Help protect the Great Barrier Reef with TensorFlow on Kaggle Join Challenge

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A Dataset comprising lines from one or more text files.

filenames A tf.string tensor or containing one or more filenames.
compression_type (Optional.) A tf.string scalar evaluating to one of "" (no compression), "ZLIB", or "GZIP".
buffer_size (Optional.) A tf.int64 scalar denoting the number of bytes to buffer. A value of 0 results in the default buffering values chosen based on the compression type.
num_parallel_reads (Optional.) A tf.int64 scalar representing the number of files to read in parallel. If greater than one, the records of files read in parallel are outputted in an interleaved order. If your input pipeline is I/O bottlenecked, consider setting this parameter to a value greater than one to parallelize the I/O. If None, files will be read sequentially.

element_spec The type specification of an element of this dataset.



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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.

For example:

dataset = ( x: x ** 2)
           .apply(group_by_window(key_func, reduce_func, window_size))
           .map(lambda x: x ** 3))

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

Dataset The Dataset returned by applying transformation_func to this dataset.


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Combines consecutive elements of this dataset into batches.

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.

batch_size A tf.int64 scalar tf.Tensor, representing the number of consecutive elements of this dataset to combine in a single batch.
drop_remainder (Optional.) A tf.bool scalar tf.Tensor, representing whether the last batch should be dropped in the case it has fewer than batch_size elements; the default behavior is not to drop the smaller batch.

Dataset A Dataset.


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Caches the elements in this dataset.

filename A tf.string scalar tf.Tensor, representing the name of a directory on the filesystem to use for caching elements in this Dataset. If a filename is not provided, the dataset will be cached in memory.

Dataset A Dataset.


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Creates a Dataset by concatenating the given dataset with this dataset.

a = Dataset.range(1, 4)  # ==> [ 1, 2, 3 ]
b = Dataset.range(4, 8)  # ==> [ 4, 5, 6, 7 ]

# The input dataset and dataset to be concatenated should have the same
# nested structures and output types.
# c = Dataset.range(8, 14).batch(2)  # ==> [ [8, 9], [10, 11], [12, 13] ]
# d = Dataset.from_tensor_slices([14.0, 15.0, 16.0])
# a.concatenate(c) and a.concatenate(d) would result in error.

a.concatenate(b)  # ==> [ 1, 2, 3, 4, 5, 6, 7 ]

dataset Dataset to be concatenated.

Dataset A Dataset.


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Enumerates the elements of this dataset.

It is similar to python's enumerate.

For example:

# NOTE: The following examples use `{ ... }` to represent the
# contents of a dataset.
a = { 1, 2, 3 }
b = { (7, 8), (9, 10) }

# The nested structure of the `datasets` argument determines the
# structure of elements in the resulting dataset.
a.enumerate(start=5)) == { (5, 1), (6, 2), (7, 3) }
b.enumerate() == { (0, (7, 8)), (1, (9, 10)) }

start A tf.int64 scalar tf.Tensor, representing the start value for enumeration.

Dataset A Dataset.


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Filters this dataset according to predicate.

d =[1, 2, 3])

d = d.filter(lambda x: x < 3)  # ==> [1, 2]

# `tf.math.equal(x, y)` is required for equality comparison
def filter_fn(x):
  return tf.math.equal(x, 1)

d = d.filter(filter_fn)  # ==> [1]

predicate A function mapping a dataset element to a boolean.

Dataset The Dataset containing the elements of this dataset for which predicate is True.


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Maps map_func across this dataset and flattens the result.

Use flat_map if you want to make sure that the order of your dataset stays the same. For example, to flatten a dataset of batches into a dataset of their elements:

a = Dataset.from_tensor_slices([ [1, 2, 3], [4, 5, 6], [7, 8, 9] ])

a.flat_map(lambda x: Dataset.from_tensor_slices(x + 1)) # ==>
#  [ 2, 3, 4, 5, 6, 7, 8, 9, 10 ] is a generalization of flat_map, since flat_map produces the same output as

map_func A function mapping a dataset element to a dataset.

Dataset A Dataset.


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Creates a Dataset whose elements are generated by generator.

The generator argument must be a callable object that returns an object that supports the iter() protocol (e.g. a generator function). The elements generated by generator must be compatible with the given output_types and (optional) output_shapes arguments.

For example:

import itertools

def gen():
  for i in itertools.count(1):
    yield (i, [1] * i)

ds =
    gen, (tf.int64, tf.int64), (tf.TensorShape([]), tf.TensorShape([None])))

for value in ds.take(2):
  print value
# (1, array([1]))
# (2, array([1, 1]))

generator A callable object that returns an object that supports the iter() protocol. If args is not specified, generator must take no arguments; otherwise it must take as many arguments as there are values in args.
output_types A nested structure of tf.DType objects corresponding to each component of an element yielded by generator.
output_shapes (Optional.) A nested structure of tf.TensorShape objects corresponding to each component of an element yielded by generator.
args (Optional.) A tuple of tf.Tensor objects that will be evaluated and passed to generator as NumPy-array arguments.

Dataset A Dataset.


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Creates a Dataset whose elements are slices of the given tensors.

Note that if tensors contains a NumPy array, and eager execution is not enabled, the values will be embedded in the graph as one or more tf.constant operations. For large datasets (> 1 GB), this can waste memory and run into byte limits of graph serialization. If tensors contains one or more large NumPy arrays, consider the alternative described in this guide.