(x, weight_decay, num_datasets)
return dataset_logits
Classifies the batch x into one of the training datasets.
Classifies the batch x into one of the training datasets.
Classifies the batch x into one of the training datasets .
def dataset_classifier(x, weight_decay, num_datasets):
  """Classifies the batch x into one of the training datasets."""
  deep_set_embeddings = deep_set_encoder(x, weight_decay)  # [1, 64].

  with tf.variable_scope('dataset_fc', reuse=tf.AUTO_REUSE):
    w_fc = weight_variable([64, num_datasets], weight_decay=weight_decay)
    b_fc = bias_variable([num_datasets])
  dataset_logits = tf.matmul(deep_set_embeddings, w_fc) + b_fc
  return dataset_logits
def dataset_classifier ( x , weight_decay , num_datasets ) : deep_set_embeddings = deep_set_encoder ( x , weight_decay ) # [1, 64]. with tf . variable_scope ( 'dataset_fc' , reuse = tf . AUTO_REUSE ) : w_fc = weight_variable ( [ 64 , num_datasets ] , weight_decay = weight_decay ) b_fc = bias_variable ( [ num_datasets ] ) dataset_logits = tf . matmul ( deep_set_embeddings , w_fc ) + b_fc return dataset_logits