def usfaces_df(queue):
"""Preprocess and augment US Face Database faces to data/. Returns pandas dataframe"""
usfaces_df = pd.read_excel("Full Attribute Scores/demographic & others labels/demographic-others-labels.xlsx")
usfaces_df = usfaces_df[["Filename", "Attractive"]]
usfaces_df = usfaces_df.drop_duplicates(["Filename"])
for face in usfaces_df["Filename"]:
base = os.path.splitext(face)[0]
try:
preprocess.resize("10k US Adult Faces Database/Face Images/{0}".format(face), "data/{0}".format(face))
preprocess.hflip("data/{0}".format(face), "data/{0}-F.jpg".format(base))
preprocess.add_noise("data/{0}".format(face), "data/{0}-N.jpg".format(base))
except:
usfaces_df = usfaces_df[usfaces_df.Filename != face]
flipped_df = usfaces_df.copy()
noisy_df = usfaces_df.copy()
flipped_df["Filename"] = flipped_df["Filename"].str[:-4] + "-F.jpg"
noisy_df["Filename"] = noisy_df["Filename"].str[:-4] + "-N.jpg"
df = pd.concat([usfaces_df, flipped_df, noisy_df], ignore_index=True)
df.columns = ["Face", "Rating"]
df["Rating"] *= 10.0 / 5.0
queue.put(df)
def scutfbp_df(queue):
"""Preprocess and augment SCUT-FBP faces to data/. Returns pandas dataframe"""
scutfbp_df = pd.read_excel("Rating_Collection/Attractiveness label.xlsx")
# Convert type of #Image column to str
scutfbp_df["#Image"] = scutfbp_df["#Image"].astype(str)
# Drop column Standard Deviation
scutfbp_df = scutfbp_df.drop("Standard Deviation", 1)
for face in os.listdir("Data_Collection"):
if face.endswith(".jpg"):
base = os.path.splitext(face)[0]
# Regex to find numbers at end of string
img_num = re.match(".*?([0-9]+)$", base).group(1)
try:
preprocess.resize("Data_Collection/{0}".format(face), "data/{0}".format(face))
preprocess.hflip("data/{0}".format(face), "data/{0}-F.jpg".format(base))
preprocess.add_noise("data/{0}".format(face), "data/{0}-N.jpg".format(base))
except:
scutfbp_df = scutfbp_df[getattr(scutfbp_df, "#Image") != img_num]
flipped_df = scutfbp_df.copy()
noisy_df = scutfbp_df.copy()
flipped_df["#Image"] = "SCUT-FBP-" + flipped_df["#Image"] + "-F.jpg"
noisy_df["#Image"] = "SCUT-FBP-" + noisy_df["#Image"] + "-N.jpg"
scutfbp_df["#Image"] = "SCUT-FBP-" + scutfbp_df["#Image"] + ".jpg"
df = pd.concat([scutfbp_df, flipped_df, noisy_df], ignore_index=True)
# Rename #Image -> Face and Attractiveness label -> Rating
df.columns = ["Face", "Rating"]
# Convert from 5 point scale to 10 point scale
df["Rating"] *= 10.0 / 5.0
queue.put(df)
def chicago_df(queue):
"""Preprocess and augment Chicago faces to data/. Returns pandas dataframe"""
chicago_df = pd.read_excel("CFD Version 2.0/CFD 2.0 Norming Data and Codebook.xlsx", skiprows=4)
chicago_df = chicago_df[["Target", "Attractive"]]
for dir in os.listdir("CFD Version 2.0/CFD 2.0 Images"):
if dir == ".DS_Store":
continue
for face in os.listdir("CFD Version 2.0/CFD 2.0 Images/{0}".format(dir)):
# Neutral faces
if face.endswith("N.jpg"):
# Is one face detected
try:
preprocess.resize(
"CFD Version 2.0/CFD 2.0 Images/{0}/{1}".format(dir, face), "data/{0}.jpg".format(dir)
)
preprocess.hflip("data/{0}.jpg".format(dir), "data/{0}-F.jpg".format(dir))
preprocess.add_noise("data/{0}.jpg".format(dir), "data/{0}-N.jpg".format(dir))
except:
chicago_df = chicago_df[chicago_df.Target != dir]
flipped_df = chicago_df.copy()
