def check_shallow_env_corrs():
i_layer = 5
for i_exp in xrange(1, 21):
file_base_name = 'data/models-backup/paper/ours/cnt/shallow//car/{:d}'.format(
i_exp)
result = np.load(file_base_name + '.result.pkl')
log.info("Running {:d} of {:d}:\n{:s}".format(i_exp, 20,
file_base_name))
exp_shallow, model_shallow = load_exp_and_model(file_base_name)
exp_shallow.dataset.load()
env_filename = dataset_to_env_file(
result.parameters['dataset_filename'])
trial_env = load_trial_env(env_filename,
model_shallow,
i_layer,
exp_shallow.dataset.sets[0],
n_inputs_per_trial=2)
trial_acts = compute_trial_acts(model_shallow, i_layer,
exp_shallow.iterator,
exp_shallow.dataset.train_set)
log.info("Compute topo corrs")
topo_corrs = compute_topo_corrs(trial_env, trial_acts)
topo_corrs_old = np.load(file_base_name + '5.env_corrs.npy')
diff = np.mean(np.abs(topo_corrs - topo_corrs_old))
log.info("Diff ok {:f}".format(diff))
assert diff < 1e-3, ("Diff too big for {:s}: {:f}".format(
file_base_name, diff))
def create_env_class_corr_files_after_load(base_name, with_square, exp, model):
train_set = exp.dataset_provider.get_train_merged_valid_test(exp.dataset)['train']
trial_env = load_trial_env(base_name + '.env.npy',
model, i_layer=26, # 26 is last max-pool i think
train_set=train_set,
n_inputs_per_trial=2,
square_before_mean=with_square)
topo_corrs = compute_env_class_corr(exp, trial_env)
rand_model = create_experiment(base_name + '.yaml').final_layer
rand_topo_corrs = compute_rand_preds_topo_corr(exp, rand_model,
trial_env)
file_name_end = 'class.npy'
if with_square:
file_name_end = 'square.' + file_name_end
np.save('{:s}.env_corrs.{:s}'.format(base_name, file_name_end), topo_corrs)
np.save('{:s}.env_rand_corrs.{:s}'.format(base_name, file_name_end), rand_topo_corrs)
def create_env_class_corr_files_after_load(base_name, with_square, exp, model):
train_set = exp.dataset_provider.get_train_merged_valid_test(
exp.dataset)['train']
trial_env = load_trial_env(
base_name + '.env.npy',
model,
i_layer=26, # 26 is last max-pool i think
train_set=train_set,
n_inputs_per_trial=2,
square_before_mean=with_square)
topo_corrs = compute_env_class_corr(exp, trial_env)
rand_model = create_experiment(base_name + '.yaml').final_layer
rand_topo_corrs = compute_rand_preds_topo_corr(exp, rand_model, trial_env)
file_name_end = 'class.npy'
if with_square:
file_name_end = 'square.' + file_name_end
np.save('{:s}.env_corrs.{:s}'.format(base_name, file_name_end), topo_corrs)
np.save('{:s}.env_rand_corrs.{:s}'.format(base_name, file_name_end),
rand_topo_corrs)
def check_shallow_env_corrs():
i_layer = 5
for i_exp in xrange(1,21):
file_base_name = 'data/models-backup/paper/ours/cnt/shallow//car/{:d}'.format(
i_exp)
result = np.load(file_base_name + '.result.pkl')
log.info("Running {:d} of {:d}:\n{:s}".format(i_exp, 20,
file_base_name))
exp_shallow, model_shallow = load_exp_and_model(file_base_name)
exp_shallow.dataset.load()
env_filename = dataset_to_env_file(result.parameters['dataset_filename'])
trial_env = load_trial_env(env_filename, model_shallow,
i_layer, exp_shallow.dataset.sets[0],
n_inputs_per_trial=2)
trial_acts = compute_trial_acts(model_shallow, i_layer,
exp_shallow.iterator, exp_shallow.dataset.train_set)
log.info("Compute topo corrs")
topo_corrs = compute_topo_corrs(trial_env, trial_acts)
topo_corrs_old = np.load(file_base_name + '5.env_corrs.npy')
diff = np.mean(np.abs(topo_corrs - topo_corrs_old))
log.info("Diff ok {:f}".format(diff))
assert diff < 1e-3, (
"Diff too big for {:s}: {:f}".format(file_base_name, diff))
def create_topo_env_corrs_files(base_name, i_all_layers, with_square):
# Load env first to make sure env is actually there.
result = np.load(base_name + '.result.pkl')
env_file_name = dataset_to_env_file(result.parameters['dataset_filename'])
exp, model = load_exp_and_model(base_name)
exp.dataset.load()
train_set = exp.dataset_provider.get_train_merged_valid_test(
exp.dataset)['train']
rand_model = create_experiment(base_name + '.yaml').final_layer
for i_layer in i_all_layers:
log.info("Layer {:d}".format(i_layer))
trial_env = load_trial_env(env_file_name, model,
i_layer, train_set, n_inputs_per_trial=2, square_before_mean=with_square)
topo_corrs = compute_trial_topo_corrs(model, i_layer, train_set,
exp.iterator, trial_env, split_per_class=True)
rand_topo_corrs = compute_trial_topo_corrs(rand_model, i_layer, train_set,
exp.iterator, trial_env, split_per_class=True)
file_name_end = '{:d}.npy'.format(i_layer)
if with_square:
file_name_end = 'square.' + file_name_end
np.save('{:s}.labelsplitted.env_corrs.{:s}'.format(base_name, file_name_end), topo_corrs)
np.save('{:s}.labelsplitted.env_rand_corrs.{:s}'.format(base_name, file_name_end), rand_topo_corrs)
return
def create_topo_env_corrs_files(base_name, i_all_layers, with_square):
# Load env first to make sure env is actually there.
result = np.load(base_name + '.result.pkl')
env_file_name = dataset_to_env_file(result.parameters['dataset_filename'])
exp, model = load_exp_and_model(base_name)
exp.dataset.load()
train_set = exp.dataset_provider.get_train_merged_valid_test(
exp.dataset)['train']
rand_model = create_experiment(base_name + '.yaml').final_layer
for i_layer in i_all_layers:
log.info("Layer {:d}".format(i_layer))
trial_env = load_trial_env(env_file_name, model,
i_layer, train_set, n_inputs_per_trial=2, square_before_mean=with_square)
topo_corrs = compute_trial_topo_corrs(model, i_layer, train_set,
exp.iterator, trial_env)
rand_topo_corrs = compute_trial_topo_corrs(rand_model, i_layer, train_set,
exp.iterator, trial_env)
file_name_end = '{:d}.npy'.format(i_layer)
if with_square:
file_name_end = 'square.' + file_name_end
np.save('{:s}.labelsplitted.env_corrs.{:s}'.format(base_name, file_name_end), topo_corrs)
np.save('{:s}.labelsplitted.env_rand_corrs.{:s}'.format(base_name, file_name_end), rand_topo_corrs)
return
