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a/develop/doc/_sources/api/v2/data.rst.txt +++ b/develop/doc/_sources/api/v2/data.rst.txt @@ -2,112 +2,9 @@ Data Reader Interface and DataSets ================================== +.. toctree:: + :maxdepth: 1 -DataTypes -========= - -.. automodule:: paddle.v2.data_type - :members: - :noindex: - -DataFeeder -========== - -.. automodule:: paddle.v2.data_feeder - :members: - :noindex: - -Reader -====== - -.. automodule:: paddle.v2.reader - :members: - :noindex: - -.. automodule:: paddle.v2.reader.creator - :members: - :noindex: - -minibatch -========= - -.. automodule:: paddle.v2.minibatch - :members: - :noindex: - -Dataset -======= - -.. automodule:: paddle.v2.dataset - :members: - :noindex: - -mnist -+++++ - -.. automodule:: paddle.v2.dataset.mnist - :members: - :noindex: - -cifar -+++++ - -.. automodule:: paddle.v2.dataset.cifar - :members: - :noindex: - -conll05 -+++++++ - -.. automodule:: paddle.v2.dataset.conll05 - :members: get_dict,get_embedding,test - :noindex: - -imdb -++++ - -.. automodule:: paddle.v2.dataset.imdb - :members: - :noindex: - -imikolov -++++++++ - -.. automodule:: paddle.v2.dataset.imikolov - :members: - :noindex: - -movielens -+++++++++ - -.. automodule:: paddle.v2.dataset.movielens - :members: - :noindex: - -.. autoclass:: paddle.v2.dataset.movielens.MovieInfo - :noindex: - -.. autoclass:: paddle.v2.dataset.movielens.UserInfo - :noindex: - -sentiment -+++++++++ - -.. automodule:: paddle.v2.dataset.sentiment - :members: - :noindex: - -uci_housing -+++++++++++ - -.. automodule:: paddle.v2.dataset.uci_housing - :members: - :noindex: - -wmt14 -+++++ - -.. automodule:: paddle.v2.dataset.wmt14 - :members: - :noindex: - + data/data_reader.rst + data/image.rst + data/dataset.rst diff --git a/develop/doc/_sources/api/v2/data/data_reader.rst.txt b/develop/doc/_sources/api/v2/data/data_reader.rst.txt new file mode 100644 index 00000000000..2ccfec9c284 --- /dev/null +++ b/develop/doc/_sources/api/v2/data/data_reader.rst.txt @@ -0,0 +1,36 @@ +===================== +Data Reader Interface +===================== + + +DataTypes +========= + +.. automodule:: paddle.v2.data_type + :members: + :noindex: + +DataFeeder +========== + +.. automodule:: paddle.v2.data_feeder + :members: + :noindex: + +Reader +====== + +.. automodule:: paddle.v2.reader + :members: + :noindex: + +.. automodule:: paddle.v2.reader.creator + :members: + :noindex: + +minibatch +========= + +.. automodule:: paddle.v2.minibatch + :members: + :noindex: diff --git a/develop/doc/_sources/api/v2/data/dataset.rst.txt b/develop/doc/_sources/api/v2/data/dataset.rst.txt new file mode 100644 index 00000000000..6a8ecc5bb1d --- /dev/null +++ b/develop/doc/_sources/api/v2/data/dataset.rst.txt @@ -0,0 +1,75 @@ +Dataset +======= + +.. automodule:: paddle.v2.dataset + :members: + :noindex: + +mnist ++++++ + +.. automodule:: paddle.v2.dataset.mnist + :members: + :noindex: + +cifar ++++++ + +.. automodule:: paddle.v2.dataset.cifar + :members: + :noindex: + +conll05 ++++++++ + +.. automodule:: paddle.v2.dataset.conll05 + :members: get_dict,get_embedding,test + :noindex: + +imdb +++++ + +.. automodule:: paddle.v2.dataset.imdb + :members: + :noindex: + +imikolov +++++++++ + +.. automodule:: paddle.v2.dataset.imikolov + :members: + :noindex: + +movielens ++++++++++ + +.. automodule:: paddle.v2.dataset.movielens + :members: + :noindex: + +.. autoclass:: paddle.v2.dataset.movielens.MovieInfo + :noindex: + +.. autoclass:: paddle.v2.dataset.movielens.UserInfo + :noindex: + +sentiment ++++++++++ + +.. automodule:: paddle.v2.dataset.sentiment + :members: + :noindex: + +uci_housing ++++++++++++ + +.. automodule:: paddle.v2.dataset.uci_housing + :members: + :noindex: + +wmt14 ++++++ + +.. automodule:: paddle.v2.dataset.wmt14 + :members: + :noindex: diff --git a/develop/doc/_sources/api/v2/data/image.rst.txt b/develop/doc/_sources/api/v2/data/image.rst.txt new file mode 100644 index 00000000000..97651ffa6be --- /dev/null +++ b/develop/doc/_sources/api/v2/data/image.rst.txt @@ -0,0 +1,5 @@ +Image Interface +=============== + +.. automodule:: paddle.v2.image + :members: diff --git a/develop/doc/api/index_en.html b/develop/doc/api/index_en.html index 481ded21ebc..0a68f7f28b7 100644 --- a/develop/doc/api/index_en.html +++ b/develop/doc/api/index_en.html @@ -147,7 +147,12 @@
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    -paddle.v2.data_type.dense_array(dim, seq_type=0)
    -

    Dense Array. It means the input feature is dense array with float type. -For example, if the input is an image with 28*28 pixels, the input of -Paddle neural network could be a dense vector with dimension 784 or a -numpy array with shape (28, 28).

    -

    For the 2-D convolution operation, each sample in one mini-batch must have -the similarly size in PaddlePaddle now. But, it supports variable-dimension -feature across mini-batch. For the variable-dimension, the param dim is not -used. While the data reader must yield numpy array and the data feeder will -set the data shape correctly.

    - --- - - - - - - - -
    Parameters:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of input.
      • -
    -
    Returns:

    An input type object.

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.dense_vector(dim, seq_type=0)
    -

    Dense Array. It means the input feature is dense array with float type. -For example, if the input is an image with 28*28 pixels, the input of -Paddle neural network could be a dense vector with dimension 784 or a -numpy array with shape (28, 28).

    -

    For the 2-D convolution operation, each sample in one mini-batch must have -the similarly size in PaddlePaddle now. But, it supports variable-dimension -feature across mini-batch. For the variable-dimension, the param dim is not -used. While the data reader must yield numpy array and the data feeder will -set the data shape correctly.

