def _load_current_data(self, X, cp, Tim):
if self.perm is not None:
X_current = perm_data(X[self.current_samples, :, :], self.perm)
else:
X_current = X[self.current_samples, :, :]
y = _combine_params(cp, Tim)
y_current = y[self.current_samples]
#mean_X = np.mean(X_current, axis=(1, 2))
#X_current = X_current - mean_X[:, np.newaxis, np.newaxis]
return X_current, y_current
def main(_):
global EPOCH_SIZE
# Initialize tempdir
if FLAGS.action == "eval" and FLAGS.read_dir is not None:
FLAGS.log_dir = FLAGS.read_dir
else:
FLAGS.log_dir = os.path.join(FLAGS.log_dir, FLAGS.model_type)
exp_n = _last_exp(
FLAGS.log_dir) + 1 if FLAGS.action == "train" else _last_exp(
FLAGS.log_dir)
FLAGS.log_dir = os.path.join(FLAGS.log_dir, "exp_" + str(exp_n))
print(FLAGS.log_dir)
# Initialize data
X, y = load_data()
G = create_spatial_eeg_graph(MONTAGE, q=FLAGS.q, k=FLAGS.k)
# G = create_data_eeg_graph(MONTAGE, X)
G.compute_laplacian("normalized")
if FLAGS.action == "train":
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
# Prepare pooling
num_levels = _number_of_pooling_levels(FLAGS.vertex_poolings)
error = True
while error:
try:
adjacencies, perm = coarsen(
G.A, levels=num_levels) # Coarsens in powers of 2
error = False
except IndexError:
error = True
continue
np.save(os.path.join(FLAGS.log_dir, "ordering"), perm)
L = [initialize_laplacian_tensor(A) for A in adjacencies]
L = keep_pooling_laplacians(L, FLAGS.vertex_poolings)
elif FLAGS.action == "eval":
perm = np.load(os.path.join(FLAGS.log_dir, "ordering.npy"))
X = perm_data(X, perm)
train_samples, test_samples = train_validation_test_split(
len(y), FLAGS.margin, FLAGS.test_size)
if FLAGS.action == "train":
EPOCH_SIZE = train_samples.shape[0]
print(EPOCH_SIZE)
train_mb_source = EyeMinibatchSource(X,
y,
train_samples,
margin=FLAGS.margin,
repeat=True)
EPOCH_SIZE = train_mb_source.dataset_length
test_mb_source = EyeMinibatchSource(X,
y,
test_samples,
margin=FLAGS.margin,
repeat=False)
if FLAGS.action == "train":
params = vars(FLAGS)
with open(os.path.join(FLAGS.log_dir, "params.json"), "w") as f:
json.dump(params, f)
# Run training and evaluation loop
print("Training model...")
run_training(L, train_mb_source, test_mb_source)
elif FLAGS.action == "eval":
print("Evaluating model...")
run_eval(test_mb_source)
else:
raise ValueError("No valid action selected")
def main(_):
# Initialize tempdir
if FLAGS.action == "eval" and FLAGS.read_dir is not None:
FLAGS.log_dir = FLAGS.read_dir
else:
FLAGS.log_dir = os.path.join(FLAGS.log_dir, FLAGS.model_type)
exp_n = _last_exp(
FLAGS.log_dir) + 1 if FLAGS.action == "train" else _last_exp(
FLAGS.log_dir)
FLAGS.log_dir = os.path.join(FLAGS.log_dir, "exp_" + str(exp_n))
print(FLAGS.log_dir)
if FLAGS.action == "train":
if tf.io.gfile.exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.io.gfile.makedirs(FLAGS.log_dir)
# Initialize data
G = graphs.Community(FLAGS.num_vertices, seed=FLAGS.seed)
G.compute_laplacian("normalized")
# Save graph
np.save(os.path.join(FLAGS.log_dir, "graph_weights"), G.W.todense())
# Prepare pooling
num_levels = _number_of_pooling_levels(FLAGS.vertex_poolings)
error = True
while error:
try:
adjacencies, perm = coarsen(
G.A, levels=num_levels) # Coarsens in powers of 2
error = False
except IndexError:
error = True
continue
np.save(os.path.join(FLAGS.log_dir, "ordering"), perm)
L = [initialize_laplacian_tensor(A) for A in adjacencies]
L = keep_pooling_laplacians(L, FLAGS.vertex_poolings)
elif FLAGS.action == "eval":
W = np.load(os.path.join(FLAGS.log_dir, "graph_weights.npy"))
G = graphs.Graph(W)
G.compute_laplacian("normalized")
perm = np.load(os.path.join(FLAGS.log_dir, "ordering.npy"))
if FLAGS.action == "train":
#G_nx = nx.from_numpy_matrix(G.W.todense())
if FLAGS.load_data:
#num test can't exceed saved array size
train_data, train_labels = load_from_npy('train', FLAGS.num_train)
else:
random.seed(FLAGS.seed)
train_data, train_labels = epidemics_generator(
g_nx=nx.from_numpy_matrix(G.W.todense()),
batch_size=FLAGS.num_train,
timesteps=FLAGS.num_frames,
initial_nodes=random.sample(range(FLAGS.num_vertices),
int(FLAGS.num_vertices / 10)))
save_to_npy('train', train_data, train_labels)
train_data = perm_data(train_data, perm)
train_mb_source = MinibatchSource(train_data,
train_labels,
repeat=True)
if FLAGS.load_data:
#num test can't exceed saved array size
test_data, test_labels = load_from_npy('test', FLAGS.num_test)
else:
test_data, test_labels = epidemics_generator(
g_nx=nx.from_numpy_matrix(G.W.todense()),
batch_size=FLAGS.num_test,
timesteps=FLAGS.num_frames,
initial_nodes=random.sample(range(FLAGS.num_vertices),
int(FLAGS.num_vertices / 10)))
save_to_npy('test', test_data, test_labels)
test_data = perm_data(test_data, perm)
test_mb_source = MinibatchSource(test_data, test_labels, repeat=False)
if FLAGS.action == "train":
params = vars(FLAGS)
with open(os.path.join(FLAGS.log_dir, "params.json"), "w") as f:
json.dump(params, f)
# Run training and evaluation loop
print("Training model...")
