def main_trainer(hparams):
print_params(hparams)
full_exp = Experiment(name=hparams.tt_name + '_overall',
debug=hparams.debug,
autosave=False,
description=hparams.tt_description,
save_dir=hparams.tt_save_path)
full_exp.add_argparse_meta(hparams)
# fit model
val_scores, train_scores = [], []
best_acc = 0
best_loss = 0
best_trial_nb = 0
for trial_nb in range(hparams.nb_trials):
exp = Experiment(name=hparams.tt_name,
debug=hparams.debug,
autosave=False,
description=hparams.tt_description,
save_dir=hparams.tt_save_path)
exp.add_argparse_meta(hparams)
data = SequentialReadingsData(window_size=hparams.time_steps,
data_path=hparams.data_path)
val_loss, val_acc, history = fit_feedforward(hparams, exp, trial_nb,
data)
log_history(history.history, exp)
exp.add_metric_row({
'final_val_acc': val_acc,
'final_train_acc': val_loss
})
exp.save()
full_exp.add_metric_row({
'val_acc': val_acc,
'val_loss': val_loss,
'trial_nb': trial_nb
})
# save model when we have a better one
if val_acc > best_acc:
best_acc = val_acc
best_loss = val_loss
best_trial_nb = trial_nb
val_scores.append(val_acc)
mean_val_acc = np.mean(val_scores)
full_exp.add_metric_row({
'final_val_acc': mean_val_acc,
'best_val_loss': best_loss,
'best_val_acc': best_acc,
'best_trial_nb': best_trial_nb
})
full_exp.save()
def train(hparams):
# init exp and track all the parameters from the HyperOptArgumentParser
exp = Experiment(name='dense_model', save_dir='/some/path', autosave=False)
exp.add_argparse_meta(hparams)
# define tensorflow graph
x = tf.placeholder(dtype=tf.int32, name='x')
y = tf.placeholder(dtype=tf.int32, name='y')
out = x * y
sess = tf.Session()
# Run the tf op
for train_step in range(0, 100):
output = sess.run(out, feed_dict={x: hparams.x_val, y: hparams.y_val})
exp.add_metric_row({'fake_err': output})
# save exp when we're done
exp.save()
def main_trainer(hparams):
print_params(hparams)
exp = Experiment(name=hparams.tt_name,
debug=hparams.debug,
autosave=False,
description=hparams.tt_description,
save_dir=hparams.tt_save_path)
exp.add_argparse_meta(hparams)
# fit model
val_scores = []
best_score = 0
for trial_nb in range(hparams.nb_trials):
data = dataset_loader.IndividualSequencesData(
hparams.data_path, y_labels=hparams.y_labels.split(','))
X, Y, lengths = flatten_data(data.train_x_y)
# fit
model = hmm.GaussianHMM(n_components=hparams.nb_components,
n_iter=hparams.nb_hmm_iters)
model.fit(X, lengths)
val_X, val_Y, lengths = flatten_data(data.val_x_y)
Y_hat = model.predict(val_X, lengths)
val_score = np.equal(Y_hat, val_Y).sum() / float(len(Y_hat))
# save model
if val_score > best_score:
best_score = val_score
save_model(model, hparams, exp, trial_nb)
val_scores.append(val_score)
exp.add_metric_row({'val_acc': val_score, 'trail_nb': trial_nb})
mean_val_acc = np.mean(val_scores)
exp.add_metric_row({'final_val_acc': mean_val_acc})
exp.save()
def main_trainer(hparams):
print_params(hparams)
exp = Experiment(name=hparams.tt_name,
debug=hparams.debug,
autosave=False,
description=hparams.tt_description,
save_dir=hparams.tt_save_path)
exp.add_argparse_meta(hparams)
# init data loader
# fit model
val_scores, train_scores = [], []
best_score = 0
for trial_nb in range(hparams.nb_trials):
data = SequentialReadingsData(window_size=hparams.time_steps, data_path=hparams.data_path, flatten_x=True)
clf = RandomForestClassifier(n_estimators=hparams.nb_estimators)
clf.fit(data.train_x, data.train_y)
train_score = clf.score(data.train_x, data.train_y)
val_score = clf.score(data.val_x, data.val_y)
# save model when we have a better one
if val_score > best_score:
best_score = val_score
save_model(clf, hparams, exp, trial_nb)
train_scores.append(train_score)
val_scores.append(val_score)
exp.add_metric_row({'val_acc': val_score, 'train_acc': train_score, 'trail_nb': trial_nb})
mean_val_acc = np.mean(val_scores)
mean_train_acc = np.mean(train_scores)
exp.add_metric_row({'final_val_acc': mean_val_acc, 'final_train_acc': mean_train_acc})
exp.save()
def get_critic_train_data(succ_states, rewards, dones):
# r + Q(s, pi(s'))
Q_succ = critic_target(succ_states, actor_target(succ_states)).squeeze()
td_estimate = rewards + ((1 - dones) * hparams.discount * Q_succ)
return td_estimate.detach()
actor_opt = Adam(actor.parameters())
critic_opt = Adam(critic.parameters())
buffer = ReplayBuffer(hparams.buffer_size)
s, rews = np_to_var(env.reset()), []
for hparam in hparams.trials(5):
exp.add_argparse_meta(hparam)
for timestep in range(hparam.num_steps):
noise = Normal(
mean=Variable(torch.zeros(A)),
std=hparam.noise_factor * Variable(torch.ones(A)),
)
if timestep % 1000 == 0:
hparam.noise_factor /= 2
a = actor(s) + noise.sample()
succ, r, done, _ = env.step(a.data.numpy())
succ = np_to_var(succ)
buffer.append(Step(s, a, r, succ, done))
rews.append(r)
s = np_to_var(env.reset()) if done else succ
