def discriminator(x):
_ = x
with O.argscope(O.fc, nonlin=O.tanh):
_ = O.fc('fc1', _, 500)
_ = O.fc('fc3', _, 1)
logits = _
return logits
def make_network(env):
with env.create_network() as net:
dpc = env.create_dpcontroller()
with dpc.activate():
def inputs():
h, w, c = 28, 28, 1
img = O.placeholder('img', shape=(None, h, w, c))
return [img]
def forward(img):
_ = img
_ = O.conv2d('conv1',
_,
16, (3, 3),
padding='SAME',
nonlin=O.identity)
_ = O.batch_norm('bn1', _)
_ = O.relu(_)
_ = O.pooling2d('pool1', _, kernel=2)
_ = O.conv2d('conv2',
_,
32, (3, 3),
padding='SAME',
nonlin=O.identity)
_ = O.batch_norm('bn2', _)
_ = O.relu(_)
_ = O.pooling2d('pool2', _, kernel=2)
dpc.add_output(_, name='feature')
dpc.set_input_maker(inputs).set_forward_func(forward)
_ = dpc.outputs['feature']
_ = O.fc('fc1', _, 64)
_ = O.fc('fc2', _, 10)
prob = O.softmax(_, name='prob')
pred = _.argmax(axis=1).astype('int32', name='pred')
net.add_output(prob)
net.add_output(pred)
if env.phase is env.Phase.TRAIN:
label = O.placeholder('label', shape=(None, ), dtype='int32')
loss = O.sparse_softmax_cross_entropy_with_logits(
logits=_, labels=label).mean()
loss = O.identity(loss, name='loss')
net.set_loss(loss)
accuracy = O.eq(label, pred).astype('float32').mean()
error = 1. - accuracy
summary.scalar('accuracy', accuracy)
summary.scalar('error', error)
summary.inference.scalar('loss', loss)
summary.inference.scalar('accuracy', accuracy)
summary.inference.scalar('error', error)
def phi_fc(feature):
_ = feature
_ = O.fc('fc0',
_,
512,
nonlin=functools.partial(O.leaky_relu, alpha=0.01))
q_pred = O.fc('fcq', _, get_player_nr_actions())
q_max = q_pred.max(axis=1)
q_argmax = q_pred.argmax(axis=1)
return q_pred, q_max, q_argmax
def forward(x):
_ = x
_ = O.fc('fcp1', _, 512, nonlin=O.relu)
_ = O.fc('fcp2', _, 256, nonlin=O.relu)
dpc.add_output(_, name='feature_p')
_ = x
_ = O.fc('fcv1', _, 512, nonlin=O.relu)
_ = O.fc('fcv2', _, 256, nonlin=O.relu)
dpc.add_output(_, name='feature_v')
def make_network(env):
with env.create_network() as net:
nr_classes = get_env('dataset.nr_classes')
conv_bn_relu = functools.partial(O.conv2d, nonlin=O.bn_relu)
conv2d = conv_bn_relu
dpc = env.create_dpcontroller()
with dpc.activate():
def inputs():
h, w, c = 32, 32, 3
img = O.placeholder('img', shape=(None, h, w, c))
return [img]
def forward(img):
_ = img
_ = conv2d('conv1.1', _, 16, (3, 3), padding='SAME')
_ = conv2d('conv1.2', _, 16, (3, 3), padding='SAME')
_ = O.pooling2d('pool1', _, kernel=3, stride=2)
_ = conv2d('conv2.1', _, 32, (3, 3), padding='SAME')
_ = conv2d('conv2.2', _, 32, (3, 3), padding='SAME')
_ = O.pooling2d('pool2', _, kernel=3, stride=2)
_ = conv2d('conv3.1', _, 64, (3, 3), padding='VALID')
_ = conv2d('conv3.2', _, 64, (3, 3), padding='VALID')
_ = conv2d('conv3.3', _, 64, (3, 3), padding='VALID')
dpc.add_output(_, name='feature')
dpc.set_input_maker(inputs).set_forward_func(forward)
_ = dpc.outputs['feature']
_ = O.fc('fc1', _, 128, nonlin=O.relu)
_ = O.fc('fc2', _, 64, nonlin=O.relu)
