springboot-example-generator | 代码生成器 -

def train_generator():
  # for each batch, we will send 1 pair of each subject
  # and the same number of non-matching pairs
  n_batches = int(np.ceil(len(train_positives) / batch_size))
  
  while True:
    np.random.shuffle(train_positives)

    n_  

def all_candidate_sampler(true_classes, num_true, num_sampled, unique,
                          seed=None, name=None):
  """Generate the set of all classes.

  Deterministically generates and returns the set of all possible classes.
  For testing pu  

def multiplex(cond, a, b, name=None):
  """Return elements chosen from `a` or `b` depending on `cond`.

  This is similar to `np.where` and `tf.where`, but simplified to only handle
  the case of dense tensors, no optional parameters, no broadcasting