python - How to accumulate gradients in tensorflow 2.0?

Question

I'm training a model with tensorflow 2.0. The images in my training set are of different resolutions. The Model I've built can handle variable resolutions (conv layers followed by global averaging). My training set is very small and I want to use full training set in a single batch.

Since my images are of different resolutions, I can't use model.fit(). So, I'm planning to pass each sample through the network individually, accumulate the errors/gradients and then apply one optimizer step. I'm able to compute loss values, but I don't know how to accumulate the losses/gradients. How can I accumulate the losses/gradients and then apply a single optimizer step?

Code:

for i in range(num_epochs):
    print(f'Epoch: {i + 1}')
    total_loss = 0
    for j in tqdm(range(num_samples)):
        sample = samples[j]
        with tf.GradientTape as tape:
            prediction = self.model(sample)
            loss_value = self.loss_function(y_true=labels[j], y_pred=prediction)
        gradients = tape.gradient(loss_value, self.model.trainable_variables)
        self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))
        total_loss += loss_value

    epoch_loss = total_loss / num_samples
    print(f'Epoch loss: {epoch_loss}')

Answer 1

In line with the Stack Overflow Answer 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.