noisy_df = chicago_df.copy()
flipped_df["Target"] = flipped_df["Target"] + "-F.jpg"
noisy_df["Target"] = noisy_df["Target"] + "-N.jpg"
chicago_df["Target"] = chicago_df["Target"] + ".jpg"
df = pd.concat([chicago_df, flipped_df, noisy_df], ignore_index=True)
# Rename Target -> Face and Attractive -> Rating
df.columns = ["Face", "Rating"]
# Convert from 7 point scale to 10 point scale
df["Rating"] *= 10.0 / 7.0
queue.put(df)
def models_df(queue):
"""Preprocess and augment models.com faces to data/. Returns pandas dataframe"""
imgs = []
for i in range(1, 216):
url = "http://models.com/newfaces/page/{0}".format(i)
page = urllib2.urlopen(url).read()
soup = BeautifulSoup(page, "lxml")
for tag in soup.findAll("img", {"class": "attachment-square"}):
src = "http:{0}".format(tag["src"])
base = uuid.uuid4().hex
filename = base + ".jpg"
urllib.urlretrieve(src, "data/{0}".format(filename))
try:
preprocess.resize("data/{0}".format(filename), "data/{0}".format(filename))
preprocess.hflip("data/{0}".format(filename), "data/{0}-F.jpg".format(base))
preprocess.add_noise("data/{0}".format(filename), "data/{0}-N.jpg".format(base))
except:
os.remove("data/{0}".format(filename))
continue
imgs.append({"Face": filename, "Rating": 10})
imgs.append({"Face": "{0}-F.jpg".format(base), "Rating": 10})
imgs.append({"Face": "{0}-N.jpg".format(base), "Rating": 10})
df = pd.DataFrame(imgs)
queue.put(df)
def eccv_df(queue):
"""Preprocess and augment Gray et al. dataset to data/. Returns pandas dataframe"""
root = ET.parse("eccv2010_beauty_data/hotornot_face_all.xml").getroot()
childs = []
for child in root:
filename = os.path.split(child.attrib["filename"])[-1]
base = os.path.splitext(filename)[0]
try:
preprocess.resize(
"eccv2010_beauty_data/{0}".format(child.attrib["filename"]),
"data/{0}".format(base + ".jpg"),
crop=False,
)
preprocess.hflip("data/{0}".format(filename), "data/{0}-F.jpg".format(base))
preprocess.add_noise("data/{0}".format(filename), "data/{0}-N.jpg".format(base))
except:
continue
childs.append([base + "-F.jpg", float(child.attrib["score"])])
childs.append([base + "-N.jpg", float(child.attrib["score"])])
childs.append([base + ".jpg", float(child.attrib["score"])])
df = pd.DataFrame(childs, columns=["Face", "Rating"])
df["Rating"] += 4
df["Rating"] *= 10.0 / 8.0
queue.put(df)
def train(gamma, double_q, n_step_q, exp_fraction, final_eps, kp_type,
colour_input, patch_sizes, lsp_layers, batch_size, num_iters,
learning_starts, train_freq, kpt_encoder_type, kpt_cnn_channels,
agent_size, learning_rate, max_grad_norm, mask_threshold, tau,
window_size, ckpts_prefix, ckpt_load_dir, vis_load, test_every,
mp_num_steps, img_size, replay_buffer_size, seed, noise_type, _run):
model_init_start = time.time()
process_seed = seed + hvd.local_rank()
# init Gym environments
train_env = make_env(mode="train", seed=process_seed)
if hvd.local_rank() == 0: # eval only on 1 node (horovod)
eval_env = make_env(mode="eval", seed=20 * (process_seed + 1))
n_actions = train_env.action_space.n
# build models
vision_model_dict = build_vision_model()
agent_model_dict = build_agent_model(n_actions=n_actions,
kpt_cnn_channels=kpt_cnn_channels)
target_agent_model_dict = build_agent_model(
n_actions=n_actions, kpt_cnn_channels=kpt_cnn_channels)