    - --- - - - - - - - -
    Parameters:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of input.
      • -
    -
    Returns:

    An input type object.

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.dense_vector_sequence(dim)
    -

    Data type of a sequence of dense vector.

    - --- - - - - - - - -
    Parameters:dim (int) – dimension of dense vector.
    Returns:An input type object
    Return type:InputType
    -
    - -
    -
    -paddle.v2.data_type.integer_value(value_range, seq_type=0)
    -

    Data type of integer.

    - --- - - - - - - - -
    Parameters:
      -
    • seq_type (int) – sequence type of this input.
      • -
    • value_range (int) – range of this integer.
      • -
    -
    Returns:

    An input type object

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.integer_value_sequence(value_range)
    -

    Data type of a sequence of integer.

    - --- - - - -
    Parameters:value_range (int) – range of each element.
    -
    - -
    -
    -paddle.v2.data_type.sparse_binary_vector(dim, seq_type=0)
    -

    Sparse binary vector. It means the input feature is a sparse vector and the -every element in this vector is either zero or one.

    - --- - - - - - - - -
    Parameters:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    Returns:

    An input type object.

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.sparse_binary_vector_sequence(dim)
    -
    -
    Data type of a sequence of sparse vector, which every element is either zero
    -
    or one.
    -
    - --- - - - - - - - -
    Parameters:dim (int) – dimension of sparse vector.
    Returns:An input type object
    Return type:InputType
    -
    - -
    -
    -paddle.v2.data_type.sparse_float_vector(dim, seq_type=0)
    -

    Sparse vector. It means the input feature is a sparse vector. Most of the -elements in this vector are zero, others could be any float value.

    - --- - - - - - - - -
    Parameters:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    Returns:

    An input type object.

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.sparse_float_vector_sequence(dim)
    -

    Data type of a sequence of sparse vector, which most elements are zero, -others could be any float value.

    - --- - - - - - - - -
    Parameters:dim (int) – dimension of sparse vector.
    Returns:An input type object
    Return type:InputType
    -
    - -
    -
    -paddle.v2.data_type.sparse_non_value_slot(dim, seq_type=0)
    -

    Sparse binary vector. It means the input feature is a sparse vector and the -every element in this vector is either zero or one.

    - --- - - - - - - - -
    Parameters:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    Returns:

    An input type object.

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.sparse_value_slot(dim, seq_type=0)
    -

    Sparse vector. It means the input feature is a sparse vector. Most of the -elements in this vector are zero, others could be any float value.

    - --- - - - - - - - -
    Parameters:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    Returns:

    An input type object.

    -
    Return type:

    InputType

    -
    -
    - -
    -
    -class paddle.v2.data_type.InputType(dim, seq_type, tp)
    -

    InputType is the base class for paddle input types.

    -
    -

    Note

    -

    this is a base class, and should never be used by user.

    -
    - --- - - - -
    Parameters:
      -
    • dim (int) – dimension of input. If the input is an integer, it means the -value range. Otherwise, it means the size of layer.
      • -
    • seq_type (int) – sequence type of input. 0 means it is not a sequence. 1 -means it is a variable length sequence. 2 means it is a -nested sequence.
      • -
    • type (int) – data type of input.
      • -
    -
    -
    - -
    -
    -

    DataFeeder

    -
    -
    -

    Reader

    -

    At training and testing time, PaddlePaddle programs need to read data. To ease -the users’ work to write data reading code, we define that

    -
      -
    • A reader is a function that reads data (from file, network, random number -generator, etc) and yields data items.
      • -
    • A reader creator is a function that returns a reader function.
      • -
    • A reader decorator is a function, which accepts one or more readers, and -returns a reader.
      • -
    • A batch reader is a function that reads data (from reader, file, network, -random number generator, etc) and yields a batch of data items.
      • +
      + -
      -

      Data Reader Interface

      -

      Indeed, data reader doesn’t have to be a function that reads and yields data -items. It can be any function with no parameter that creates a iterable -(anything can be used in for x in iterable):

      -
      iterable = data_reader()
-
      -
      -

      Element produced from the iterable should be a single entry of data, -not a mini batch. That entry of data could be a single item, or a tuple of -items. -Item should be of supported type (e.g., numpy 1d -array of float32, int, list of int)

      -

      An example implementation for single item data reader creator:

      -
      def reader_creator_random_image(width, height):
-    def reader():
-        while True:
-            yield numpy.random.uniform(-1, 1, size=width*height)
-return reader
-
      -
      -

      An example implementation for multiple item data reader creator:

      -
      def reader_creator_random_image_and_label(width, height, label):
-    def reader():
-        while True:
-            yield numpy.random.uniform(-1, 1, size=width*height), label
-return reader
-
      -
      -

      TODO(yuyang18): Should we add whole design doc here?

      -
      -
      -paddle.v2.reader.map_readers(func, *readers)
      -

      Creates a data reader that outputs return value of function using -output of each data readers as arguments.

      - --- - - - - - - - - - -
      Parameters:
        -
      • func – function to use. The type of func should be (Sample) => Sample
        • -
      • readers – readers whose outputs will be used as arguments of func.
        • -
      -
      Type:

      callable

      -
      Returns:

      the created data reader.

      -
      Return type:

      callable

      -
      -
      - -
      -
      -paddle.v2.reader.buffered(reader, size)
      -

      Creates a buffered data reader.

      -

      The buffered data reader will read and save data entries into a -buffer. Reading from the buffered data reader will proceed as long -as the buffer is not empty.

      - --- - - - - - -
      Parameters:
        -
      • reader (callable) – the data reader to read from.
        • -
      • size (int) – max buffer size.
        • -
      -
      Returns:

      the buffered data reader.

      -
      -
      - -
      -
      -paddle.v2.reader.compose(*readers, **kwargs)
      -

      Creates a data reader whose output is the combination of input readers.

      -

      If input readers output following data entries: -(1, 2) 3 (4, 5) -The composed reader will output: -(1, 2, 3, 4, 5)

      - --- - - - - - - - -
      Parameters:
        -
      • readers – readers that will be composed together.
        • -
      • check_alignment (bool) – if True, will check if input readers are aligned -correctly. If False, will not check alignment and trailing outputs -will be discarded. Defaults to True.
        • -
      -
      Returns:

      the new data reader.

      -
      Raises:

      ComposeNotAligned – outputs of readers are not aligned. -Will not raise when check_alignment is set to False.