_ = O.fc('linear', _, nr_classes)
prob = O.softmax(_, name='prob')
pred = _.argmax(axis=1).astype('int32', name='pred')
net.add_output(prob)
net.add_output(pred)
if env.phase is env.Phase.TRAIN:
label = O.placeholder('label', shape=(None, ), dtype='int32')
loss = O.sparse_softmax_cross_entropy_with_logits(logits=_, labels=label).mean()
loss = O.identity(loss, name='loss')
net.set_loss(loss)
accuracy = O.eq(label, pred).astype('float32').mean()
error = 1. - accuracy
summary.scalar('accuracy', accuracy)
summary.scalar('error', error)
summary.inference.scalar('loss', loss)
summary.inference.scalar('accuracy', accuracy)
summary.inference.scalar('error', error)
def forward(img):
g_batch_size = get_env('trainer.batch_size') if env.phase is env.Phase.TRAIN else 1
z = O.as_varnode(tf.random_normal([g_batch_size, code_length]))
with env.variable_scope(GANGraphKeys.GENERATOR_VARIABLES):
_ = z
with O.argscope(O.fc, nonlin=O.tanh):
_ = O.fc('fc1', _, 500)
_ = O.fc('fc3', _, 784, nonlin=O.sigmoid)
x_given_z = _.reshape(-1, 28, 28, 1)
def discriminator(x):
_ = x
with O.argscope(O.fc, nonlin=O.tanh):
_ = O.fc('fc1', _, 500)
_ = O.fc('fc3', _, 1)
logits = _
return logits
if is_train:
with env.variable_scope(GANGraphKeys.DISCRIMINATOR_VARIABLES):
logits_real = discriminator(img).flatten()
score_real = O.sigmoid(logits_real)
with env.variable_scope(GANGraphKeys.DISCRIMINATOR_VARIABLES, reuse=is_train):
logits_fake = discriminator(x_given_z).flatten()
score_fake = O.sigmoid(logits_fake)
if is_train:
# build loss
with env.variable_scope('loss'):
d_loss_real = O.sigmoid_cross_entropy_with_logits(
logits=logits_real, labels=O.ones_like(logits_real)).mean()
d_loss_fake = O.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=O.zeros_like(logits_fake)).mean()
g_loss = O.sigmoid_cross_entropy_with_logits(
logits=logits_fake, labels=O.ones_like(logits_fake)).mean()
d_acc_real = (score_real > 0.5).astype('float32').mean()
d_acc_fake = (score_fake < 0.5).astype('float32').mean()
g_accuracy = (score_fake > 0.5).astype('float32').mean()
d_accuracy = .5 * (d_acc_real + d_acc_fake)
d_loss = .5 * (d_loss_real + d_loss_fake)
dpc.add_output(d_loss, name='d_loss', reduce_method='sum')
dpc.add_output(d_accuracy, name='d_accuracy', reduce_method='sum')
dpc.add_output(d_acc_real, name='d_acc_real', reduce_method='sum')
dpc.add_output(d_acc_fake, name='d_acc_fake', reduce_method='sum')
dpc.add_output(g_loss, name='g_loss', reduce_method='sum')
dpc.add_output(g_accuracy, name='g_accuracy', reduce_method='sum')
dpc.add_output(x_given_z, name='output')
dpc.add_output(score_fake, name='score')
def generator(z):
w_init = O.truncated_normal_initializer(stddev=0.02)
with O.argscope(O.conv2d, O.deconv2d, kernel=4, stride=2, W=w_init),\
O.argscope(O.fc, W=w_init):
_ = z
_ = O.fc('fc1', _, 1024, nonlin=O.bn_relu)
_ = O.fc('fc2', _, 128 * 7 * 7, nonlin=O.bn_relu)
_ = O.reshape(_, [-1, 7, 7, 128])
_ = O.deconv2d('deconv1', _, 64, nonlin=O.bn_relu)
_ = O.deconv2d('deconv2', _, 1)