# Horovod: adjust learning rate based on number of GPUs.
optimizer = get_optimizer(learning_rate=learning_rate * hvd.size())
# setting up ckpts for all the modules
query_ckpt, attn_ckpt, pos_enc_ckpt, node_enc_ckpt, \
scene_ckpt, kpt_enc_ckpt = None, None, None, None, None, None
policy_ckpt = tf.train.Checkpoint(optimizer=optimizer,
model=agent_model_dict["agent_net"])
kpt_enc_ckpt = tf.train.Checkpoint(optimizer=optimizer,
model=agent_model_dict["kpt_encoder"])
if kpt_encoder_type == "gnn":
node_enc_ckpt = tf.train.Checkpoint(optimizer=optimizer,
model=agent_model_dict["node_enc"])
pos_enc_ckpt = tf.train.Checkpoint(optimizer=optimizer,
model=agent_model_dict["pos_net"])
# load pre-trained vision module
vision_model_dict = load_vision_model(vision_model_dict, kp_type,
colour_input, batch_size, lsp_layers,
patch_sizes, ckpt_load_dir, vis_load)
if hvd.local_rank() == 0:
print("initializing models and env took %4.5f s" %
(time.time() - model_init_start))
def train_step(inputs):
# Minimize the TD error on a batch sampled from replay buffer.
with tf.GradientTape() as tape:
step_loss, extra = q_learning(
vision_model_dict, agent_model_dict, target_agent_model_dict,
inputs, batch_size, kp_type, agent_size, mask_threshold,
patch_sizes, kpt_encoder_type, mp_num_steps, img_size,
lsp_layers, window_size, gamma, double_q, n_step_q)
w_update_start = time.time()
# Horovod: add Horovod Distributed GradientTape.
tape = hvd.DistributedGradientTape(tape)
# collecting trainable params of all modules
params = []
for agent_model in agent_model_dict.values():
params = params + list(agent_model.trainable_variables)
# compute grads
grads = tape.gradient(step_loss, params)
# apply grad clipping
grads, global_norm = tf.clip_by_global_norm(grads,
clip_norm=max_grad_norm)
# update agent
optimizer.apply_gradients(zip(grads, params))
# print("grad comp + weight updates take %4.5f" % (time.time() - w_update_start))
return step_loss, extra
# load weights using var assignment
source_vars, target_vars = update_target_networks(agent_model_dict,
target_agent_model_dict,
tau)
# init replay buffer
data_spec = (specs.TensorSpec([84, 84, 3], tf.int32, 'obs_tm1'),
specs.TensorSpec([1], tf.int32, 'a_tm1'),
specs.TensorSpec([1], tf.float32, 'r_tm1'),
specs.TensorSpec([2], tf.float32, 'begin_end'))
# each process has it's own smaller reply_buffer
replay_buffer = EpisodicReplayBuffer(
capacity=int(replay_buffer_size),
buffer_size=8,
dataset_drop_remainder=False,
data_spec=data_spec,
begin_episode_fn=lambda x: bool(x[3][0, 0]),
end_episode_fn=lambda x: bool(x[3][0, 1]))
# create tf.Dataset object from replay_buffer and sample
rb_ds = replay_buffer.as_dataset(sample_batch_size=batch_size,
num_steps=window_size + n_step_q + 1)
# dataset iterator sampling trajectories from replay_buffer
episode_ids = replay_buffer.create_episode_ids(1)
rb_ds = rb_ds.prefetch(buffer_size=AUTOTUNE)
rb_iterator = iter(rb_ds)
episode_rewards = [0.0]
obs = train_env.reset()
reset = False
# lists for logging exp results
eps = 0.1
episode_timestep = 0
exploration = exploration_policy(num_iters, exp_fraction, final_eps)
avg_td_error = 0.0
# init lstm_agent state
c_tm1 = tf.Variable(tf.zeros((1, agent_size)), trainable=False)
h_tm1 = tf.Variable(tf.zeros((1, agent_size)), trainable=False)
best_eval_score = -float("inf")
# TRAINING LOOP
for t in range(int(num_iters)):
