def create_tf_parameters(self, name='test_tf_param'):
with tf.variable_scope(name):
a = tf.get_variable(shape=[3, 4], dtype=tf.float32, name='var_1')
b = tf.get_variable(shape=[3, 4], dtype=tf.bool, name='var_2')
conf = DictConfig(required_key_dict=Foo.required_key_dict,
config_dict=dict(var1=1, var2=0.01))
param = ParametersWithTensorflowVariable(
tf_var_list=[a, b],
rest_parameters=dict(var3='sss'),
name=name,
source_config=conf,
require_snapshot=True,
to_ph_parameter_dict=dict(
var1=tf.placeholder(shape=(), dtype=tf.int32)))
return param, locals()
def create_ph(self, name):
with tf.variable_scope(name):
a = tf.get_variable(shape=[3, 4], dtype=tf.float32, name='var_1')
conf = DictConfig(required_key_dict=Foo.required_key_dict,
config_dict=dict(var1=1, var2=0.01))
param = ParametersWithTensorflowVariable(
tf_var_list=[a],
rest_parameters=dict(var3='sss'),
name=name,
source_config=conf,
require_snapshot=True,
to_ph_parameter_dict=dict(
var1=tf.placeholder(shape=(), dtype=tf.int32)))
param.init()
a = PlaceholderInput(parameters=param, inputs=None)
return a, locals()
def create_dict_config(self):
a = DictConfig(required_key_dict=Foo.required_key_dict,
config_dict=dict(var1=1, var2=0.1),
cls_name='Foo')
return a, locals()