def Inference(self):
"""Computes y = w^T x + b. Returns y and x, as outputs and inputs."""
with tf.variable_scope('inference'):
x = tf.placeholder(dtype=tf.float32, name='input')
r = tf.random.stateless_uniform([3],
seed=py_utils.GenerateStepSeedPair(
self.params, self.theta.global_step))
y = tf.reduce_sum((self.vars.w + r) * x) + self.vars.b
return {'default': ({'output': y}, {'input': x})}
def Inference(self):
"""Computes y = w^T x + b. Returns y and x, as outputs and inputs."""
# Add a dummy file def to the collection
filename = tf.convert_to_tensor(
'dummy.txt', tf.dtypes.string, name='asset_filepath')
tf.compat.v1.add_to_collection(tf.compat.v1.GraphKeys.ASSET_FILEPATHS,
filename)
with tf.name_scope('inference'):
x = tf.placeholder(dtype=tf.float32, name='input')
r = tf.random.stateless_uniform([3],
seed=py_utils.GenerateStepSeedPair(
self.params))
y = tf.reduce_sum((self.vars.w + r) * x) + self.vars.b
return {'default': ({'output': y}, {'input': x})}
def _GetWeight(self, theta):
p = self.params
filter_w = theta.w
# First normalize filter_w over the temporal dimension here.
filter_w = tf.nn.softmax(filter_w / p.temperature, axis=0)
# Add dropconnect on the weights for regularization.
if p.dropconnect_prob > 0.0 and not self.do_eval:
if p.deterministic_dropout:
filter_w = py_utils.DeterministicDropout(
filter_w, 1.0 - p.dropconnect_prob,
py_utils.GenerateStepSeedPair(p))
else:
filter_w = tf.nn.dropout(
filter_w, rate=p.dropconnect_prob, seed=p.random_seed)
# Tie the parameters of every subsequent number of weight_tiling_factor
# channels.
filter_w = tf.tile(filter_w, [1, 1, p.weight_tiling_factor, 1])
return filter_w
