Recebo um TypeError ao tentar treinar uma floresta aleatória de fluxo de tensão usando TensorForestEstimator.

TypeError: Input 'input_data' of 'CountExtremelyRandomStats' Op has type float64 that does not match expected type of float32.

Eu tentei usar o Python 2.7 e o Python 3 e tentei usar o tf.cast () para colocar tudo no float32, mas isso não ajuda. Eu verifiquei o tipo de dados na execução e é float32. O problema não parece ser os dados que eu forneço (csv de todos os carros alegóricos), então não tenho certeza para onde ir a partir daqui.

Qualquer sugestão de coisas que eu possa tentar seria muito apreciada.

Código:

# Build an estimator.
def build_estimator(model_dir):
  params = tensor_forest.ForestHParams(
      num_classes=2, num_features=760,
      num_trees=FLAGS.num_trees, max_nodes=FLAGS.max_nodes)
  graph_builder_class = tensor_forest.RandomForestGraphs
  if FLAGS.use_training_loss:
    graph_builder_class = tensor_forest.TrainingLossForest
  # Use the SKCompat wrapper, which gives us a convenient way to split in-memory data into batches.
  return estimator.SKCompat(random_forest.TensorForestEstimator(params, graph_builder_class=graph_builder_class, model_dir=model_dir))


# Train and evaluate the model.
def train_and_eval():

  # load datasets
  training_set = pd.read_csv('/Users/carl/Dropbox/Docs/Python/randomforest_balanced_train.csv', dtype=np.float32, header=None)
  test_set = pd.read_csv('/Users/carl/Dropbox/Docs/Python/randomforest_balanced_test.csv', dtype=np.float32, header=None)

  print('###########')
  print(training_set.loc[:,1].dtype)  # this prints float32

  # load labels
  training_labels = pd.read_csv('/Users/carl/Dropbox/Docs/Python/randomforest_balanced_train_class.csv', dtype=np.int32, names=LABEL, header=None)
  test_labels = pd.read_csv('/Users/carl/Dropbox/Docs/Python/randomforest_balanced_test_class.csv', dtype=np.int32, names=LABEL, header=None)

  # define the path where the model will be stored - default is current directory
  model_dir = tempfile.mkdtemp() if not FLAGS.model_dir else FLAGS.model_dir
  print('model directory = %s' % model_dir)

  # build the random forest estimator
  est = build_estimator(model_dir)

  tf.cast(training_set, tf.float32) #error occurs with/without casts
  tf.cast(test_set, tf.float32)
  # train the forest to fit the training data
  est.fit(x=training_set, y=training_labels)  #this line throws the error