# Horovod: broadcast initial variable states from rank 0 to all other processes.
# This is necessary to ensure consistent initialization of all workers when
# training is started with random weights or restored from a checkpoint.
if t == 0:
hvd.broadcast_variables(source_vars, root_rank=0)
hvd.broadcast_variables(target_vars, root_rank=0)
hvd.broadcast_variables(optimizer.variables(), root_rank=0)
online_step_start = time.time()
# convert obs to float and scale to 0-1
obs_float = np.asarray(obs[None, :, :, :], dtype=np.float32) / 255.0
# sometimes add distractors
if noise_type is not "none":
obs_float = add_noise(obs_float[0, :, :, :], noise_type)
obs_float = obs_float[None, :, :, :]
# exploration
update_eps = tf.constant(exploration.value(t))
# compute forward pass of input obs over vision + attention modules
bottom_up_masks, encoder_features, kpt_centers = vision_forward_pass(
obs_float, vision_model_dict, lsp_layers, kp_type, patch_sizes,
img_size)
# compute keypoint encodings
bottom_up_features = encode_keypoints(
bottom_up_masks,
encoder_features,
kpt_centers,
mask_threshold,
kp_type,
kpt_encoder_type,
mp_num_steps,
q_learn=False,
pos_net=agent_model_dict.get("pos_net"),
node_encoder=agent_model_dict.get("node_enc"),
kpt_encoder=agent_model_dict.get(
"kpt_encoder")) # passes None if not available
# agent step
action, h_t, c_t = agent_model_dict["agent_net"].step(
bottom_up_features, [h_tm1, c_tm1],
update_eps,
training=True,
stochastic=True)
# env step
new_obs, rew, done, _ = train_env.step(action)
episode_timestep = episode_timestep + 1
episode_rewards[-1] += rew
# store transitions in replay buffer
store_exp_start = time.time()
# making data_tuple compatible for add_batch() method
obs = img_as_ubyte(np.array(obs_float[0, :, :, :], dtype=float))
action = np.array(action, dtype=np.int32)
rew = np.array(rew, ndmin=1, dtype=np.float32)
end = np.array(done, ndmin=1, dtype=np.float32)
begin = np.array(reset, ndmin=1, dtype=np.float32)
begin_end = np.concatenate((begin, end), axis=0)
# converting from
values = (obs, action, rew, begin_end)
values_batched = tf.nest.map_structure(lambda b: tf.stack([b]), values)
# add batch of transitions of episode_ids to replay_buffer
episode_ids = replay_buffer.add_batch(values_batched, episode_ids)
obs = new_obs
h_tm1 = h_t
c_tm1 = c_t
reset = False
# episode termination
if done:
# saving cummulative returns at end of episode
print("Episode Return: %3.3f" % (episode_rewards[-1]))
print(episode_ids.numpy(), update_eps.numpy())
obs = train_env.reset()
episode_timestep = 0
# reset lstm state at episode end
c_tm1 = tf.Variable(tf.zeros((1, agent_size)), trainable=False)
h_tm1 = tf.Variable(tf.zeros((1, agent_size)), trainable=False)
episode_rewards.append(0.0)
reset = True
# Q_LEARNING UPDATES BEGIN
if t > learning_starts and t % train_freq == 0:
batch_q_start = time.time()
# sample a batch of trajectories from replay_buffer for recurrent-dqn
inputs = next(rb_iterator)
step_loss, extra = train_step(inputs)
step_loss = hvd.allreduce(step_loss)
# soft-update target networks
update_start = time.time()
source_vars, target_vars = update_target_networks(
agent_model_dict, target_agent_model_dict, tau)
# print("Target network updates take %4.5f" % (time.time() - update_start))
td_error = tf.reduce_mean(hvd.allreduce(extra.td_error), axis=0)
if hvd.local_rank() == 0:
print(
"Iteration: %5d Step loss: %4.4f, TD_error: %3.4f took %4.5f s"
% (t, step_loss, td_error, time.time() - batch_q_start))
# logging step losses to sacred
add_sacred_log("train.t",
int((t - learning_starts) / train_freq), _run)
add_sacred_log("train.step_loss", float(step_loss), _run)
add_sacred_log("train.step_td_error", float(td_error), _run)
avg_td_error = avg_td_error + np.abs(td_error)
# VALIDATION/CKPT
if t > learning_starts and t % test_every == 0:
# trigger evaluation run on only 1 node
if hvd.local_rank() == 0:
eval_start = time.time()
mean_ep_rew, var_ep_rew, _, _ = eval_step(
eval_env, vision_model_dict, agent_model_dict)
avg_td_error = avg_td_error / float(
(t - learning_starts) / train_freq)
print(
"Evaluation after: %5d steps avg_ep_return: %4.5f running_avg_td_error: %4.5f took %4.5f s"
% (t, mean_ep_rew, avg_td_error, time.time() - eval_start))
# logging avg. episodic rewards to sacred
add_sacred_log("test.t", int(
(t - learning_starts) / train_freq), _run)
add_sacred_log("test.mean_ep_return", float(mean_ep_rew), _run)
add_sacred_log("test.var_ep_return", float(var_ep_rew), _run)
add_sacred_log("test.avg_td_error", float(avg_td_error), _run)
avg_td_error = 0.0
# ckpt model based on eval-run scores
if mean_ep_rew > 0.95 * best_eval_score:
