Method get_gradients

tensorflow/python/keras/optimizers.py:77–105

Returns gradients of `loss` with respect to `params`. Arguments: loss: Loss tensor. params: List of variables. Returns: List of gradient tensors. Raises: ValueError: In case any gradient cannot be computed (e.g. if gradient function not implem

(self, loss, params)

    raise NotImplementedError

  def get_gradients(self, loss, params):
    """Returns gradients of `loss` with respect to `params`.

    Arguments:
        loss: Loss tensor.
        params: List of variables.

    Returns:
        List of gradient tensors.

    Raises:
        ValueError: In case any gradient cannot be computed (e.g. if gradient
          function not implemented).
    """
    grads = K.gradients(loss, params)
    if None in grads:
      raise ValueError('An operation has `None` for gradient. '
                       'Please make sure that all of your ops have a '
                       'gradient defined (i.e. are differentiable). '
                       'Common ops without gradient: '
                       'K.argmax, K.round, K.eval.')
    if hasattr(self, 'clipnorm'):
      grads = [clip_ops.clip_by_norm(g, self.clipnorm) for g in grads]
    if hasattr(self, 'clipvalue'):
      grads = [
          clip_ops.clip_by_value(g, -self.clipvalue, self.clipvalue)
          for g in grads
      ]
    return grads

  def set_weights(self, weights):
    """Sets the weights of the optimizer, from Numpy arrays.