run_training(L, train_mb_source, test_mb_source)
elif FLAGS.action == "eval":
print("Evaluating model...")
run_eval(test_mb_source)
else:
raise ValueError("No valid action selected")
def main(_):
# Initialize tempdir
if FLAGS.action == "eval" and FLAGS.read_dir is not None:
FLAGS.log_dir = FLAGS.read_dir
else:
FLAGS.log_dir = os.path.join(FLAGS.log_dir, FLAGS.model_type)
exp_n = _last_exp(
FLAGS.log_dir) + 1 if FLAGS.action == "train" else _last_exp(
FLAGS.log_dir)
FLAGS.log_dir = os.path.join(FLAGS.log_dir, "exp_" + str(exp_n))
print(FLAGS.log_dir)
if FLAGS.action == "train":
if tf.gfile.Exists(FLAGS.log_dir):
tf.gfile.DeleteRecursively(FLAGS.log_dir)
tf.gfile.MakeDirs(FLAGS.log_dir)
# Initialize data
G = graphs.Community(FLAGS.num_vertices,
seed=FLAGS.seed) #, world_density = 0.1)
G.compute_laplacian("normalized")
# Save graph
np.save(os.path.join(FLAGS.log_dir, "graph_weights"), G.W.todense())
# Prepare pooling
num_levels = _number_of_pooling_levels(FLAGS.vertex_poolings)
error = True
while error:
try:
adjacencies, perm = coarsen(
G.A, levels=num_levels) # Coarsens in powers of 2
error = False
except IndexError:
error = True
continue
np.save(os.path.join(FLAGS.log_dir, "ordering"), perm)
L = [initialize_laplacian_tensor(A) for A in adjacencies]
L = keep_pooling_laplacians(L, FLAGS.vertex_poolings)
#0 graph
if FLAGS.zeros == True:
for i in range(len(L)):
L[i] *= 0
print(FLAGS.zeros)
print("0 Graph \n\n")
elif FLAGS.action == "eval":
W = np.load(os.path.join(FLAGS.log_dir, "graph_weights.npy"))
G = graphs.Graph(W)
G.compute_laplacian("normalized")
perm = np.load(os.path.join(FLAGS.log_dir, "ordering.npy"))
if FLAGS.action == "train":
train_data, train_labels = generate_spectral_samples_hard(
N=FLAGS.num_train // FLAGS.num_classes,
G=G,
T=FLAGS.num_frames,
f_h=FLAGS.f_h,
f_l=FLAGS.f_h,
lambda_h=FLAGS.lambda_h,
lambda_l=FLAGS.lambda_l,
sigma=FLAGS.sigma,
sigma_n=FLAGS.sigma_n)
train_data = perm_data(train_data, perm)
train_mb_source = MinibatchSource(train_data,
train_labels,
repeat=True)
test_data, test_labels = generate_spectral_samples_hard(
N=FLAGS.num_test // FLAGS.num_classes,
G=G,
T=FLAGS.num_frames,
f_h=FLAGS.f_h,
f_l=FLAGS.f_h,
lambda_h=FLAGS.lambda_h,
lambda_l=FLAGS.lambda_l,
sigma=FLAGS.sigma,
sigma_n=FLAGS.sigma_n)
test_data = perm_data(test_data, perm)
test_mb_source = MinibatchSource(test_data, test_labels, repeat=False)
if FLAGS.action == "train":
params = vars(FLAGS)
with open(os.path.join(FLAGS.log_dir, "params.json"), "w") as f:
json.dump(params, f)
# Run training and evaluation loop
print("Training model...")
run_training(L, train_mb_source, test_mb_source)
elif FLAGS.action == "eval":
print("Evaluating model...")
run_eval(test_mb_source)
else:
raise ValueError("No valid action selected")