_ = O.sigmoid(_, 'out')
return _
def discriminator(img):
w_init = O.truncated_normal_initializer(stddev=0.02)
with O.argscope(O.conv2d, O.deconv2d, kernel=4, stride=2, W=w_init),\
O.argscope(O.fc, W=w_init),\
O.argscope(O.leaky_relu, alpha=0.2):
_ = img
_ = O.conv2d('conv1', _, 64, nonlin=O.leaky_relu)
_ = O.conv2d('conv2', _, 128, nonlin=O.bn_nonlin)
_ = O.leaky_relu(_)
_ = O.fc('fc1', _, 1024, nonlin=O.bn_nonlin)
_ = O.leaky_relu(_)
_ = O.fc('fct', _, 1)
return _
def discriminator(x, name, reuse):
with env.variable_scope(GANGraphKeys.DISCRIMINATOR_VARIABLES,
reuse=reuse):
with env.variable_scope(name):
z = encoder(x, nonlin=bn_leaky_relu)
logit = O.fc('fccls', z, 1)
return logit
def discriminator(img):
w_init = O.truncated_normal_initializer(stddev=0.02)
with O.argscope(O.conv2d, O.deconv2d, kernel=4, stride=2, W=w_init),\
O.argscope(O.fc, W=w_init),\
O.argscope(O.leaky_relu, alpha=0.2):
_ = img
_ = O.conv2d('conv1', _, 64, nonlin=O.leaky_relu)
_ = O.conv2d('conv2', _, 128, nonlin=O.bn_nonlin)
_ = O.leaky_relu(_)
_ = O.fc('fc1', _, 1024, nonlin=O.bn_nonlin)
_ = O.leaky_relu(_)
with env.variable_scope('score'):
logits = O.fc('fct', _, 1)
with env.variable_scope('code'):
_ = O.fc('fc1', _, 128, nonlin=O.bn_nonlin)
_ = O.leaky_relu(_)
code = O.fc('fc2', _, zc_distrib.param_size)
return logits, code
def forward(img):
_ = img
_ = O.conv2d('conv1',
_,
4, (3, 3),
stride=2,
padding='SAME',
nonlin=O.relu)
# shape = (14, 14)
_ = O.conv2d('conv2',
_,
8, (3, 3),
stride=2,
padding='SAME',
nonlin=O.relu)
# shape = (7, 7)
_ = O.fc('fc', _, 392)
_ = _.reshape([-1, 7, 7, 8])
_ = O.deconv2d('deconv1',
_,
4, (3, 3),
stride=2,
padding='SAME',
nonlin=O.relu)
# shape = (14, 14)
_ = O.deconv2d('deconv2',
_,
1, (3, 3),
stride=2,
padding='SAME',
nonlin=O.sigmoid)
# shape = (28, 28)
out = _
loss = O.raw_cross_entropy_prob('raw_loss', out, img)
loss = O.get_pn_balanced_loss('loss', loss, img)
dpc.add_output(out, name='output')
dpc.add_output(loss, name='loss', reduce_method='sum')
def forward(x):
if is_reconstruct or env.phase is env.Phase.TRAIN:
with env.variable_scope('encoder'):
_ = x
_ = O.fc('fc1', _, 500, nonlin=O.tanh)
_ = O.fc('fc2', _, 500, nonlin=O.tanh)
mu = O.fc('fc3_mu', _, code_length)
log_var = O.fc('fc3_sigma', _, code_length)
var = O.exp(log_var)
std = O.sqrt(var)
epsilon = O.random_normal([x.shape[0], code_length])
z_given_x = mu + std * epsilon
else:
z_given_x = O.random_normal([1, code_length])
with env.variable_scope('decoder'):
_ = z_given_x
_ = O.fc('fc1', _, 500, nonlin=O.tanh)
_ = O.fc('fc2', _, 500, nonlin=O.tanh)
_ = O.fc('fc3', _, 784, nonlin=O.sigmoid)
_ = _.reshape(-1, h, w, c)
x_given_z = _
if env.phase is env.Phase.TRAIN:
with env.variable_scope('loss'):
content_loss = O.raw_cross_entropy_prob(
'raw_content', x_given_z.flatten2(), x.flatten2())
content_loss = content_loss.sum(axis=1).mean(
name='content')
# distrib_loss = 0.5 * (O.sqr(mu) + O.sqr(std) - 2. * O.log(std + 1e-8) - 1.0).sum(axis=1)
distrib_loss = -0.5 * (1. + log_var - O.sqr(mu) -
