GRL
GRL이 사용된다.코드
import tensorflow as tf
import numpy as np
from tensorflow.python.framework import ops
class FlipGradientBuilder(object):
def __init__(self):
self.num_calls = 0
def __call__(self, x, lr=1.0):
grad_name = "FlipGradient%d" % self.num_calls
@ops.RegisterGradient(grad_name)
def _flip_gradients(op, grad):
return [tf.negative(grad) * lr]
g = tf.get_default_graph()
with g.gradient_override_map({"Identity": grad_name}):
y = tf.identity(x) # copy for assign op
self.num_calls += 1
return y
예시
_flip_gradient = FlipGradientBuilder()
_flip_gradient.num_calls
0
_X = tf.constant(np.random.random([2,3]))
# as a feature
_W1 = tf.constant(np.random.random([3,2]))
_feature = tf.matmul(_X,_W1)
_W2 = tf.constant(np.random.random([2,5]))
_output = tf.matmul(_feature,_W2)
tf.InteractiveSession()
<tensorflow.python.client.session.InteractiveSession at 0x7f63f3675940>
_feature
<tf.Tensor 'MatMul:0' shape=(2, 2) dtype=float64>
tf.gradients(_feature, _W1)[0].eval()
array([[1.44770713, 1.44770713],
[0.30008724, 0.30008724],
[0.57832454, 0.57832454]])
grad_flip_feature = _flip_gradient(_feature, 1.0)
grad_flip_feature
<tf.Tensor 'Identity:0' shape=(2, 2) dtype=float64>
tf.gradients(grad_flip_feature, _W1)[0].eval()
array([[-1.44770713, -1.44770713],
[-0.30008724, -0.30008724],
[-0.57832454, -0.57832454]])
_flip_gradient.num_calls
1
Reference
https://github.com/TGISer/tf-dann