best_eval_score = mean_ep_rew
# Horovod: save checkpoints only on worker 0 to prevent other workers from
# corrupting it.
policy_ckpt.save(ckpts_prefix + '_agent_net')
kpt_enc_ckpt.save(ckpts_prefix + '_kpt_encoder')
if kpt_encoder_type == "gnn":
node_enc_ckpt.save(ckpts_prefix + '_node_enc')
pos_enc_ckpt.save(ckpts_prefix + '_pos_net')
if hvd.local_rank() == 0:
print("Training complete!!!")
def eval_step(eval_env, vision_model_dict, agent_model_dict, eval_eps,
max_eval_ep, agent_size, lsp_layers, kp_type, mask_threshold,
patch_sizes, img_size, kpt_encoder_type, noise_type,
mp_num_steps):
# Run max_eval_ep number of episodes using greedy-policy inferred
# from q-function and compute avg. episodic reward
eval_ep_rewards = [0.0]
obs = eval_env.reset()
reset = True
num_ep = 0
eval_c_tm1 = tf.Variable(tf.zeros((1, agent_size)), trainable=False)
eval_h_tm1 = tf.Variable(tf.zeros((1, agent_size)), trainable=False)
while num_ep < max_eval_ep:
obs_float = np.asarray(obs[None, :, :, :], dtype=np.float32) / 255.0
# sometimes add distractors
if noise_type != "none":
obs_float = add_noise(obs_float[0, :, :, :], noise_type)
obs_float = obs_float[None, :, :, :]
# vision-module forward pass
bottom_up_maps, encoder_features, kpt_centers = vision_forward_pass(
tf.constant(obs_float), vision_model_dict, lsp_layers, kp_type,
patch_sizes, img_size)
# compute keypoint encodings
bottom_up_features = encode_keypoints(
bottom_up_maps,
encoder_features,
kpt_centers,
mask_threshold,
kp_type,
kpt_encoder_type,
mp_num_steps,
q_learn=False,
pos_net=agent_model_dict.get("pos_net"),
node_encoder=agent_model_dict.get("node_enc"),
kpt_encoder=agent_model_dict.get(
"kpt_encoder")) # passes None if not available
# agent step
action, eval_h_t, eval_c_t = agent_model_dict["agent_net"].step(
bottom_up_features, [eval_h_tm1, eval_c_tm1],
eval_eps,
training=False,
stochastic=True)
# env step
new_obs, rew, done, _ = eval_env.step(action)
eval_ep_rewards[-1] += rew
obs = new_obs
eval_h_tm1, eval_c_tm1 = eval_h_t, eval_c_t
# episode termination
if done:
obs = eval_env.reset()
# reset lstm cell state at episode end
eval_c_tm1 = tf.Variable(tf.zeros((1, agent_size)),
trainable=False)
eval_h_tm1 = tf.Variable(tf.zeros((1, agent_size)),
trainable=False)
num_ep = num_ep + 1
# if hvd.local_rank() == 0:
# print(eval_ep_rewards[-1])
eval_ep_rewards.append(0.0)
reset = True
# log episodic return stats
avg_eval_ep_return = np.mean(np.array(eval_ep_rewards[0:-1]), axis=0)
std_ep_return = np.std(np.array(eval_ep_rewards[0:-1]), axis=0)
min_ep_return = np.amin(np.array(eval_ep_rewards[0:-1]), axis=0)
max_ep_return = np.amax(np.array(eval_ep_rewards[0:-1]), axis=0)
return avg_eval_ep_return, std_ep_return, min_ep_return, max_ep_return
def sample_datasets(hyperparams):
print("Getting data...", end=" ")
sys.stdout.flush()
seed = hyperparams['seed']
n_inputs = hyperparams['n_inputs']
# Number of examples to sample from each dataset
num_examples = hyperparams['num_examples']
# Number of synthetic examples to sample from each dataset
num_synthetic_examples = hyperparams['num_synthetic_examples']
# Number of bad examples to sample from each dataset
sweeps = []
sweeps_labels = []
if num_examples != 0:
for i, data_file in enumerate(hyperparams['datasets']):
temp_data = np.load("../../data_training/" + data_file + ".npz")['sweeps']
# np.random.seed(seed + i)
# np.random.shuffle(temp_data)
temp_data = temp_data[0:temp_data.shape[0] if num_examples > temp_data.shape[0]
else num_examples]
# Remove offsets
temp_data[:, 256:512] -= np.mean(temp_data[:, 256:256 + 32], axis=1, keepdims=True)
sweeps.append(temp_data)
try:
temp_data_labels = np.load("../../data_training/" + data_file + "_labels.npz")['labels']
except:
print("Labels not found for {}".format(data_file))
temp_data_labels = np.zeros((temp_data.shape[0], 3))
sweeps_labels.append(temp_data_labels)
sweeps_labels = np.concatenate(sweeps_labels, axis=0)
sweeps = np.concatenate(sweeps, axis=0)