var).sum(axis=1)
distrib_loss = distrib_loss.mean(name='distrib')
loss = content_loss + distrib_loss
dpc.add_output(loss, name='loss', reduce_method='sum')
dpc.add_output(x_given_z, name='output')
def make_network(env):
is_train = env.phase is env.Phase.TRAIN
if is_train:
slave_devices = env.slave_devices
env.set_slave_devices([])
with env.create_network() as net:
h, w, c = get_input_shape()
dpc = env.create_dpcontroller()
with dpc.activate():
def inputs():
state = O.placeholder('state', shape=(None, h, w, c))
return [state]
def forward(x):
_ = x / 255.0
with O.argscope(O.conv2d, nonlin=O.relu):
_ = O.conv2d('conv0', _, 32, 5)
_ = O.max_pooling2d('pool0', _, 2)
_ = O.conv2d('conv1', _, 32, 5)
_ = O.max_pooling2d('pool1', _, 2)
_ = O.conv2d('conv2', _, 64, 4)
_ = O.max_pooling2d('pool2', _, 2)
_ = O.conv2d('conv3', _, 64, 3)
dpc.add_output(_, name='feature')
dpc.set_input_maker(inputs).set_forward_func(forward)
_ = dpc.outputs['feature']
_ = O.fc('fc0', _, 512, nonlin=O.p_relu)
policy = O.fc('fc_policy', _, get_player_nr_actions())
value = O.fc('fc_value', _, 1)
expf = O.scalar('explore_factor', 1, trainable=False)
policy_explore = O.softmax(policy * expf, name='policy_explore')
policy = O.softmax(policy, name='policy')
value = value.remove_axis(1, name='value')
net.add_output(policy_explore, name='policy_explore')
net.add_output(policy, name='policy')
net.add_output(value, name='value')
if is_train:
action = O.placeholder('action', shape=(None, ), dtype='int64')
future_reward = O.placeholder('future_reward', shape=(None, ))
log_policy = O.log(policy + 1e-6)
log_pi_a_given_s = (
log_policy *
O.one_hot(action, get_player_nr_actions())).sum(axis=1)
advantage = (future_reward -
O.zero_grad(value)).rename('advantage')
policy_cost = (log_pi_a_given_s *
advantage).mean(name='policy_cost')
xentropy_cost = (-policy *
log_policy).sum(axis=1).mean(name='xentropy_cost')
value_loss = O.raw_l2_loss('raw_value_loss', future_reward,
value).mean(name='value_loss')
entropy_beta = O.scalar('entropy_beta', 0.01, trainable=False)
loss = O.add_n(
[-policy_cost, -xentropy_cost * entropy_beta, value_loss],
name='loss')
net.set_loss(loss)
for v in [
policy_cost, xentropy_cost, value_loss,
value.mean(name='predict_value'),
advantage.rms(name='rms_advantage'), loss
]:
summary.scalar(v)
if is_train:
env.set_slave_devices(slave_devices)
def forward(img=None):
encoder = O.BasicLSTMCell(256)
decoder = O.BasicLSTMCell(256)
batch_size = img.shape[0] if is_train else 1
canvas = O.zeros(shape=O.canonize_sym_shape([batch_size, h, w, c]), dtype='float32')
enc_state = encoder.zero_state(batch_size, dtype='float32')
dec_state = decoder.zero_state(batch_size, dtype='float32')
enc_h, dec_h = enc_state[1], dec_state[1]
def encode(x, state, reuse):
with env.variable_scope('read_encoder', reuse=reuse):
return encoder(x, state)
def decode(x, state, reuse):
with env.variable_scope('write_decoder', reuse=reuse):
return decoder(x, state)
all_sqr_mus, all_vars, all_log_vars = 0., 0., 0.
