563207ae6bd9a8565243b04dcd92af9c33bac524,deepchem/models/keras_model.py,KerasModel,fit_generator,#KerasModel#Any#Any#Any#Any#Any#Any#Any#,246

Before Change

        vars = variables
      grads = tape.gradient(batch_loss, vars)
      self._tf_optimizer.apply_gradients(zip(grads, vars))
      self._global_step.assign_add(1)
      current_step = self._global_step.numpy()

      avg_loss += batch_loss

After Change

    if loss is None:
      loss = self._loss_fn
    var_key = None
    if variables is not None:
      var_key = tuple(v.experimental_ref() for v in variables)

      // The optimizer creates internal variables the first time apply_gradients()
      // is called for a new set of variables.  If that happens inside a function
      // annotated with tf.function it throws an exception, so call it once here.

      zero_grads = [tf.zeros(v.shape) for v in variables]
      self._tf_optimizer.apply_gradients(zip(zero_grads, variables))
    if var_key not in self._gradient_fn_for_vars:
      self._gradient_fn_for_vars[var_key] = self._create_gradient_fn(variables)
    apply_gradient_for_batch = self._gradient_fn_for_vars[var_key]
    time1 = time.time()

Instances