def _split_mu_log_sigma(self, params, slice_dim):
"""Splits `params` into `mu` and `log_sigma` along `slice_dim`."""
params = tf.convert_to_tensor(params)
size = params.get_shape()[slice_dim].value
if size % 2 != 0:
raise ValueError('`params` must have an even size along dimension {}.'
.format(slice_dim))
half_size = size // 2
mu = snt.SliceByDim(
dims=[slice_dim], begin=[0], size=[half_size], name='mu')(params)
log_sigma = snt.SliceByDim(
dims=[slice_dim], begin=[half_size], size=[half_size],
name='log_sigma')(params)
return mu, log_sigma
def _get_global_encoder(self):
global_vars = self.model.game.global_vars.get_nominal()
indices = []
for i in range(len(global_vars)):
var_index = snt.SliceByDim(dims=[1],
begin=[i],
size=[1],
name=f"indices-{global_vars[i]}")
indices.append(var_index)
def global_encoder(global_input):
state_layers = []
n_local_features = 0
for j in range(len(global_vars)):
embedding_layer = self.global_embedding_layers[j]
var_indices = indices[j](global_input)
var_state = tf.squeeze(
embedding_layer(tf.to_int32(var_indices)),
axis=[1],
name=f"squeeze_embedding-{global_vars[j]}")
n_local_features += embedding_layer.embed_dim
state_layers.append(var_state)
tf.summary.histogram("indices-" + embedding_layer.module_name,
var_indices)
self._summarize_embedding(embedding_layer)
return tf.concat(state_layers, 1, name="embed_globals")
return global_encoder
def _get_node_encoder(self):
# Embed both kinds of nodes: environment objects and legal actions
object_vars = self.model.game.object_vars.get_nominal()
action_vars = self.model.game.action_vars.get_nominal()
object_indices = []
action_indices = []
for i in range(len(object_vars)):
var_index = snt.SliceByDim(dims=[1],
begin=[i],
size=[1],
name=f"indices-{object_vars[i]}")
object_indices.append(var_index)
for i in range(len(action_vars)):
var_index = snt.SliceByDim(dims=[1],
begin=[i + len(object_vars)],
size=[1],
name=f"indices-{action_vars[i]}")
action_indices.append(var_index)
def node_encoder(node_input):
state_layers = []
for j in range(len(object_vars)):
embedding_layer = self.object_embedding_layers[j]
var_indices = object_indices[j](node_input)
var_state = tf.squeeze(
embedding_layer(tf.to_int32(var_indices)),
axis=[1],
name=f"squeeze_embedding-{object_vars[j]}")
state_layers.append(var_state)
tf.summary.histogram("indices-" + embedding_layer.module_name,
var_indices)
self._summarize_embedding(embedding_layer)
for j in range(len(action_vars)):
embedding_layer = self.action_embedding_layers[j]
var_indices = action_indices[j](node_input)
var_state = tf.squeeze(
embedding_layer(tf.to_int32(var_indices)),
axis=[1],
name=f"squeeze_embedding-{action_vars[j]}")
state_layers.append(var_state)
tf.summary.histogram("indices-" + embedding_layer.module_name,
var_indices)
self._summarize_embedding(embedding_layer)
result = tf.concat(state_layers, 1, name="embed_nodes")
return result
return node_encoder