for step in range(nr_glimpse):
reuse = (step != 0)
if is_reconstruct or env.phase is env.Phase.TRAIN:
img_hat = draw_opr.image_diff(img, canvas) # eq. 3
# Note: here the input should be dec_h
with env.variable_scope('read', reuse=reuse):
read_param = O.fc('fc_param', dec_h, 5)
with env.name_scope('read_step{}'.format(step)):
cx, cy, delta, var, gamma = draw_opr.split_att_params(h, w, att_dim, read_param)
read_inp = O.concat([img, img_hat], axis=3) # of shape: batch_size x h x w x (2c)
read_out = draw_opr.att_read(att_dim, read_inp, cx, cy, delta, var) # eq. 4
enc_inp = O.concat([gamma * read_out.flatten2(), dec_h], axis=1)
enc_h, enc_state = encode(enc_inp, enc_state, reuse) # eq. 5
with env.variable_scope('sample', reuse=reuse):
_ = enc_h
sample_mu = O.fc('fc_mu', _, code_length)
sample_log_var = O.fc('fc_sigma', _, code_length)
with env.name_scope('sample_step{}'.format(step)):
sample_var = O.exp(sample_log_var)
sample_std = O.sqrt(sample_var)
sample_epsilon = O.random_normal([batch_size, code_length])
z = sample_mu + sample_std * sample_epsilon # eq. 6
# accumulate for losses
all_sqr_mus += sample_mu ** 2.
all_vars += sample_var
all_log_vars += sample_log_var
else:
z = O.random_normal([1, code_length])
# z = O.callback_injector(z)
dec_h, dec_state = decode(z, dec_state, reuse) # eq. 7
with env.variable_scope('write', reuse=reuse):
write_param = O.fc('fc_param', dec_h, 5)
write_in = O.fc('fc', dec_h, (att_dim * att_dim * c)).reshape(-1, att_dim, att_dim, c)
with env.name_scope('write_step{}'.format(step)):
cx, cy, delta, var, gamma = draw_opr.split_att_params(h, w, att_dim, write_param)
write_out = draw_opr.att_write(h, w, write_in, cx, cy, delta, var) # eq. 8
canvas += write_out
if env.phase is env.Phase.TEST:
dpc.add_output(O.sigmoid(canvas), name='canvas_step{}'.format(step))
canvas = O.sigmoid(canvas)
if env.phase is env.Phase.TRAIN:
with env.variable_scope('loss'):
img, canvas = img.flatten2(), canvas.flatten2()
content_loss = O.raw_cross_entropy_prob('raw_content', canvas, img)
content_loss = content_loss.sum(axis=1).mean(name='content')
# distrib_loss = 0.5 * (O.sqr(mu) + O.sqr(std) - 2. * O.log(std + 1e-8) - 1.0).sum(axis=1)
distrib_loss = -0.5 * (float(nr_glimpse) + all_log_vars - all_sqr_mus - all_vars).sum(axis=1)
distrib_loss = distrib_loss.mean(name='distrib')
summary.scalar('content_loss', content_loss)
summary.scalar('distrib_loss', distrib_loss)
loss = content_loss + distrib_loss
dpc.add_output(loss, name='loss', reduce_method='sum')
dpc.add_output(canvas, name='output')
def forward_fc(feature, action):
action = O.one_hot(action, get_player_nr_actions())
_ = O.concat([feature.flatten2(), action], axis=1)
_ = O.fc('fc0', _, 512, nonlin=O.p_relu)
reward = O.fc('fc_reward', _, 1)
return reward
def make_network(env):
with env.create_network() as net:
state = O.placeholder('state', shape=(None, ) + get_input_shape())
logits = O.fc('fc', state, get_action_shape())
net.add_output(logits, name='policy')
def make_network(env):
use_linear_vr = get_env('trpo.use_linear_vr')
with env.create_network() as net:
net.dist = O.distrib.GaussianDistribution('policy',
size=get_action_shape()[0],
fixed_std=False)
if use_linear_vr:
from tartist.app.rl.utils.math import LinearValueRegressor
net.value_regressor = LinearValueRegressor()
state = O.placeholder('state', shape=(None, ) + get_input_shape())
# state = O.moving_average(state)
# state = O.clip_by_value(state, -10, 10)
batch_size = state.shape[0]