      -
      -
      - -
      -
      -paddle.v2.reader.chain(*readers)
      -

      Creates a data reader whose output is the outputs of input data -readers chained together.

      -

      If input readers output following data entries: -[0, 0, 0] -[1, 1, 1] -[2, 2, 2] -The chained reader will output: -[0, 0, 0, 1, 1, 1, 2, 2, 2]

      - --- - - - - - - - -
      Parameters:readers – input readers.
      Returns:the new data reader.
      Return type:callable
      -
      - -
      -
      -paddle.v2.reader.shuffle(reader, buf_size)
      -

      Creates a data reader whose data output is shuffled.

      -

      Output from the iterator that created by original reader will be -buffered into shuffle buffer, and then shuffled. The size of shuffle buffer -is determined by argument buf_size.

      - --- - - - - - - - -
      Parameters:
        -
      • reader (callable) – the original reader whose output will be shuffled.
        • -
      • buf_size (int) – shuffle buffer size.
        • -
      -
      Returns:

      the new reader whose output is shuffled.

      -
      Return type:

      callable

      -
      -
      - -
      -
      -paddle.v2.reader.firstn(reader, n)
      -

      Limit the max number of samples that reader could return.

      - --- - - - - - - - -
      Parameters:
        -
      • reader (callable) – the data reader to read from.
        • -
      • n (int) – the max number of samples that return.
        • -
      -
      Returns:

      the decorated reader.

      -
      Return type:

      callable

      -
      -
      - -
      -
      -paddle.v2.reader.xmap_readers(mapper, reader, process_num, buffer_size, order=False)
      -

      Use multiprocess to map samples from reader by a mapper defined by user. -And this function contains a buffered decorator. -:param mapper: a function to map sample. -:type mapper: callable -:param reader: the data reader to read from -:type reader: callable -:param process_num: process number to handle original sample -:type process_num: int -:param buffer_size: max buffer size -:type buffer_size: int -:param order: keep the order of reader -:type order: bool -:return: the decarated reader -:rtype: callable

      -
      - -
      -

      Creator package contains some simple reader creator, which could -be used in user program.

      -
      -
      -paddle.v2.reader.creator.np_array(x)
      -

      Creates a reader that yields elements of x, if it is a -numpy vector. Or rows of x, if it is a numpy matrix. -Or any sub-hyperplane indexed by the highest dimension.

      - --- - - - - - -
      Parameters:x – the numpy array to create reader from.
      Returns:data reader created from x.
      -
      - -
      -
      -paddle.v2.reader.creator.text_file(path)
      -

      Creates a data reader that outputs text line by line from given text file. -Trailing new line (‘\n’) of each line will be removed.

      - --- - - - - - -
      Path:path of the text file.
      Returns:data reader of text file
      -
      - -
      -
      -paddle.v2.reader.creator.cloud_reader(paths, etcd_endpoints, timeout_sec=5, buf_size=64)
      -
      -
      Create a data reader that yield a record one by one from
      -
      the paths:
      -
      - --- - - - - - - - -
      Paths:path of recordio files, can be a string or a string list.
      Etcd_endpoints:the endpoints for etcd cluster
      Returns:data reader of recordio files.
      -
      - -
      -
      -

      minibatch

      -
      -
      -paddle.v2.minibatch.batch(reader, batch_size)
      -

      Create a batched reader.

      - --- - - - - - - - -
      Parameters:
        -
      • reader (callable) – the data reader to read from.
        • -
      • batch_size (int) – size of each mini-batch
        • -
      -
      Returns:

      the batched reader.

      -
      Return type:

      callable

      -
      -
      - -
      -
      -

      Dataset

      -

      Dataset package.

      -
      -

      mnist

      -

      MNIST dataset.

      -

      This module will download dataset from http://yann.lecun.com/exdb/mnist/ and -parse training set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.mnist.train()
      -

      MNIST training set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.mnist.test()
      -

      MNIST test set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      Returns:Test reader creator.
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.mnist.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      cifar

      -

      CIFAR dataset.

      -

      This module will download dataset from -https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into -paddle reader creators.

      -

      The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, -with 6000 images per class. There are 50000 training images and 10000 test -images.

      -

      The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes -containing 600 images each. There are 500 training images and 100 testing -images per class.

      -
      -
      -paddle.v2.dataset.cifar.train100()
      -

      CIFAR-100 training set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 99].

      - --- - - - - - -
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.test100()
      -

      CIFAR-100 test set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      Returns:Test reader creator.
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.train10()
      -

      CIFAR-10 training set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.test10()
      -

      CIFAR-10 test set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      Returns:Test reader creator.
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      conll05

      -

      Conll05 dataset. -Paddle semantic role labeling Book and demo use this dataset as an example. -Because Conll05 is not free in public, the default downloaded URL is test set -of Conll05 (which is public). Users can change URL and MD5 to their Conll -dataset. And a pre-trained word vector model based on Wikipedia corpus is used -to initialize SRL model.

      -
      -
      -paddle.v2.dataset.conll05.get_dict()
      -

      Get the word, verb and label dictionary of Wikipedia corpus.

      -
      - -
      -
      -paddle.v2.dataset.conll05.get_embedding()
      -

      Get the trained word vector based on Wikipedia corpus.

      -
      - -
      -
      -paddle.v2.dataset.conll05.test()
      -

      Conll05 test set creator.

      -

      Because the training dataset is not free, the test dataset is used for -training. It returns a reader creator, each sample in the reader is nine -features, including sentence sequence, predicate, predicate context, -predicate context flag and tagged sequence.

      - --- - - - - - -
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -

      imdb

      -

      IMDB dataset.

      -

      This module downloads IMDB dataset from -http://ai.stanford.edu/%7Eamaas/data/sentiment/. This dataset contains a set -of 25,000 highly polar movie reviews for training, and 25,000 for testing. -Besides, this module also provides API for building dictionary.

      -
      -
      -paddle.v2.dataset.imdb.build_dict(pattern, cutoff)
      -

      Build a word dictionary from the corpus. Keys of the dictionary are words, -and values are zero-based IDs of these words.

      -
      - -
      -
      -paddle.v2.dataset.imdb.train(word_idx)
      -

      IMDB training set creator.

      -

      It returns a reader creator, each sample in the reader is an zero-based ID -sequence and label in [0, 1].

      - --- - - - - - - - -
      Parameters:word_idx (dict) – word dictionary
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.imdb.test(word_idx)
      -

      IMDB test set creator.

