def create_outer_parameters(self):
"""
:param var_collections: name of collections to store the created variables.
:return: dictionary to index the created variables.
"""
for i in range(len(self.dim_hidden)):
self.outer_param_dict["conv" +
str(i)] = network_utils.get_conv_weight(
self,
i=i,
initializer=self.conv_initializer)
self.outer_param_dict["bias" +
str(i)] = network_utils.get_bias_weight(
self,
i=i,
initializer=self.bias_initializer)
[
tf.add_to_collections(extension.GraphKeys.METAPARAMETERS, hyper)
for hyper in self.outer_param_dict.values()
]
if len(self.model_param_dict) == 0 and callable(
getattr(self, "create_model_parameters", None)):
self.create_model_parameters()
return self.outer_param_dict
def create_outer_parameters(self,
var_collections=GraphKeys.METAPARAMETERS):
"""
:param var_collections: name of collections to store the created variables.
:return: dictionary to index the created variables.
"""
for i in range(len(self.dim_hidden)):
self.outer_param_dict["conv" +
str(i)] = network_utils.get_conv_weight(
self,
i=i,
initializer=self.conv_initializer)
self.outer_param_dict["bias" +
str(i)] = network_utils.get_bias_weight(
self,
i=i,
initializer=self.bias_initializer)
if self.max_pool:
self.outer_param_dict[
"w" + str(len(self.dim_hidden))] = tf.get_variable(
"w" + str(len(self.dim_hidden)),
[self.dim_hidden[-1] * 5 * 5, self.dims[-1]],
initializer=self.output_weight_initializer,
)
self.outer_param_dict["bias" +
str(len(self.dim_hidden))] = tf.get_variable(
"bias" + str(len(self.dim_hidden)),
[self.dims[-1]],
initializer=self.bias_initializer,
dtype=self.datatype,
)
else:
self.outer_param_dict["w" +
str(len(self.dim_hidden))] = tf.get_variable(
"w" + str(len(self.dim_hidden)),
[self.dim_hidden[-1], self.dims[-1]],
initializer=tf.random_normal_initializer,
)
self.outer_param_dict["bias" +
str(len(self.dim_hidden))] = tf.get_variable(
"bias" + str(len(self.dim_hidden)),
[self.dims[-1]],
initializer=self.bias_initializer,
dtype=self.datatype,
)
[
tf.add_to_collections(var_collections, hyper)
for hyper in self.outer_param_dict.values()
]
if len(self.model_param_dict) == 0 and callable(
getattr(self, "create_model_parameters", None)):
self.create_model_parameters()
return self.outer_param_dict