# We have to define variable scope here for later optimization.
with env.variable_scope('policy'):
_ = state
with O.argscope(O.fc):
_ = O.fc('fc1', _, 64, nonlin=O.relu)
_ = O.fc('fc2', _, 64, nonlin=O.relu)
mu = O.fc('fc_mu', _, net.dist.sample_size, nonlin=O.tanh)
logstd = O.variable(
'logstd',
O.truncated_normal_initializer(stddev=0.01),
shape=(net.dist.sample_size, ),
trainable=True)
logstd = O.tile(logstd.add_axis(0), [batch_size, 1])
theta = O.concat([mu, logstd], axis=1)
policy = net.dist.sample(batch_size=batch_size,
theta=theta,
process_theta=True)
policy = O.clip_by_value(policy, -1, 1)
net.add_output(theta, name='theta')
net.add_output(policy, name='policy')
if env.phase == env.Phase.TRAIN:
theta_old = O.placeholder('theta_old',
shape=(None, net.dist.param_size))
action = O.placeholder('action',
shape=(None, net.dist.sample_size))
advantage = O.placeholder('advantage', shape=(None, ))
log_prob = net.dist.log_likelihood(action,
theta,
process_theta=True)
log_prob_old = net.dist.log_likelihood(action,
theta_old,
process_theta=True)
# Importance sampling of surrogate loss (L in paper).
ratio = O.exp(log_prob - log_prob_old)
policy_loss = -O.reduce_mean(ratio * advantage)
kl = net.dist.kl(theta_p=theta_old,
theta_q=theta,
process_theta=True).mean()
kl_self = net.dist.kl(theta_p=O.zero_grad(theta),
theta_q=theta,
process_theta=True).mean()
entropy = net.dist.entropy(theta, process_theta=True).mean()
net.add_output(policy_loss, name='policy_loss')
net.add_output(kl, name='kl')
net.add_output(kl_self, name='kl_self')
summary.scalar('policy_entropy',
entropy,
collections=[rl.train.ACGraphKeys.POLICY_SUMMARIES])
if not use_linear_vr:
with env.variable_scope('value'):
value = O.fc('fcv', state, 1)
net.add_output(value, name='value')
if env.phase == env.Phase.TRAIN:
value_label = O.placeholder('value_label', shape=(None, ))
value_loss = O.raw_l2_loss('raw_value_loss', value,
value_label).mean(name='value_loss')
net.add_output(value_loss, name='value_loss')
def make_network(env):
with env.create_network() as net:
n = 2
nr_classes = get_env('dataset.nr_classes')
conv2d = functools.partial(O.conv2d,
kernel=3,
use_bias=False,
padding='SAME')
conv_bn_relu = functools.partial(conv2d, nonlin=O.bn_relu)
dpc = env.create_dpcontroller()
with dpc.activate():
def inputs():
h, w, c = 32, 32, 3
img = O.placeholder('img', shape=(None, h, w, c))
return [img]
def residual(name, x, first=False, inc_dim=False):
in_channel = x.static_shape[3]
out_channel = in_channel
stride = 1
if inc_dim:
out_channel = in_channel * 2
stride = 2
with env.variable_scope(name):
_ = x if first else O.bn_relu(x)
_ = conv_bn_relu('conv1', _, out_channel, stride=stride)
_ = conv2d('conv2', _, out_channel)
if inc_dim:
x = O.pooling2d('pool', x, kernel=2)
x = O.pad(x, [[0, 0], [0, 0], [0, 0],
[in_channel // 2, in_channel // 2]])
print(name, x.static_shape)
_ = _ + x
return _
def forward(img):
_ = img / 128.0 - 1.0
_ = conv_bn_relu('conv0', _, 16)
_ = residual('res1.0', _, first=True)
for i in range(1, n):
_ = residual('res1.{}'.format(i), _)
_ = residual('res2.0', _, inc_dim=True)
for i in range(1, n):
_ = residual('res2.{}'.format(i), _)
_ = residual('res3.0', _, inc_dim=True)
for i in range(1, n):
_ = residual('res3.{}'.format(i), _)
_ = O.batch_norm('bn_last', _)
_ = O.relu(_)
_ = _.mean(axis=[1, 2]) # global avg pool
dpc.add_output(_, name='feature')
dpc.set_input_maker(inputs).set_forward_func(forward)
_ = dpc.outputs['feature']
_ = O.fc('linear', _, nr_classes)
prob = O.softmax(_, name='prob')
pred = _.argmax(axis=1).astype('int32', name='pred')
net.add_output(prob)
net.add_output(pred)
if env.phase is env.Phase.TRAIN:
label = O.placeholder('label', shape=(None, ), dtype='int32')
