本文整理匯總了Python中utils.add_grads_and_vars方法的典型用法代碼示例。如果您正苦於以下問題：Python utils.add_grads_and_vars方法的具體用法？Python utils.add_grads_and_vars怎麽用？Python utils.add_grads_and_vars使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils的用法示例。
在下文中一共展示了utils.add_grads_and_vars方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: get_rebar_gradient
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import add_grads_and_vars [as 別名]
def get_rebar_gradient(self):
"""Get the rebar gradient."""
hardELBO, nvil_gradient, logQHard = self._create_hard_elbo()
if self.hparams.quadratic:
gumbel_cv, _ = self._create_gumbel_control_variate_quadratic(logQHard)
else:
gumbel_cv, _ = self._create_gumbel_control_variate(logQHard)
f_grads = self.optimizer_class.compute_gradients(tf.reduce_mean(-nvil_gradient))
eta = {}
h_grads, eta_statistics = self.multiply_by_eta_per_layer(
self.optimizer_class.compute_gradients(tf.reduce_mean(gumbel_cv)),
eta)
model_grads = U.add_grads_and_vars(f_grads, h_grads)
total_grads = model_grads
# Construct the variance objective
variance_objective = tf.reduce_mean(tf.square(U.vectorize(model_grads, set_none_to_zero=True)))
debug = { 'ELBO': hardELBO,
'etas': eta_statistics,
'variance_objective': variance_objective,
}
return total_grads, debug, variance_objective
###
# Create varaints
### 示例2: get_dynamic_rebar_gradient
# 需要導入模塊: import utils [as 別名]
# 或者: from utils import add_grads_and_vars [as 別名]
def get_dynamic_rebar_gradient(self):
"""Get the dynamic rebar gradient (t, eta optimized)."""
tiled_pre_temperature = tf.tile([self.pre_temperature_variable],
[self.batch_size])
temperature = tf.exp(tiled_pre_temperature)
hardELBO, nvil_gradient, logQHard = self._create_hard_elbo()
if self.hparams.quadratic:
gumbel_cv, extra = self._create_gumbel_control_variate_quadratic(logQHard, temperature=temperature)
else:
gumbel_cv, extra = self._create_gumbel_control_variate(logQHard, temperature=temperature)
f_grads = self.optimizer_class.compute_gradients(tf.reduce_mean(-nvil_gradient))
eta = {}
h_grads, eta_statistics = self.multiply_by_eta_per_layer(
self.optimizer_class.compute_gradients(tf.reduce_mean(gumbel_cv)),
eta)
model_grads = U.add_grads_and_vars(f_grads, h_grads)
total_grads = model_grads
# Construct the variance objective
g = U.vectorize(model_grads, set_none_to_zero=True)
self.maintain_ema_ops.append(self.ema.apply([g]))
gbar = 0 #tf.stop_gradient(self.ema.average(g))
variance_objective = tf.reduce_mean(tf.square(g - gbar))
reinf_g_t = 0
if self.hparams.quadratic:
for layer in xrange(self.hparams.n_layer):
gumbel_learning_signal, _ = extra[layer]
df_dt = tf.gradients(gumbel_learning_signal, tiled_pre_temperature)[0]
reinf_g_t_i, _ = self.multiply_by_eta_per_layer(
self.optimizer_class.compute_gradients(tf.reduce_mean(tf.stop_gradient(df_dt) * logQHard[layer])),
eta)
reinf_g_t += U.vectorize(reinf_g_t_i, set_none_to_zero=True)
reparam = tf.add_n([reparam_i for _, reparam_i in extra])
else:
gumbel_learning_signal, reparam = extra
df_dt = tf.gradients(gumbel_learning_signal, tiled_pre_temperature)[0]
reinf_g_t, _ = self.multiply_by_eta_per_layer(
self.optimizer_class.compute_gradients(tf.reduce_mean(tf.stop_gradient(df_dt) * tf.add_n(logQHard))),
eta)
reinf_g_t = U.vectorize(reinf_g_t, set_none_to_zero=True)
reparam_g, _ = self.multiply_by_eta_per_layer(
self.optimizer_class.compute_gradients(tf.reduce_mean(reparam)),
eta)
reparam_g = U.vectorize(reparam_g, set_none_to_zero=True)
reparam_g_t = tf.gradients(tf.reduce_mean(2*tf.stop_gradient(g - gbar)*reparam_g), self.pre_temperature_variable)[0]
variance_objective_grad = tf.reduce_mean(2*(g - gbar)*reinf_g_t) + reparam_g_t
debug = { 'ELBO': hardELBO,
'etas': eta_statistics,
'variance_objective': variance_objective,
}
return total_grads, debug, variance_objective, variance_objective_grad