      -

      It returns a reader creator, each sample in the reader is an zero-based ID -sequence and label in [0, 1].

      - --- - - - - - - - -
      Parameters:word_idx (dict) – word dictionary
      Returns:Test reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.imdb.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      imikolov

      -

      imikolov’s simple dataset.

      -

      This module will download dataset from -http://www.fit.vutbr.cz/~imikolov/rnnlm/ and parse training set and test set -into paddle reader creators.

      -
      -
      -paddle.v2.dataset.imikolov.build_dict(min_word_freq=50)
      -

      Build a word dictionary from the corpus, Keys of the dictionary are words, -and values are zero-based IDs of these words.

      -
      - -
      -
      -paddle.v2.dataset.imikolov.train(word_idx, n, data_type=1)
      -

      imikolov training set creator.

      -

      It returns a reader creator, each sample in the reader is a word ID -tuple.

      - --- - - - - - - - -
      Parameters:
        -
      • word_idx (dict) – word dictionary
        • -
      • n (int) – sliding window size if type is ngram, otherwise max length of sequence
        • -
      • data_type (member variable of DataType (NGRAM or SEQ)) – data type (ngram or sequence)
        • -
      -
      Returns:

      Training reader creator

      -
      Return type:

      callable

      -
      -
      - -
      -
      -paddle.v2.dataset.imikolov.test(word_idx, n, data_type=1)
      -

      imikolov test set creator.

      -

      It returns a reader creator, each sample in the reader is a word ID -tuple.

      - --- - - - - - - - -
      Parameters:
        -
      • word_idx (dict) – word dictionary
        • -
      • n (int) – sliding window size if type is ngram, otherwise max length of sequence
        • -
      • data_type (member variable of DataType (NGRAM or SEQ)) – data type (ngram or sequence)
        • -
      -
      Returns:

      Test reader creator

      -
      Return type:

      callable

      -
      -
      - -
      -
      -paddle.v2.dataset.imikolov.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      movielens

      -

      Movielens 1-M dataset.

      -

      Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000 -movies, which was collected by GroupLens Research. This module will download -Movielens 1-M dataset from -http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training -set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.movielens.get_movie_title_dict()
      -

      Get movie title dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.max_movie_id()
      -

      Get the maximum value of movie id.

      -
      - -
      -
      -paddle.v2.dataset.movielens.max_user_id()
      -

      Get the maximum value of user id.

      -
      - -
      -
      -paddle.v2.dataset.movielens.max_job_id()
      -

      Get the maximum value of job id.

      -
      - -
      -
      -paddle.v2.dataset.movielens.movie_categories()
      -

      Get movie categoriges dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.user_info()
      -

      Get user info dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.movie_info()
      -

      Get movie info dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -class paddle.v2.dataset.movielens.MovieInfo(index, categories, title)
      -

      Movie id, title and categories information are stored in MovieInfo.

      -
      - -
      -
      -class paddle.v2.dataset.movielens.UserInfo(index, gender, age, job_id)
      -

      User id, gender, age, and job information are stored in UserInfo.

      -
      - -
      -
      -

      sentiment

      -

      The script fetch and preprocess movie_reviews data set that provided by NLTK

      -

      TODO(yuyang18): Complete dataset.

      -
      -
      -paddle.v2.dataset.sentiment.get_word_dict()
      -

      Sorted the words by the frequency of words which occur in sample -:return:

      -
      -
      words_freq_sorted
      -
      - -
      -
      -paddle.v2.dataset.sentiment.train()
      -

      Default training set reader creator

      -
      - -
      -
      -paddle.v2.dataset.sentiment.test()
      -

      Default test set reader creator

      -
      - -
      -
      -paddle.v2.dataset.sentiment.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      uci_housing

      -

      UCI Housing dataset.

      -

      This module will download dataset from -https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and -parse training set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.uci_housing.train()
      -

      UCI_HOUSING training set creator.

      -

      It returns a reader creator, each sample in the reader is features after -normalization and price number.

      - --- - - - - - -
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.uci_housing.test()
      -

      UCI_HOUSING test set creator.

      -

      It returns a reader creator, each sample in the reader is features after -normalization and price number.

      - --- - - - - - -
      Returns:Test reader creator
      Return type:callable
      -
      - -
      -
      -

      wmt14

      -

      WMT14 dataset. -The original WMT14 dataset is too large and a small set of data for set is -provided. This module will download dataset from -http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and -parse training set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.wmt14.train(dict_size)
      -

      WMT14 training set creator.

      -

      It returns a reader creator, each sample in the reader is source language -word ID sequence, target language word ID sequence and next word ID -sequence.

      - --- - - - - - -
      Returns:Training reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.wmt14.test(dict_size)
      -

      WMT14 test set creator.

      -

      It returns a reader creator, each sample in the reader is source language -word ID sequence, target language word ID sequence and next word ID -sequence.

      - --- - - - - - -
      Returns:Test reader creator
      Return type:callable
      -
      - -
      -
      -paddle.v2.dataset.wmt14.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
    @@ -1314,7 +211,7 @@ sequence.

