## Issue

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.

## Solution

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[0]) for i, gv in enumerate(gvs)]
## Define the training step (part with variable value update)
train_step = opt.apply_gradients([(accum_vars[i], gv[1]) for i, gv in enumerate(gvs)])
```

This first part basically adds new `variables`

and `ops`

to your graph which will allow you to

- Accumulate the gradient with ops
`accum_ops`

in (the list of) variable`accum_vars`

- Update the model weights with ops
`train_step`

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

**This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **