_loss_end = second.time_steps
second.velocity_step = 1
second.velocity_min = velocity_min
second.velocity_max = velocity_max
second.dir_weights = os.path.join(st_model_opts.save_path,
st_model_opts.weight_name + '.pkl')
second.learning_rate = .001
# second.debug_weights = True
# n_env = 10
# second.subtrack = False
# second.rescale_env = True
# second.n_env = n_env
# second.bump_in_network = False
# second.grid_input = False
second.dropout = False
second.dropout_rate = .4
second.noise = False
second.noise_intensity = .1
second.noise_density = .5
second.nonneg_input = True
second.EI_in = False
second.EI_h = False
second.EI_out = False
second.prop_ex = .8
#### ASSIGN TRAINING PARADIGM ####
second = fast_hd_trig(second)
# second = fast_hd_bump(second)
# second = fast_grid(second)
# c = [first]
# c = [first_more]
# c = [first, first_more]
c = [second]
# c = [second_more]
# c = [second, second_more]
# c = [second_last]
# c = [moving_static, second_more]
return c