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    • @@ -188,17 +193,87 @@

    Index

    - P + B + | C + | L + | P + | R + | S + | T
    +

    B

    + + +
    + +

    C

    + + +
    + +

    L

    + + + +
    +

    P

    +
    +

    R

    + + + +
    + +

    S

    + + +
    + +

    T

    + + +
    +
    diff --git a/develop/doc/getstarted/basic_usage/index_en.html b/develop/doc/getstarted/basic_usage/index_en.html index fbef10c7e61..f93d2d17126 100644 --- a/develop/doc/getstarted/basic_usage/index_en.html +++ b/develop/doc/getstarted/basic_usage/index_en.html @@ -145,7 +145,12 @@
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Model Format","Paddle\u591a\u8bed\u8a00\u63a5\u53e3\u5b9e\u73b0","C-API \u6a21\u578b\u63a8\u65ad\u5b9e\u73b0\u6587\u6863","RNNOp design","Optimizer Design","Averaging Parameter in PaddlePaddle","Design Doc: The C++ Class Parameters","Design Doc: PaddlePaddle Programs","Prune","Design Doc: Python API","Python Data Reader Design Doc","Design Doc: Distributed Training Architecture","Design Doc: Operation Graph Based Parameter Server","Design Doc: Session","Design Doc: Refactorization Overview","Design Doc: Gradient Operators Registration","Regularization in PaddlePaddle","PaddlePaddle\u53d1\u884c\u89c4\u8303","Design of Scope in Paddle","Design Doc: Selected Rows","Interaction between C++ and Python","Design for TensorArray","Background","Simple Linear Regression","Installing from Sources","PaddlePaddle in Docker Containers","Install and Build","GET STARTED","RNN Models","RNN Configuration","Build PaddlePaddle from Source Code and Run Unit Test","Contribute Code","Write New Layers","How 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  • Data Reader Interface and DataSets +
  • Training and Inference
    • diff --git a/develop/doc_cn/_sources/api/v2/data.rst.txt b/develop/doc_cn/_sources/api/v2/data.rst.txt index fef87c4fbdb..b56c7332cc2 100644 --- a/develop/doc_cn/_sources/api/v2/data.rst.txt +++ b/develop/doc_cn/_sources/api/v2/data.rst.txt @@ -2,112 +2,9 @@ Data Reader Interface and DataSets ================================== +.. toctree:: + :maxdepth: 1 -DataTypes -========= - -.. automodule:: paddle.v2.data_type - :members: - :noindex: - -DataFeeder -========== - -.. automodule:: paddle.v2.data_feeder - :members: - :noindex: - -Reader -====== - -.. automodule:: paddle.v2.reader - :members: - :noindex: - -.. automodule:: paddle.v2.reader.creator - :members: - :noindex: - -minibatch -========= - -.. automodule:: paddle.v2.minibatch - :members: - :noindex: - -Dataset -======= - -.. automodule:: paddle.v2.dataset - :members: - :noindex: - -mnist -+++++ - -.. automodule:: paddle.v2.dataset.mnist - :members: - :noindex: - -cifar -+++++ - -.. automodule:: paddle.v2.dataset.cifar - :members: - :noindex: - -conll05 -+++++++ - -.. automodule:: paddle.v2.dataset.conll05 - :members: get_dict,get_embedding,test - :noindex: - -imdb -++++ - -.. automodule:: paddle.v2.dataset.imdb - :members: - :noindex: - -imikolov -++++++++ - -.. automodule:: paddle.v2.dataset.imikolov - :members: - :noindex: - -movielens -+++++++++ - -.. automodule:: paddle.v2.dataset.movielens - :members: - :noindex: - -.. autoclass:: paddle.v2.dataset.movielens.MovieInfo - :noindex: - -.. autoclass:: paddle.v2.dataset.movielens.UserInfo - :noindex: - -sentiment -+++++++++ - -.. automodule:: paddle.v2.dataset.sentiment - :members: - :noindex: - -uci_housing -+++++++++++ - -.. automodule:: paddle.v2.dataset.uci_housing - :members: - :noindex: - -wmt14 -+++++ - -.. automodule:: paddle.v2.dataset.wmt14 - :members: - :noindex: - + data/data_reader.rst + data/image.rst + data/dataset.rst diff --git a/develop/doc_cn/_sources/api/v2/data/data_reader.rst.txt b/develop/doc_cn/_sources/api/v2/data/data_reader.rst.txt new file mode 100644 index 00000000000..2ccfec9c284 --- /dev/null +++ b/develop/doc_cn/_sources/api/v2/data/data_reader.rst.txt @@ -0,0 +1,36 @@ +===================== +Data Reader Interface +===================== + + +DataTypes +========= + +.. automodule:: paddle.v2.data_type + :members: + :noindex: + +DataFeeder +========== + +.. automodule:: paddle.v2.data_feeder + :members: + :noindex: + +Reader +====== + +.. automodule:: paddle.v2.reader + :members: + :noindex: + +.. automodule:: paddle.v2.reader.creator + :members: + :noindex: + +minibatch +========= + +.. automodule:: paddle.v2.minibatch + :members: + :noindex: diff --git a/develop/doc_cn/_sources/api/v2/data/dataset.rst.txt b/develop/doc_cn/_sources/api/v2/data/dataset.rst.txt new file mode 100644 index 00000000000..6a8ecc5bb1d --- /dev/null +++ b/develop/doc_cn/_sources/api/v2/data/dataset.rst.txt @@ -0,0 +1,75 @@ +Dataset +======= + +.. automodule:: paddle.v2.dataset + :members: + :noindex: + +mnist ++++++ + +.. automodule:: paddle.v2.dataset.mnist + :members: + :noindex: + +cifar ++++++ + +.. automodule:: paddle.v2.dataset.cifar + :members: + :noindex: + +conll05 ++++++++ + +.. automodule:: paddle.v2.dataset.conll05 + :members: get_dict,get_embedding,test + :noindex: + +imdb +++++ + +.. automodule:: paddle.v2.dataset.imdb + :members: + :noindex: + +imikolov +++++++++ + +.. automodule:: paddle.v2.dataset.imikolov + :members: + :noindex: + +movielens ++++++++++ + +.. automodule:: paddle.v2.dataset.movielens + :members: + :noindex: + +.. autoclass:: paddle.v2.dataset.movielens.MovieInfo + :noindex: + +.. autoclass:: paddle.v2.dataset.movielens.UserInfo + :noindex: + +sentiment ++++++++++ + +.. automodule:: paddle.v2.dataset.sentiment + :members: + :noindex: + +uci_housing ++++++++++++ + +.. automodule:: paddle.v2.dataset.uci_housing + :members: + :noindex: + +wmt14 ++++++ + +.. automodule:: paddle.v2.dataset.wmt14 + :members: + :noindex: diff --git a/develop/doc_cn/_sources/api/v2/data/image.rst.txt b/develop/doc_cn/_sources/api/v2/data/image.rst.txt new file mode 100644 index 00000000000..97651ffa6be --- /dev/null +++ b/develop/doc_cn/_sources/api/v2/data/image.rst.txt @@ -0,0 +1,5 @@ +Image Interface +=============== + +.. automodule:: paddle.v2.image + :members: diff --git a/develop/doc_cn/api/index_cn.html b/develop/doc_cn/api/index_cn.html index b2b3b98c09a..6f031dcaffe 100644 --- a/develop/doc_cn/api/index_cn.html +++ b/develop/doc_cn/api/index_cn.html @@ -152,7 +152,12 @@
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    Data Reader Interface and DataSets

    -
    -

    DataTypes

    -
    -
    -paddle.v2.data_type.dense_array(dim, seq_type=0)
    -

    Dense Array. It means the input feature is dense array with float type. -For example, if the input is an image with 28*28 pixels, the input of -Paddle neural network could be a dense vector with dimension 784 or a -numpy array with shape (28, 28).

    -

    For the 2-D convolution operation, each sample in one mini-batch must have -the similarly size in PaddlePaddle now. But, it supports variable-dimension -feature across mini-batch. For the variable-dimension, the param dim is not -used. While the data reader must yield numpy array and the data feeder will -set the data shape correctly.

    - --- - - - - - - - -
    参数:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of input.
      • -
    -
    返回:

    An input type object.

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.dense_vector(dim, seq_type=0)
    -

    Dense Array. It means the input feature is dense array with float type. -For example, if the input is an image with 28*28 pixels, the input of -Paddle neural network could be a dense vector with dimension 784 or a -numpy array with shape (28, 28).

    -

    For the 2-D convolution operation, each sample in one mini-batch must have -the similarly size in PaddlePaddle now. But, it supports variable-dimension -feature across mini-batch. For the variable-dimension, the param dim is not -used. While the data reader must yield numpy array and the data feeder will -set the data shape correctly.

    - --- - - - - - - - -
    参数:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of input.
      • -
    -
    返回:

    An input type object.

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.dense_vector_sequence(dim)
    -

    Data type of a sequence of dense vector.

    - --- - - - - - - - -
    参数:dim (int) – dimension of dense vector.
    返回:An input type object
    返回类型:InputType
    -
    - -
    -
    -paddle.v2.data_type.integer_value(value_range, seq_type=0)
    -

    Data type of integer.

    - --- - - - - - - - -
    参数:
      -
    • seq_type (int) – sequence type of this input.
      • -
    • value_range (int) – range of this integer.
      • -
    -
    返回:

    An input type object

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.integer_value_sequence(value_range)
    -

    Data type of a sequence of integer.

    - --- - - - -
    参数:value_range (int) – range of each element.
    -
    - -
    -
    -paddle.v2.data_type.sparse_binary_vector(dim, seq_type=0)
    -

    Sparse binary vector. It means the input feature is a sparse vector and the -every element in this vector is either zero or one.

    - --- - - - - - - - -
    参数:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    返回:

    An input type object.

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.sparse_binary_vector_sequence(dim)
    -
    -
    Data type of a sequence of sparse vector, which every element is either zero
    -
    or one.
    -
    - --- - - - - - - - -
    参数:dim (int) – dimension of sparse vector.
    返回:An input type object
    返回类型:InputType
    -
    - -
    -
    -paddle.v2.data_type.sparse_float_vector(dim, seq_type=0)
    -

    Sparse vector. It means the input feature is a sparse vector. Most of the -elements in this vector are zero, others could be any float value.

    - --- - - - - - - - -
    参数:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    返回:

    An input type object.

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.sparse_float_vector_sequence(dim)
    -

    Data type of a sequence of sparse vector, which most elements are zero, -others could be any float value.

    - --- - - - - - - - -
    参数:dim (int) – dimension of sparse vector.
    返回:An input type object
    返回类型:InputType
    -
    - -
    -
    -paddle.v2.data_type.sparse_non_value_slot(dim, seq_type=0)
    -

    Sparse binary vector. It means the input feature is a sparse vector and the -every element in this vector is either zero or one.

    - --- - - - - - - - -
    参数:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    返回:

    An input type object.

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -paddle.v2.data_type.sparse_value_slot(dim, seq_type=0)
    -

    Sparse vector. It means the input feature is a sparse vector. Most of the -elements in this vector are zero, others could be any float value.

    - --- - - - - - - - -
    参数:
      -
    • dim (int) – dimension of this vector.
      • -
    • seq_type (int) – sequence type of this input.
      • -
    -
    返回:

    An input type object.

    -
    返回类型:

    InputType

    -
    -
    - -
    -
    -class paddle.v2.data_type.InputType(dim, seq_type, tp)
    -

    InputType is the base class for paddle input types.

    -
    -

    注解

    -

    this is a base class, and should never be used by user.

    -
    - --- - - - -
    参数:
      -
    • dim (int) – dimension of input. If the input is an integer, it means the -value range. Otherwise, it means the size of layer.
      • -
    • seq_type (int) – sequence type of input. 0 means it is not a sequence. 1 -means it is a variable length sequence. 2 means it is a -nested sequence.
      • -
    • type (int) – data type of input.
      • -
    -
    -
    - -
    -
    -

    DataFeeder

    -
    -
    -

    Reader

    -

    At training and testing time, PaddlePaddle programs need to read data. To ease -the users’ work to write data reading code, we define that

    -
      -
    • A reader is a function that reads data (from file, network, random number -generator, etc) and yields data items.
      • -
    • A reader creator is a function that returns a reader function.
      • -
    • A reader decorator is a function, which accepts one or more readers, and -returns a reader.
      • -
    • A batch reader is a function that reads data (from reader, file, network, -random number generator, etc) and yields a batch of data items.
      • +
      + -
      -

      Data Reader Interface

      -

      Indeed, data reader doesn’t have to be a function that reads and yields data -items. It can be any function with no parameter that creates a iterable -(anything can be used in for x in iterable):

      -
      iterable = data_reader()
-
      -
      -

      Element produced from the iterable should be a single entry of data, -not a mini batch. That entry of data could be a single item, or a tuple of -items. -Item should be of supported type (e.g., numpy 1d -array of float32, int, list of int)

      -

      An example implementation for single item data reader creator:

      -
      def reader_creator_random_image(width, height):
-    def reader():
-        while True:
-            yield numpy.random.uniform(-1, 1, size=width*height)
-return reader
-
      -
      -

      An example implementation for multiple item data reader creator:

      -
      def reader_creator_random_image_and_label(width, height, label):
-    def reader():
-        while True:
-            yield numpy.random.uniform(-1, 1, size=width*height), label
-return reader
-
      -
      -

      TODO(yuyang18): Should we add whole design doc here?

      -
      -
      -paddle.v2.reader.map_readers(func, *readers)
      -

      Creates a data reader that outputs return value of function using -output of each data readers as arguments.

      - --- - - - - - - - - - -
      参数:
        -
      • func – function to use. The type of func should be (Sample) => Sample
        • -
      • readers – readers whose outputs will be used as arguments of func.
        • -
      -
      Type:

      callable

      -
      返回:

      the created data reader.

      -
      返回类型:

      callable

      -
      -
      - -
      -
      -paddle.v2.reader.buffered(reader, size)
      -

      Creates a buffered data reader.

      -

      The buffered data reader will read and save data entries into a -buffer. Reading from the buffered data reader will proceed as long -as the buffer is not empty.

      - --- - - - - - -
      参数:
        -
      • reader (callable) – the data reader to read from.
        • -
      • size (int) – max buffer size.
        • -
      -
      返回:

      the buffered data reader.

      -
      -
      - -
      -
      -paddle.v2.reader.compose(*readers, **kwargs)
      -

      Creates a data reader whose output is the combination of input readers.

      -

      If input readers output following data entries: -(1, 2) 3 (4, 5) -The composed reader will output: -(1, 2, 3, 4, 5)

      - --- - - - - - - - -
      参数:
        -
      • readers – readers that will be composed together.
        • -
      • check_alignment (bool) – if True, will check if input readers are aligned -correctly. If False, will not check alignment and trailing outputs -will be discarded. Defaults to True.
        • -
      -
      返回:

      the new data reader.

      -
      引发:

      ComposeNotAligned – outputs of readers are not aligned. -Will not raise when check_alignment is set to False.

      -
      -
      - -
      -
      -paddle.v2.reader.chain(*readers)
      -

      Creates a data reader whose output is the outputs of input data -readers chained together.

      -

      If input readers output following data entries: -[0, 0, 0] -[1, 1, 1] -[2, 2, 2] -The chained reader will output: -[0, 0, 0, 1, 1, 1, 2, 2, 2]

      - --- - - - - - - - -
      参数:readers – input readers.
      返回:the new data reader.
      返回类型:callable
      -
      - -
      -
      -paddle.v2.reader.shuffle(reader, buf_size)
      -

      Creates a data reader whose data output is shuffled.

      -

      Output from the iterator that created by original reader will be -buffered into shuffle buffer, and then shuffled. The size of shuffle buffer -is determined by argument buf_size.

      - --- - - - - - - - -
      参数:
        -
      • reader (callable) – the original reader whose output will be shuffled.
        • -
      • buf_size (int) – shuffle buffer size.
        • -
      -
      返回:

      the new reader whose output is shuffled.

      -
      返回类型:

      callable

      -
      -
      - -
      -
      -paddle.v2.reader.firstn(reader, n)
      -

      Limit the max number of samples that reader could return.

      - --- - - - - - - - -
      参数:
        -
      • reader (callable) – the data reader to read from.
        • -
      • n (int) – the max number of samples that return.
        • -
      -
      返回:

      the decorated reader.

      -
      返回类型:

      callable

      -
      -
      - -
      -
      -paddle.v2.reader.xmap_readers(mapper, reader, process_num, buffer_size, order=False)
      -

      Use multiprocess to map samples from reader by a mapper defined by user. -And this function contains a buffered decorator. -:param mapper: a function to map sample. -:type mapper: callable -:param reader: the data reader to read from -:type reader: callable -:param process_num: process number to handle original sample -:type process_num: int -:param buffer_size: max buffer size -:type buffer_size: int -:param order: keep the order of reader -:type order: bool -:return: the decarated reader -:rtype: callable

      -
      - -
      -

      Creator package contains some simple reader creator, which could -be used in user program.

      -
      -
      -paddle.v2.reader.creator.np_array(x)
      -

      Creates a reader that yields elements of x, if it is a -numpy vector. Or rows of x, if it is a numpy matrix. -Or any sub-hyperplane indexed by the highest dimension.

      - --- - - - - - -
      参数:x – the numpy array to create reader from.
      返回:data reader created from x.
      -
      - -
      -
      -paddle.v2.reader.creator.text_file(path)
      -

      Creates a data reader that outputs text line by line from given text file. -Trailing new line (‘\n’) of each line will be removed.

      - --- - - - - - -
      Path:path of the text file.
      返回:data reader of text file
      -
      - -
      -
      -paddle.v2.reader.creator.cloud_reader(paths, etcd_endpoints, timeout_sec=5, buf_size=64)
      -
      -
      Create a data reader that yield a record one by one from
      -
      the paths:
      -
      - --- - - - - - - - -
      Paths:path of recordio files, can be a string or a string list.
      Etcd_endpoints:the endpoints for etcd cluster
      返回:data reader of recordio files.
      -
      - -
      -
      -

      minibatch

      -
      -
      -paddle.v2.minibatch.batch(reader, batch_size)
      -

      Create a batched reader.

      - --- - - - - - - - -
      参数:
        -
      • reader (callable) – the data reader to read from.
        • -
      • batch_size (int) – size of each mini-batch
        • -
      -
      返回:

      the batched reader.

      -
      返回类型:

      callable

      -
      -
      - -
      -
      -

      Dataset

      -

      Dataset package.

      -
      -

      mnist

      -

      MNIST dataset.

      -

      This module will download dataset from http://yann.lecun.com/exdb/mnist/ and -parse training set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.mnist.train()
      -

      MNIST training set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.mnist.test()
      -

      MNIST test set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      返回:Test reader creator.
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.mnist.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      cifar

      -

      CIFAR dataset.

      -

      This module will download dataset from -https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into -paddle reader creators.

      -

      The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, -with 6000 images per class. There are 50000 training images and 10000 test -images.

      -

      The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes -containing 600 images each. There are 500 training images and 100 testing -images per class.

      -
      -
      -paddle.v2.dataset.cifar.train100()
      -

      CIFAR-100 training set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 99].

      - --- - - - - - -
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.test100()
      -

      CIFAR-100 test set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      返回:Test reader creator.
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.train10()
      -

      CIFAR-10 training set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.test10()
      -

      CIFAR-10 test set creator.

      -

      It returns a reader creator, each sample in the reader is image pixels in -[0, 1] and label in [0, 9].

      - --- - - - - - -
      返回:Test reader creator.
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.cifar.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      conll05

      -

      Conll05 dataset. -Paddle semantic role labeling Book and demo use this dataset as an example. -Because Conll05 is not free in public, the default downloaded URL is test set -of Conll05 (which is public). Users can change URL and MD5 to their Conll -dataset. And a pre-trained word vector model based on Wikipedia corpus is used -to initialize SRL model.

      -
      -
      -paddle.v2.dataset.conll05.get_dict()
      -

      Get the word, verb and label dictionary of Wikipedia corpus.

      -
      - -
      -
      -paddle.v2.dataset.conll05.get_embedding()
      -

      Get the trained word vector based on Wikipedia corpus.

      -
      - -
      -
      -paddle.v2.dataset.conll05.test()
      -

      Conll05 test set creator.

      -

      Because the training dataset is not free, the test dataset is used for -training. It returns a reader creator, each sample in the reader is nine -features, including sentence sequence, predicate, predicate context, -predicate context flag and tagged sequence.

