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 compute_trial_topo_corrs(model, i_layer, train_set, iterator, trial_env,
split_per_class):
"""Now with split per class arg."""
trial_acts = compute_trial_acts(model, i_layer, iterator, train_set)
trial_labels = determine_trial_labels(train_set, iterator)
if split_per_class:
topo_corrs_per_class = []
for i_label in xrange(trial_labels.shape[1]):
this_trial_mask = trial_labels[:,i_label] == 1
this_trial_env = trial_env[:, this_trial_mask]
this_trial_acts = trial_acts[this_trial_mask]
topo_corrs = compute_topo_corrs(this_trial_env, this_trial_acts)
topo_corrs_per_class.append(topo_corrs)
topo_corrs_per_class = np.array(topo_corrs_per_class)
# TODO compute within trial corrs -> remove mean for each trial
# compute between trial corrs -> compute means for trial env and trial acts
# with keepdims=True
return topo_corrs_per_class
else:
topo_corrs = compute_topo_corrs(trial_env, trial_acts)
return topo_corrs
def compute_rand_preds_topo_corr(exp, rand_model, trial_env):
pred_fn = create_pred_fn(rand_model)
train_set = exp.dataset_provider.get_train_merged_valid_test(exp.dataset)['train']
batches = [b[0] for b in exp.iterator.get_batches(train_set, shuffle=False)]
all_batch_sizes = [len(b) for b in batches]
all_preds = [pred_fn(b) for b in batches]
preds_per_trial = compute_preds_per_trial(train_set.y, all_preds, all_batch_sizes,
exp.iterator.input_time_length)
preds_per_trial = np.array(preds_per_trial)
# transpose from trialxsamplesxclasses to trialsxclassesxsamples
# as compute topo corr expects like this
rand_topo_corrs = compute_topo_corrs(trial_env,
preds_per_trial.transpose(0,2,1))
return rand_topo_corrs
def compute_env_class_corr(exp, trial_env):
train_set = exp.dataset_provider.get_train_merged_valid_test(
exp.dataset)['train']
i_trial_starts, i_trial_ends = compute_trial_start_end_samples(
train_set.y,
check_trial_lengths_equal=True,
input_time_length=exp.iterator.input_time_length)
assert len(i_trial_ends) == trial_env.shape[1]
# +1 as i_end is inclusive
y_signal = [exp.dataset.train_set.y[i_start:i_end+1]
for i_start, i_end in zip(i_trial_starts, i_trial_ends)]
y_signal = np.array(y_signal).transpose(0,2,1)
assert y_signal.shape[2] == trial_env.shape[3]
topo_corrs = compute_topo_corrs(trial_env, y_signal)
return topo_corrs
def compute_env_class_corr(exp, trial_env):
train_set = exp.dataset_provider.get_train_merged_valid_test(
exp.dataset)['train']
i_trial_starts, i_trial_ends = compute_trial_start_end_samples(
train_set.y,
check_trial_lengths_equal=True,
input_time_length=exp.iterator.input_time_length)
assert len(i_trial_ends) == trial_env.shape[1]
# +1 as i_end is inclusive
y_signal = [
exp.dataset.train_set.y[i_start:i_end + 1]
for i_start, i_end in zip(i_trial_starts, i_trial_ends)
]
y_signal = np.array(y_signal).transpose(0, 2, 1)
assert y_signal.shape[2] == trial_env.shape[3]
topo_corrs = compute_topo_corrs(trial_env, y_signal)
return topo_corrs
def compute_rand_preds_topo_corr(exp, rand_model, trial_env):
pred_fn = create_pred_fn(rand_model)
train_set = exp.dataset_provider.get_train_merged_valid_test(
exp.dataset)['train']
batches = [
b[0] for b in exp.iterator.get_batches(train_set, shuffle=False)
]
all_batch_sizes = [len(b) for b in batches]
all_preds = [pred_fn(b) for b in batches]
preds_per_trial = compute_preds_per_trial(train_set.y, all_preds,
all_batch_sizes,
exp.iterator.input_time_length)
preds_per_trial = np.array(preds_per_trial)
# transpose from trialxsamplesxclasses to trialsxclassesxsamples
# as compute topo corr expects like this
rand_topo_corrs = compute_topo_corrs(trial_env,
preds_per_trial.transpose(0, 2, 1))
return rand_topo_corrs
def compute_trial_topo_corrs(model, i_layer, train_set, iterator, trial_env):
"""Now split per class."""
trial_acts = compute_trial_acts(model, i_layer, iterator, train_set)
trial_labels = determine_trial_labels(train_set, iterator)
topo_corrs_per_class = []
for i_label in xrange(trial_labels.shape[1]):
this_trial_mask = trial_labels[:,i_label] == 1
this_trial_env = trial_env[:, this_trial_mask]
this_trial_acts = trial_acts[this_trial_mask]
topo_corrs = compute_topo_corrs(this_trial_env, this_trial_acts)
topo_corrs_per_class.append(topo_corrs)
topo_corrs_per_class = np.array(topo_corrs_per_class)
# TODELETE (old without split input per label):
# topo_corrs = compute_topo_corrs(trial_env, trial_acts)
# TODO compute within trial corrs -> remove mean for each trial
# compute between trial corrs -> compute means for trial env and trial acts
# with keepdims=True
return topo_corrs_per_class
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))
