I have a question similar to this one.
Because I have limited resources and I work with a deep model (VGG-16) – used to train a triplet network – I want to accumulate gradients for 128 batches of size one training example, and then propagate the error and update the weights.
It’s not clear to me how do I do this. I work with tensorflow but any implementation/pseudocode is welcome.
Let’s walk through the code proposed in one of the answers you linked to:
## Optimizer definition - nothing different from any classical example opt = tf.train.AdamOptimizer() ## Retrieve all trainable variables you defined in your graph tvs = tf.trainable_variables() ## Creation of a list of variables with the same shape as the trainable ones # initialized with 0s accum_vars = [tf.Variable(tf.zeros_like(tv.initialized_value()), trainable=False) for tv in tvs] zero_ops = [tv.assign(tf.zeros_like(tv)) for tv in accum_vars] ## Calls the compute_gradients function of the optimizer to obtain... the list of gradients gvs = opt.compute_gradients(rmse, tvs) ## Adds to each element from the list you initialized earlier with zeros its gradient (works because accum_vars and gvs are in the same order) accum_ops = [accum_vars[i].assign_add(gv) for i, gv in enumerate(gvs)] ## Define the training step (part with variable value update) train_step = opt.apply_gradients([(accum_vars[i], gv) for i, gv in enumerate(gvs)])
This first part basically adds new
ops to your graph which will allow you to
- Accumulate the gradient with ops
accum_opsin (the list of) variable
- Update the model weights with ops
Then, to use it when training, you have to follow these steps (still from the answer you linked):
## The while loop for training while ...: # Run the zero_ops to initialize it sess.run(zero_ops) # Accumulate the gradients 'n_minibatches' times in accum_vars using accum_ops for i in xrange(n_minibatches): sess.run(accum_ops, feed_dict=dict(X: Xs[i], y: ys[i])) # Run the train_step ops to update the weights based on your accumulated gradients sess.run(train_step)
Answered By – Pop