      - --- - - - - - -
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -

      imdb

      -

      IMDB dataset.

      -

      This module downloads IMDB dataset from -http://ai.stanford.edu/%7Eamaas/data/sentiment/. This dataset contains a set -of 25,000 highly polar movie reviews for training, and 25,000 for testing. -Besides, this module also provides API for building dictionary.

      -
      -
      -paddle.v2.dataset.imdb.build_dict(pattern, cutoff)
      -

      Build a word dictionary from the corpus. Keys of the dictionary are words, -and values are zero-based IDs of these words.

      -
      - -
      -
      -paddle.v2.dataset.imdb.train(word_idx)
      -

      IMDB training set creator.

      -

      It returns a reader creator, each sample in the reader is an zero-based ID -sequence and label in [0, 1].

      - --- - - - - - - - -
      参数:word_idx (dict) – word dictionary
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.imdb.test(word_idx)
      -

      IMDB test set creator.

      -

      It returns a reader creator, each sample in the reader is an zero-based ID -sequence and label in [0, 1].

      - --- - - - - - - - -
      参数:word_idx (dict) – word dictionary
      返回:Test reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.imdb.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      imikolov

      -

      imikolov’s simple dataset.

      -

      This module will download dataset from -http://www.fit.vutbr.cz/~imikolov/rnnlm/ and parse training set and test set -into paddle reader creators.

      -
      -
      -paddle.v2.dataset.imikolov.build_dict(min_word_freq=50)
      -

      Build a word dictionary from the corpus, Keys of the dictionary are words, -and values are zero-based IDs of these words.

      -
      - -
      -
      -paddle.v2.dataset.imikolov.train(word_idx, n, data_type=1)
      -

      imikolov training set creator.

      -

      It returns a reader creator, each sample in the reader is a word ID -tuple.

      - --- - - - - - - - -
      参数:
        -
      • word_idx (dict) – word dictionary
        • -
      • n (int) – sliding window size if type is ngram, otherwise max length of sequence
        • -
      • data_type (member variable of DataType (NGRAM or SEQ)) – data type (ngram or sequence)
        • -
      -
      返回:

      Training reader creator

      -
      返回类型:

      callable

      -
      -
      - -
      -
      -paddle.v2.dataset.imikolov.test(word_idx, n, data_type=1)
      -

      imikolov test set creator.

      -

      It returns a reader creator, each sample in the reader is a word ID -tuple.

      - --- - - - - - - - -
      参数:
        -
      • word_idx (dict) – word dictionary
        • -
      • n (int) – sliding window size if type is ngram, otherwise max length of sequence
        • -
      • data_type (member variable of DataType (NGRAM or SEQ)) – data type (ngram or sequence)
        • -
      -
      返回:

      Test reader creator

      -
      返回类型:

      callable

      -
      -
      - -
      -
      -paddle.v2.dataset.imikolov.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      movielens

      -

      Movielens 1-M dataset.

      -

      Movielens 1-M dataset contains 1 million ratings from 6000 users on 4000 -movies, which was collected by GroupLens Research. This module will download -Movielens 1-M dataset from -http://files.grouplens.org/datasets/movielens/ml-1m.zip and parse training -set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.movielens.get_movie_title_dict()
      -

      Get movie title dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.max_movie_id()
      -

      Get the maximum value of movie id.

      -
      - -
      -
      -paddle.v2.dataset.movielens.max_user_id()
      -

      Get the maximum value of user id.

      -
      - -
      -
      -paddle.v2.dataset.movielens.max_job_id()
      -

      Get the maximum value of job id.

      -
      - -
      -
      -paddle.v2.dataset.movielens.movie_categories()
      -

      Get movie categoriges dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.user_info()
      -

      Get user info dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.movie_info()
      -

      Get movie info dictionary.

      -
      - -
      -
      -paddle.v2.dataset.movielens.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -class paddle.v2.dataset.movielens.MovieInfo(index, categories, title)
      -

      Movie id, title and categories information are stored in MovieInfo.

      -
      - -
      -
      -class paddle.v2.dataset.movielens.UserInfo(index, gender, age, job_id)
      -

      User id, gender, age, and job information are stored in UserInfo.

      -
      - -
      -
      -

      sentiment

      -

      The script fetch and preprocess movie_reviews data set that provided by NLTK

      -

      TODO(yuyang18): Complete dataset.

      -
      -
      -paddle.v2.dataset.sentiment.get_word_dict()
      -

      Sorted the words by the frequency of words which occur in sample -:return:

      -
      -
      words_freq_sorted
      -
      - -
      -
      -paddle.v2.dataset.sentiment.train()
      -

      Default training set reader creator

      -
      - -
      -
      -paddle.v2.dataset.sentiment.test()
      -

      Default test set reader creator

      -
      - -
      -
      -paddle.v2.dataset.sentiment.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
      -
      -

      uci_housing

      -

      UCI Housing dataset.

      -

      This module will download dataset from -https://archive.ics.uci.edu/ml/machine-learning-databases/housing/ and -parse training set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.uci_housing.train()
      -

      UCI_HOUSING training set creator.

      -

      It returns a reader creator, each sample in the reader is features after -normalization and price number.

      - --- - - - - - -
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.uci_housing.test()
      -

      UCI_HOUSING test set creator.

      -

      It returns a reader creator, each sample in the reader is features after -normalization and price number.

      - --- - - - - - -
      返回:Test reader creator
      返回类型:callable
      -
      - -
      -
      -

      wmt14

      -

      WMT14 dataset. -The original WMT14 dataset is too large and a small set of data for set is -provided. This module will download dataset from -http://paddlepaddle.cdn.bcebos.com/demo/wmt_shrinked_data/wmt14.tgz and -parse training set and test set into paddle reader creators.

      -
      -
      -paddle.v2.dataset.wmt14.train(dict_size)
      -

      WMT14 training set creator.

      -

      It returns a reader creator, each sample in the reader is source language -word ID sequence, target language word ID sequence and next word ID -sequence.

      - --- - - - - - -
      返回:Training reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.wmt14.test(dict_size)
      -

      WMT14 test set creator.

      -

      It returns a reader creator, each sample in the reader is source language -word ID sequence, target language word ID sequence and next word ID -sequence.

      - --- - - - - - -
      返回:Test reader creator
      返回类型:callable
      -
      - -
      -
      -paddle.v2.dataset.wmt14.convert(path)
      -

      Converts dataset to recordio format

      -
      - -
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