def _thresh_fn(variable, mask):
thresh_fn = percentile_thresh_fn(1 -
target_sparsities[variable.op.name],
target_iterations,
update_steps,
thresh_lower_scale=1)
return thresh_fn(variable, mask)
def train_op_fn(loss, params):
prob_thresh = _prob_piecewise()
optimizer = tf.train.MomentumOptimizer(learning_rate=_learning_rate_inverse, momentum=0.9)
weights = tf.get_collection(tf.GraphKeys.WEIGHTS)
thresh_fn = percentile_thresh_fn(
_make_target_sparsity(0.0, 0.60, 0.15, depth_multiplier),
target_iterations=200000,
update_steps=2000)
train_op = make_dns_train_op(
loss, optimizer=optimizer, prob_thresh=prob_thresh, thresh_fn_or_scale=thresh_fn,
variables=weights, global_step=tf.train.get_or_create_global_step())
return train_op
def train_op_fn(loss, params):
prob_thresh = tf.train.inverse_time_decay(
1.0, tf.train.get_or_create_global_step(),
decay_steps=100, decay_rate=0.2,
staircase=True, name='prob_thresh')
optimizer = tf.train.MomentumOptimizer(
learning_rate=_learning_rate, momentum=0.9)
weights = tf.get_collection(tf.GraphKeys.WEIGHTS)
thresh_fn = percentile_thresh_fn(
target_sparsity,
target_iterations=15000,
update_steps=500)
train_op = make_dns_train_op(
loss, optimizer=optimizer, prob_thresh=prob_thresh, thresh_fn_or_scale=thresh_fn,
variables=weights, global_step=tf.train.get_or_create_global_step())
return train_op