# Add 4 zeros after each sweep -- first zero is a flag indicating whether the following
# physical parameters (ne, Vp, Te) are included in the loss function calculation. They are
# not included for (real) sweeps because they have not been analyzed yet.
# The remain 3 zeros are the physical parameters specified above.
sweeps = np.concatenate([sweeps, np.zeros((sweeps.shape[0], 4))], axis=1)
sweeps = np.concatenate([sweeps, sweeps_labels], axis=1)
print("Real examples: {}...".format(sweeps.shape[0]), end=" ")
sys.stdout.flush()
if num_synthetic_examples != 0:
sweeps_synthetic = []
for i, data_file in enumerate(hyperparams['datasets_synthetic']):
temp_data = np.load("../../data_synthetic/" + data_file + ".npz")['sweeps']
# np.random.seed(seed + i + 1000)
# np.random.shuffle(temp_data)
temp_data = temp_data[0:temp_data.shape[0]
if num_synthetic_examples > temp_data.shape[0]
else num_synthetic_examples]
temp_data[:, 0:n_inputs * 2] = preprocess.add_noise(temp_data[:, 0:n_inputs * 2],
hyperparams, epoch=0)
temp_data[:, 0:n_inputs * 2] = preprocess.add_offset(temp_data[:, 0:n_inputs * 2],
hyperparams, epoch=0)
sweeps_synthetic.append(temp_data)
sweeps_synthetic = np.concatenate(sweeps_synthetic, axis=0)
# Insert flag indicating that these are not bad sweeps (they're good).
# sweeps_synthetic = np.insert(sweeps_synthetic, n_inputs * 2 + 1, 0, axis=1)
sweeps_synthetic = np.concatenate([sweeps_synthetic,
np.zeros((sweeps_synthetic.shape[0], 3))], axis=1)
print("Synthetic examples: {}...".format(sweeps_synthetic.shape[0]), end=" ")
sys.stdout.flush()
if len(sweeps) != 0:
sweeps = np.concatenate([sweeps, sweeps_synthetic])
else:
sweeps = sweeps_synthetic
del sweeps_synthetic
# Find the voltage sweep and current means and peak-to-peaks so the model is easier to train.
vsweep_mean = np.full(hyperparams['n_inputs'], np.mean(sweeps[:, 0:n_inputs]))
vsweep_ptp = np.full(hyperparams['n_inputs'], np.ptp(sweeps[:, 0:n_inputs]))
current_mean = np.full(hyperparams['n_inputs'], np.mean(sweeps[:, n_inputs:n_inputs * 2]))
current_ptp = np.full(hyperparams['n_inputs'], np.ptp(sweeps[:, n_inputs:n_inputs * 2]))
# Combine the two so we have a nice neat X, y, and scalings tuple returned by the function.
data_mean = np.concatenate((vsweep_mean, current_mean))
data_ptp = np.concatenate((vsweep_ptp, current_ptp))
# Voltage and current sweeps are already concatenated.
# Centering and scaling the input so that it's easier to train.
sweeps[:, 0:n_inputs * 2] = (sweeps[:, 0:n_inputs * 2] - data_mean) / data_ptp
data_train, data_test, data_valid = preprocess.shuffle_split_data(sweeps, hyperparams)
print("Done.")
return data_train, data_test, data_valid, data_mean, data_ptp
