在tensorflow中，可训练和停止梯度之间的区别是什么

Question

I would like to know the difference between the option trainable=False and the tf.stop_gradient() . If I make the trainable option False will my optimizer not consider the variable for training? Does this option make the it a constant value throughout the training?

Answer 1

trainable=False

Here the variable value will be constant throughout the training. Optimizer won't consider this variable for training, no gradient update op.

stop_gradient

In certain situations, you want to calculate the gradient of a op with respect to some variable keeping a few other variables constant; but for other ops you may use those variables also to calculate gradient. So here you can't use trinable=False , as you need those variable for training with other ops.

stop_gradient is very useful for ops; you can selectively optimize a op with respect to select few variables while keeping other constant.

y1 = tf.stop_gradient(W1x+b1)
y2 = W2y1+b2
cost = cost_function(y2, y)
# this following op wont optimize the cost with respect to W1 and b1
train_op_w2_b2 = tf.train.MomentumOptimizer(0.001, 0.9).minimize(cost)

W1 = tf.get_variable('w1', trainable=False)
y1 = W1x+b1
y2 = W2y1+b2
cost = cost_function(y2, y)
# this following op wont optimize the cost with respect to W1
train_op = tf.train.MomentumOptimizer(0.001, 0.9).minimize(cost)