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

Tensorflow 2.0 Compatible Answer: In line with the weixsong's Answer mentioned above and the explanation provided in Tensorflow Website, mentioned below is the code for Accumulating Gradients in Tensorflow Version 2.0:

def train(epochs):
  for epoch in range(epochs):
    for (batch, (images, labels)) in enumerate(dataset):
       with tf.GradientTape() as tape:
        logits = mnist_model(images, training=True)
        tvs = mnist_model.trainable_variables
        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]
        loss_value = loss_object(labels, logits)

       loss_history.append(loss_value.numpy().mean())
       grads = tape.gradient(loss_value, tvs)
       #print(grads[0].shape)
       #print(accum_vars[0].shape)
       accum_ops = [accum_vars[i].assign_add(grad) for i, grad in enumerate(grads)]



    optimizer.apply_gradients(zip(grads, mnist_model.trainable_variables))
    print ('Epoch {} finished'.format(epoch))

# call the above function    
train(epochs = 3)

Complete code can be found in this Github Gist.