Python tf.compat.v1.data.experimental.make_batched_features_dataset用法及代码示例

从Example protos 返回特征字典的Dataset。

用法

tf.compat.v1.data.experimental.make_batched_features_dataset(
    file_pattern, batch_size, features, reader=None, label_key=None,
    reader_args=None, num_epochs=None, shuffle=True, shuffle_buffer_size=10000,
    shuffle_seed=None, prefetch_buffer_size=None, reader_num_threads=None,
    parser_num_threads=None, sloppy_ordering=False, drop_final_batch=False
)

如果提供了label_key 参数，则返回由特征字典和标签组成的元组的Dataset。

例子：

serialized_examples = [
  features {
    feature { key:"age" value { int64_list { value:[ 0 ] } } }
    feature { key:"gender" value { bytes_list { value:[ "f" ] } } }
    feature { key:"kws" value { bytes_list { value:[ "code", "art" ] } } }
  },
  features {
    feature { key:"age" value { int64_list { value:[] } } }
    feature { key:"gender" value { bytes_list { value:[ "f" ] } } }
    feature { key:"kws" value { bytes_list { value:[ "sports" ] } } }
  }
]

我们可以使用参数：

features:{
  "age":FixedLenFeature([], dtype=tf.int64, default_value=-1),
  "gender":FixedLenFeature([], dtype=tf.string),
  "kws":VarLenFeature(dtype=tf.string),
}

预期的输出是：

{
  "age":[[0], [-1]],
  "gender":[["f"], ["f"]],
  "kws":SparseTensor(
    indices=[[0, 0], [0, 1], [1, 0]],
    values=["code", "art", "sports"]
    dense_shape=[2, 2]),
}