8ee4ce6931a92fc0865d64a1c41b84a23d1ed351,tensorflow/python/keras/layers/preprocessing/category_encoding.py,CategoryEncoding,call,#CategoryEncoding#Any#Any#,266

Before Change

    control_flow_ops.Assert(
        condition, ["Input values must be in the range 0 <= values < max_tokens"
                    " with max_tokens={}".format(out_depth)])
    if self._sparse:
      result = bincount_ops.sparse_bincount(
          inputs,
          weights=count_weights,
          minlength=out_depth,
          maxlength=out_depth,
          axis=-1,
          binary_output=binary_output)
      result = math_ops.cast(result, K.floatx())
      batch_size = array_ops.shape(result)[0]
      result = sparse_tensor.SparseTensor(
          indices=result.indices,
          values=result.values,
          dense_shape=[batch_size, out_depth])
      return result
    else:
      result = bincount_ops.bincount(
          inputs,
          weights=count_weights,
          minlength=out_depth,
          maxlength=out_depth,
          dtype=K.floatx(),
          axis=-1,
          binary_output=binary_output)
      result.set_shape(tensor_shape.TensorShape((None, out_depth)))
      return result


class _CategoryEncodingAccumulator(
    collections.namedtuple("Accumulator", ["data", "per_doc_count_dict"])):
  pass

After Change

    control_flow_ops.Assert(
        condition, ["Input values must be in the range 0 <= values < max_tokens"
                    " with max_tokens={}".format(out_depth)])
    if self.sparse:
      return sparse_bincount(inputs, out_depth, binary_output, count_weights)
    else:
      return dense_bincount(inputs, out_depth, binary_output, count_weights)


class _CategoryEncodingAccumulator(
    collections.namedtuple("Accumulator", ["data", "per_doc_count_dict"])):
  pass

Instances