Sometimes, we need to average an array of gradients in deep learning model. Fortunately, Tensorflow divided models into fine-grained tensors and operations, therefore it’s not difficult to implement gradients average by using it.
Let’s see the code from github：
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with tf.variable_scope(tf.get_variable_scope()): for i in xrange(FLAGS.num_gpus): with tf.device('/gpu:%d' % i): with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope: # Dequeues one batch for the GPU image_batch, label_batch = batch_queue.dequeue() # Calculate the loss for one tower of the CIFAR model. This function # constructs the entire CIFAR model but shares the variables across # all towers. loss = tower_loss(scope, image_batch, label_batch) # Reuse variables for the next tower. tf.get_variable_scope().reuse_variables() # Retain the summaries from the final tower. summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope) # Calculate the gradients for the batch of data on this CIFAR tower. grads = opt.compute_gradients(loss) # Keep track of the gradients across all towers. tower_grads.append(grads) # We must calculate the mean of each gradient. Note that this is the # synchronization point across all towers. grads = average_gradients(tower_grads) ...... apply_gradient_op = opt.apply_gradients(grads, global_step=global_step) |
We should keep in mind that these codes will only build a static graph (the ‘grads; are references rather than values).
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def average_gradients(tower_grads): average_grads = [] for grad_and_vars in zip(*tower_grads): # Note that each grad_and_vars looks like the following: # ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN)) grads = [] for g, _ in grad_and_vars: # Add 0 dimension to the gradients to represent the tower. expanded_g = tf.expand_dims(g, 0) # Append on a 'tower' dimension which we will average over below. grads.append(expanded_g) # Average over the 'tower' dimension. grad = tf.concat(axis=0, values=grads) grad = tf.reduce_mean(grad, 0) # Keep in mind that the Variables are redundant because they are shared # across towers. So .. we will just return the first tower's pointer to # the Variable. v = grad_and_vars[0][1] grad_and_var = (grad, v) average_grads.append(grad_and_var) return average_grads |
First, we need to expand dimensions of tensor(gradient) and concatenate them. Then use reduce_mean() to do actually average operation (seems not intuitive).