tf.contrib.data.make_csv_dataset(
file_pattern,
batch_size,
column_names=None,
column_defaults=None,
label_name=None,
select_columns=None,
field_delim=',',
use_quote_delim=True,
na_value='',
header=True,
num_epochs=None,
shuffle=True,
shuffle_buffer_size=10000,
shuffle_seed=None,
prefetch_buffer_size=optimization.AUTOTUNE,
num_parallel_reads=1,
sloppy=False,
num_rows_for_inference=100,
compression_type=None
)
Defined in tensorflow/contrib/data/python/ops/readers.py
.
Reads CSV files into a dataset. (deprecated)
Reads CSV files into a dataset, where each element is a (features, labels)
tuple that corresponds to a batch of CSV rows. The features dictionary
maps feature column names to Tensor
s containing the corresponding
feature data, and labels is a Tensor
containing the batch's label data.
Args:
file_pattern
: List of files or patterns of file paths containing CSV records. Seetf.gfile.Glob
for pattern rules.batch_size
: An int representing the number of records to combine in a single batch.column_names
: An optional list of strings that corresponds to the CSV columns, in order. One per column of the input record. If this is not provided, infers the column names from the first row of the records. These names will be the keys of the features dict of each dataset element.column_defaults
: A optional list of default values for the CSV fields. One item per selected column of the input record. Each item in the list is either a valid CSV dtype (float32, float64, int32, int64, or string), or aTensor
with one of the aforementioned types. The tensor can either be a scalar default value (if the column is optional), or an empty tensor (if the column is required). If a dtype is provided instead of a tensor, the column is also treated as required. If this list is not provided, tries to infer types based on reading the first num_rows_for_inference rows of files specified, and assumes all columns are optional, defaulting to0
for numeric values and""
for string values. If both this andselect_columns
are specified, these must have the same lengths, andcolumn_defaults
is assumed to be sorted in order of increasing column index.label_name
: A optional string corresponding to the label column. If provided, the data for this column is returned as a separateTensor
from the features dictionary, so that the dataset complies with the format expected by atf.Estimator.train
ortf.Estimator.evaluate
input function.select_columns
: An optional list of integer indices or string column names, that specifies a subset of columns of CSV data to select. If column names are provided, these must correspond to names provided incolumn_names
or inferred from the file header lines. When this argument is specified, only a subset of CSV columns will be parsed and returned, corresponding to the columns specified. Using this results in faster parsing and lower memory usage. If both this andcolumn_defaults
are specified, these must have the same lengths, andcolumn_defaults
is assumed to be sorted in order of increasing column index.field_delim
: An optionalstring
. Defaults to","
. Char delimiter to separate fields in a record.use_quote_delim
: An optional bool. Defaults toTrue
. If false, treats double quotation marks as regular characters inside of the string fields.na_value
: Additional string to recognize as NA/NaN.header
: A bool that indicates whether the first rows of provided CSV files correspond to header lines with column names, and should not be included in the data.num_epochs
: An int specifying the number of times this dataset is repeated. If None, cycles through the dataset forever.shuffle
: A bool that indicates whether the input should be shuffled.shuffle_buffer_size
: Buffer size to use for shuffling. A large buffer size ensures better shuffling, but increases memory usage and startup time.shuffle_seed
: Randomization seed to use for shuffling.prefetch_buffer_size
: An int specifying the number of feature batches to prefetch for performance improvement. Recommended value is the number of batches consumed per training step. Defaults to auto-tune.num_parallel_reads
: Number of threads used to read CSV records from files. If >1, the results will be interleaved.sloppy
: IfTrue
, reading performance will be improved at the cost of non-deterministic ordering. IfFalse
, the order of elements produced is deterministic prior to shuffling (elements are still randomized ifshuffle=True
. Note that if the seed is set, then order of elements after shuffling is deterministic). Defaults toFalse
.num_rows_for_inference
: Number of rows of a file to use for type inference if record_defaults is not provided. If None, reads all the rows of all the files. Defaults to 100.compression_type
: (Optional.) Atf.string
scalar evaluating to one of""
(no compression),"ZLIB"
, or"GZIP"
. Defaults to no compression.
Returns:
A dataset, where each element is a (features, labels) tuple that corresponds
to a batch of batch_size
CSV rows. The features dictionary maps feature
column names to Tensor
s containing the corresponding column data, and
labels is a Tensor
containing the column data for the label column
specified by label_name
.
Raises:
ValueError
: If any of the arguments is malformed.