Method set_weights

tensorflow/python/keras/optimizers.py:107–135

Sets the weights of the optimizer, from Numpy arrays. Should only be called after computing the gradients (otherwise the optimizer has no weights). Arguments: weights: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of

(self, weights)

    return grads

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

    Should only be called after computing the gradients
    (otherwise the optimizer has no weights).

    Arguments:
        weights: a list of Numpy arrays. The number of arrays and their shape
          must match number of the dimensions of the weights of the optimizer
          (i.e. it should match the output of `get_weights`).

    Raises:
        ValueError: in case of incompatible weight shapes.
    """
    params = self.weights
    if len(params) != len(weights):
      raise ValueError('Length of the specified weight list (' +
                       str(len(weights)) +
                       ') does not match the number of weights '
                       'of the optimizer (' + str(len(params)) + ')')
    weight_value_tuples = []
    param_values = K.batch_get_value(params)
    for pv, p, w in zip(param_values, params, weights):
      if pv.shape != w.shape:
        raise ValueError('Optimizer weight shape ' + str(pv.shape) +
                         ' not compatible with '
                         'provided weight shape ' + str(w.shape))
      weight_value_tuples.append((p, w))
    K.batch_set_value(weight_value_tuples)

  def get_weights(self):
    """Returns the current value of the weights of the optimizer.