loss = O.sparse_softmax_cross_entropy_with_logits(
logits=_, labels=label).mean()
loss = O.identity(loss, name='loss')
net.set_loss(loss)
accuracy = O.eq(label, pred).astype('float32').mean()
error = 1. - accuracy
summary.scalar('accuracy', accuracy)
summary.scalar('error', error)
summary.inference.scalar('loss', loss)
summary.inference.scalar('accuracy', accuracy)
summary.inference.scalar('error', error)
def make_network(env):
is_train = env.phase is env.Phase.TRAIN
# device control: always use master device only for training session
if is_train:
slave_devices = env.slave_devices
env.set_slave_devices([])
with env.create_network() as net:
input_length, = get_input_shape()
action_length, = get_action_shape()
dpc = env.create_dpcontroller()
with dpc.activate():
def inputs():
state = O.placeholder('state', shape=(None, input_length))
return [state]
# forward policy network and value network separately (actor-critic)
def forward(x):
_ = x
_ = O.fc('fcp1', _, 512, nonlin=O.relu)
_ = O.fc('fcp2', _, 256, nonlin=O.relu)
dpc.add_output(_, name='feature_p')
_ = x
_ = O.fc('fcv1', _, 512, nonlin=O.relu)
_ = O.fc('fcv2', _, 256, nonlin=O.relu)
dpc.add_output(_, name='feature_v')
dpc.set_input_maker(inputs).set_forward_func(forward)
_ = dpc.outputs['feature_p']
# mu and std, assuming spherical covariance
policy_mu = O.fc('fc_policy_mu', _, action_length)
# In this example, we do not use variance. instead, we use fixed value.
# policy_var = O.fc('fc_policy_var', _, 1, nonlin=O.softplus)
# policy_var = O.tile(policy_var, [1, action_length], name='policy_var')
# policy_std = O.sqrt(policy_var, name='policy_std')
actor_space = get_env('a3c.actor_space')
nr_bins = actor_space.shape[1]
# Instead of using normal distribution, we use Laplacian distribution for policy.
# And also, we are sampling from a truncated Laplacian distribution (only care the value in the
# action space). To simplify the computation, we discretize the action space.
actor_space = O.constant(actor_space)
actor_space = O.tile(actor_space.add_axis(0), [policy_mu.shape[0], 1, 1])
policy_mu3 = O.tile(policy_mu.add_axis(2), [1, 1, nr_bins])
# policy_std3 = O.tile(policy_std.add_axis(2), [1, 1, nr_bins])
# logits = O.abs(actor_space - policy_mu3) / (policy_std3 + 1e-2)
# Here, we force the std of the policy to be 1.
logits_explore = -O.abs(actor_space - policy_mu3)
policy_explore = O.softmax(logits_explore)
# Clip the policy for output
action_range = get_action_range()
action_range = tuple(map(O.constant, action_range))
action_range = tuple(map(lambda x: O.tile(x.add_axis(0), [policy_mu.shape[0], 1]), action_range))
policy_output = O.clip_by_value(policy_mu, *action_range)
_ = dpc.outputs['feature_v']
value = O.fc('fc_value', _, 1)
value = value.remove_axis(1, name='value')
# Note that, here the policy_explore is a discrete policy,
# and policy is actually the continuous one.
net.add_output(policy_explore, name='policy_explore')
net.add_output(policy_output, name='policy')
net.add_output(value, name='value')
if is_train:
action = O.placeholder('action', shape=(None, action_length), dtype='int64')
future_reward = O.placeholder('future_reward', shape=(None, ))
entropy_beta = O.scalar('entropy_beta', 0.1, trainable=False)
# Since we discretized the action space, use cross entropy here.
log_policy = O.log(policy_explore + 1e-4)
log_pi_a_given_s = (log_policy * O.one_hot(action, nr_bins)).sum(axis=2).sum(axis=1)
advantage = (future_reward - O.zero_grad(value)).rename('advantage')
# Important trick: using only positive advantage to perform gradient assent. This stabilizes the training.
advantage = advantage * O.zero_grad((advantage > 0.).astype('float32'))
policy_loss = O.identity(-(log_pi_a_given_s * advantage).mean(), name='policy_loss')
