class Grad_policy(object):
def __init__(self, state_size, action_size, lr=0.001):
self.init = xavier_initializer()
with tf.variable_scope('supervised_policy'):
self.st = tf.placeholder(tf.float32, [None, state_size], name='st')
self.acts_prob = self.sl_policy_nn(self.st, state_size,
action_size, self.init)
self.act = tf.placeholder(tf.int32, [None], name='act')
self.reward = tf.placeholder(tf.float32, name='reward')
act_mask = tf.cast(tf.one_hot(self.act, depth=action_size),
tf.bool)
self.act_prob = tf.boolean_mask(self.acts_prob, act_mask)
self.loss = sum(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
scope='supervised_policy')) + tf.reduce_sum(
-tf.log(self.act_prob) * self.reward)
self.optimizer = AdamOptimizer(learning_rate=lr)
self.training_op = self.optimizer.minimize(self.loss)
def sl_policy_nn(self, state, state_size, action_size, init):
w1 = tf.get_variable('W1', [state_size, 512],
initializer=init,
regularizer=l2_regularizer(0.01))
b1 = tf.get_variable('b1', [512],
initializer=tf.constant_initializer(0.0))
h1 = tf.nn.relu(tf.matmul(state, w1) + b1)
w2 = tf.get_variable('w2', [512, 1024],
initializer=init,
regularizer=l2_regularizer(0.01))
b2 = tf.get_variable('b2', [1024],
initializer=tf.constant_initializer(0.0))
h2 = tf.nn.relu(tf.matmul(h1, w2) + b2)
w3 = tf.get_variable('w3', [1024, action_size],
initializer=init,
regularizer=l2_regularizer(0.01))
b3 = tf.get_variable('b3', [action_size],
initializer=tf.constant_initializer(0.0))
acts_prob = tf.nn.softmax(tf.matmul(h2, w3) + b3)
return acts_prob
def get_act_probs(self, st, sess=None):
sess = sess or tf.get_default_session()
return sess.run(self.acts_prob, {self.st: st})
def train_batch(self, st, act, reward, sess=None):
sess = sess or tf.get_default_session()
_, loss = sess.run([self.training_op, self.loss], {
self.st: st,
self.act: act,
self.reward: reward
})
return loss
def train():
classifier = get_model()
opt = AdamOptimizer(1e-5)
images_data = get_classification_data("../data/data_classification_train.json")
count = 0
print("Training started")
shuffle(images_data)
for (i, label) in images_data:
img = get_img("../pictures/pictures_classification_train/{}.png".format(i))
def get_loss():
img_vector = tf.convert_to_tensor([img], dtype=np.float32)
logits = classifier(img_vector)
entropy = sparse_softmax_cross_entropy_with_logits(labels=[label], logits=logits)
entropy = tf.gather(entropy, 0)
save_data(label, logits[0].numpy().tolist(), entropy.numpy().tolist())
return entropy
opt.minimize(get_loss)
count += 1
if (count % 1000 == 0):
classifier.save_weights(weights_path)
print("Weights saved")
classifier.save_weights(weights_path)
print("Weights saved")
model = LKM(data, additive_kernels, likelihoods)
gp_train_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope="gp_hyperparameters")
ibp_train_vars = list(set(tf.global_variables()) - set(gp_train_vars))
update_tau = model.closed_form_update_tau()
elbo = model.build_marginal_loglikelihood()
z, nll_gp_refined = model.refine()
t_test, K, K_star, K_star_star, noise = model.prepare_for_postprocess()
# train IBP parameters with Adam
adam = AdamOptimizer(0.01)
# train_ibp = adam.minimize(-elbo, var_list=ibp_train_vars)
train_ibp = adam.minimize(-elbo, var_list=ibp_train_vars)
train_gp = ScipyOptimizerInterface(-elbo,
var_list=gp_train_vars,
method='L-BFGS-B',
options={"maxiter": 10})
# refined train
train_gp_refine = ScipyOptimizerInterface(nll_gp_refined,
var_list=gp_train_vars,
method='L-BFGS-B',
options={"maxiter": 300}
)
init = tf.global_variables_initializer()
