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tf.data.experimental.CsvDataset

A Dataset comprising lines from one or more CSV files.

Inherits From: Dataset

tf.data.experimental.CsvDataset(
    filenames, record_defaults, compression_type=None, buffer_size=None,
    header=False, field_delim=',', use_quote_delim=True,
    na_value='', select_cols=None, exclude_cols=None
)

Used in the notebooks

Used in the guide Used in the tutorials

filenames A tf.string tensor containing one or more filenames.
record_defaults A list of default values for the CSV fields. Each item in the list is either a valid CSV DType (float32, float64, int32, int64, string), or a Tensor object with one of the above types. One per column of CSV data, with either a scalar Tensor default value for the column if it is optional, or DType or empty Tensor if required. If both this and select_columns are specified, these must have the same lengths, and column_defaults is assumed to be sorted in order of increasing column index. If both this and 'exclude_cols' are specified, the sum of lengths of record_defaults and exclude_cols should equal the total number of columns in the CSV file.
compression_type (Optional.) A tf.string scalar evaluating to one of "" (no compression), "ZLIB", or "GZIP". Defaults to no compression.
buffer_size (Optional.) A tf.int64 scalar denoting the number of bytes to buffer while reading files. Defaults to 4MB.
header (Optional.) A tf.bool scalar indicating whether the CSV file(s) have header line(s) that should be skipped when parsing. Defaults to False.
field_delim (Optional.) A tf.string scalar containing the delimiter character that separates fields in a record. Defaults to ",".
use_quote_delim (Optional.) A tf.bool scalar. If False, treats double quotation marks as regular characters inside of string fields (ignoring RFC 4180, Section 2, Bullet 5). Defaults to True.
na_value (Optional.) A tf.string scalar indicating a value that will be treated as NA/NaN.
select_cols (Optional.) A sorted list of column indices to select from the input data. If specified, only this subset of columns will be parsed. Defaults to parsing all columns. At most one of select_cols and exclude_cols can be specified.
exclude_cols (Optional.) A sorted list of column indices to exclude from the input data. If specified, only the complement of this set of column will be parsed. Defaults to parsing all columns. At most one of select_cols and exclude_cols can be specified.

InvalidArgumentError If exclude_cols is not None and len(exclude_cols) + len(record_defaults) does not match the total number of columns in the file(s)

element_spec The type specification of an element of this dataset.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
dataset.element_spec
TensorSpec(shape=(), dtype=tf.int32, name=None)

Methods

apply

View source

apply(
    transformation_func
)

Applies a transformation function to this dataset.

apply enables chaining of custom Dataset transformations, which are represented as functions that take one Dataset argument and return a transformed Dataset.

dataset = tf.data.Dataset.range(100)
def dataset_fn(ds):
  return ds.filter(lambda x: x < 5)
dataset = dataset.apply(dataset_fn)
list(dataset.as_numpy_iterator())
[0, 1, 2, 3, 4]

Args
transformation_func A function that takes one Dataset argument and returns a Dataset.

Returns
Dataset The Dataset returned by applying transformation_func to this dataset.

as_numpy_iterator

View source

as_numpy_iterator()

Returns an iterator which converts all elements of the dataset to numpy.

Use as_numpy_iterator to inspect the content of your dataset. To see element shapes and types, print dataset elements directly instead of using as_numpy_iterator.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset:
  print(element)
tf.Tensor(1, shape=(), dtype=int32)
tf.Tensor(2, shape=(), dtype=int32)
tf.Tensor(3, shape=(), dtype=int32)

This method requires that you are running in eager mode and the dataset's element_spec contains only TensorSpec components.

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
for element in dataset.as_numpy_iterator():
  print(element)
1
2
3

dataset = tf.data.Dataset.from_tensor_slices([1, 2, 3])
print(list(dataset.as_numpy_iterator()))
[1, 2, 3]

as_numpy_iterator() will preserve the nested structure of dataset elements.

dataset = tf.data.Dataset.from_tensor_slices({'a': ([1, 2], [3, 4]),
                                              'b': [5, 6]})
list(dataset.as_numpy_iterator()) == [{'a': (1, 3), 'b': 5},
                                      {'a': (2, 4), 'b': 6}]
True

Returns
An iterable over the elements of the dataset, with their tensors converted to numpy arrays.

Raises
TypeError if an element contains a non-Tensor value.
RuntimeError if eager execution is not enabled.

batch

View source

batch(
    batch_size, drop_remainder=False
)

Combines consecutive elements of this dataset into batches.

dataset = tf.data.Dataset.range(8)
dataset = dataset.batch(3)
list(dataset.as_numpy_iterator())
[array([0, 1, 2]), array([3, 4, 5]), array([6, 7])]

dataset = tf.data.Dataset.range(8)
dataset = dataset.batch(3, drop_remainder=True)
list(dataset.as_numpy_iterator())
[array([0, 1, 2]), array([3, 4, 5])]

The components of the resulting element will have an additional outer dimension, which will be batch_size (or N % batch_size for the last element if batch_size does not divide the number of input elements N evenly and drop_remainder is False). If your program depends on the batches having the same outer dimension, you should set the drop_remainder argument to True to prevent the smaller batch from being produced.

Args