# As mentioned, there is no trainable variance.
# entropy_loss = O.identity(-entropy_beta * (policy_std ** 2.).sum(axis=1).mean(), name='entropy_loss')
value_loss = O.raw_smooth_l1_loss('raw_value_loss', future_reward, value).mean(name='value_loss')
loss = O.add_n([policy_cost, value_loss], name='loss')
net.set_loss(loss)
for v in [policy_cost, value_loss,
value.mean(name='predict_value'), advantage.rms(name='rms_advantage'), loss]:
summary.scalar(v)
if is_train:
env.set_slave_devices(slave_devices)
def make_network(env):
with env.create_network() as net:
net.dist = O.distrib.GaussianDistribution('policy',
size=get_action_shape()[0],
fixed_std=False)
state = O.placeholder('state', shape=(None, ) + get_input_shape())
batch_size = state.shape[0]
# We have to define variable scope here for later optimization.
with env.variable_scope('policy'):
_ = state
_ = O.fc('fc1', _, 64, nonlin=O.relu)
_ = O.fc('fc2', _, 64, nonlin=O.relu)
mu = O.fc('fc_mu', _, net.dist.sample_size, nonlin=O.tanh)
logstd = O.variable('logstd',
O.truncated_normal_initializer(stddev=0.01),
shape=(net.dist.sample_size, ),
trainable=True)
logstd = O.tile(logstd.add_axis(0), [batch_size, 1])
theta = O.concat([mu, logstd], axis=1)
policy = net.dist.sample(batch_size=batch_size,
theta=theta,
process_theta=True)
policy = O.clip_by_value(policy, -1, 1)
net.add_output(theta, name='theta')
net.add_output(policy, name='policy')
if env.phase == env.Phase.TRAIN:
theta_old = O.placeholder('theta_old',
shape=(None, net.dist.param_size))
action = O.placeholder('action',
shape=(None, net.dist.sample_size))
advantage = O.placeholder('advantage', shape=(None, ))
entropy_beta = O.scalar('entropy_beta', g.entropy_beta)
log_prob = net.dist.log_likelihood(action,
theta,
process_theta=True)
log_prob_old = net.dist.log_likelihood(action,
theta_old,
process_theta=True)
ratio = O.exp(log_prob - log_prob_old)
epsilon = get_env('ppo.epsilon')
surr1 = ratio * advantage # surrogate from conservative policy iteration
surr2 = O.clip_by_value(ratio, 1.0 - epsilon,
1.0 + epsilon) * advantage
policy_loss = -O.reduce_mean(O.min(
surr1, surr2)) # PPO's pessimistic surrogate (L^CLIP)
entropy = net.dist.entropy(theta, process_theta=True).mean()
entropy_loss = -entropy_beta * entropy
net.add_output(policy_loss, name='policy_loss')
net.add_output(entropy_loss, name='entropy_loss')
summary.scalar('policy_entropy', entropy)
with env.variable_scope('value'):
_ = state
_ = O.fc('fc1', _, 64, nonlin=O.relu)
_ = O.fc('fc2', _, 64, nonlin=O.relu)
value = O.fc('fcv', _, 1)
value = value.remove_axis(1)
net.add_output(value, name='value')
if env.phase == env.Phase.TRAIN:
value_label = O.placeholder('value_label', shape=(None, ))
value_old = O.placeholder('value_old', shape=(None, ))
value_surr1 = O.raw_l2_loss('raw_value_loss_surr1', value,
value_label)
value_clipped = value_old + O.clip_by_value(
value - value_old, -epsilon, epsilon)
value_surr2 = O.raw_l2_loss('raw_value_loss_surr2', value_clipped,
value_label)
value_loss = O.reduce_mean(O.max(value_surr1, value_surr2))
net.add_output(value_loss, name='value_loss')
if env.phase == env.Phase.TRAIN:
loss = O.identity(policy_loss + entropy_loss + value_loss,
name='total_loss')
net.set_loss(loss)
