def Or(*wffs):
if len(wffs) == 0:
result = tf.constant(0.0)
result.doms = []
else:
cross_wffs, _ = cross_args(wffs)
label = "_OR_".join([wff.name.split(":")[0] for wff in wffs])
result = tf.identity(F_Or(cross_wffs), name=label)
result.doms = cross_wffs.doms
return result
def pred(*args):
global BIAS
crossed_args, list_of_args_in_crossed_args = cross_args(args)
result = apply_pred(*list_of_args_in_crossed_args)
if crossed_args.doms != []:
result = tf.reshape(
result, tf.concat([tf.shape(crossed_args)[:-1], [1]], axis=0))
else:
result = tf.reshape(result, (1, ))
result.doms = crossed_args.doms
BIAS = tf.divide(BIAS + .5 - tf.reduce_mean(result), 2) * BIAS_factor
return result
def fun(*args):
crossed_args, list_of_args_in_crossed_args = cross_args(args)
result = apply_fun(*list_of_args_in_crossed_args)
if crossed_args.doms != []:
result = tf.reshape(
result,
tf.concat([tf.shape(crossed_args)[:-1],
tf.shape(result)[-1:]],
axis=0))
else:
result = tf.reshape(result, (output_shape_spec, ))
result.doms = crossed_args.doms
return result
def function_grounding(*args):
crossed_args, list_of_args_in_crossed_args = cross_args(args)
result = self.func_definition(*list_of_args_in_crossed_args)
if crossed_args.doms != []:
result = tf.reshape(
result,
tf.concat(
[tf.shape(crossed_args)[:-1],
tf.shape(result)[-1:]],
axis=0))
else:
result = tf.reshape(result, (self.output_shape_spec, ))
result.doms = crossed_args.doms
return result
def predicate_grounding(*args):
# global BIAS
crossed_args, list_of_args_in_crossed_args = cross_args(args)
result = self.grounding_definition(*list_of_args_in_crossed_args)
if crossed_args.doms != []:
result = tf.reshape(
result,
tf.concat([tf.shape(crossed_args)[:-1], [1]], axis=0))
else:
result = tf.reshape(result, (1, ))
result.doms = crossed_args.doms
BIAS = get_bias()
update_bias(
tf.divide(BIAS + .5 - tf.reduce_mean(result), 2) * BIAS_factor)
return result