How to update model parameters with accumulated gradients?

Question

I\'m using TensorFlow to build a deep learning model. And new to TensorFlow.

Due to some reason, my model has limited batch size, then this limited batch-size will m

Answer 1

I had the same problem and just figured it out.

First get symbolic gradients, then define accumulated gradients as tf.Variables. (It seems that tf.global_variables_initializer() has to be run before defining grads_accum. I got errors otherwise, not sure why.)

tvars = tf.trainable_variables()
optimizer = tf.train.GradientDescentOptimizer(lr)
grads = tf.gradients(cost, tvars)

# initialize
tf.local_variables_initializer().run()
tf.global_variables_initializer().run()

grads_accum = [tf.Variable(tf.zeros_like(v)) for v in grads] 
update_op = optimizer.apply_gradients(zip(grads_accum, tvars)) 

In training you can accumulate gradients (saved in gradients_accum) at each batch, and update the model after running the 64-th batch:

feed_dict = dict()
for i, _grads in enumerate(gradients_accum):
    feed_dict[grads_accum[i]] = _grads
sess.run(fetches=[update_op], feed_dict=feed_dict) 

You can refer to tensorflow/tensorflow/python/training/optimizer_test.py for example usage, particularly this function: testGradientsAsVariables().

Hope it helps.