ó ÉÈ÷Xc@s¼dZddlmZddlmZddlmZddlmZddlm Z d d„Z ej dƒd ƒd d d d d „Z ej d ƒd ƒd „Zd e_eƒZd S(sLPython wrappers around Brain. This file is MACHINE GENERATED! Do not edit. iÿÿÿÿ(t text_format(t op_def_pb2(top_def_registry(tops(top_def_librarycCs=tjdd|d|d|d|d|d|d|d |ƒS( sCTraining via negative sampling. Args: w_in: A `Tensor` of type mutable `float32`. input word embedding. w_out: A `Tensor` of type mutable `float32`. output word embedding. examples: A `Tensor` of type `int32`. A vector of word ids. labels: A `Tensor` of type `int32`. A vector of word ids. lr: A `Tensor` of type `float32`. vocab_count: A list of `ints`. Count of words in the vocabulary. num_negative_samples: An `int`. Number of negative samples per exaple. name: A name for the operation (optional). Returns: The created Operation. tNegTraintw_intw_outtexamplestlabelstlrt vocab_counttnum_negative_samplestname(t _op_def_libtapply_op(RRRR R R R R ((sj/tmp/pip-build-UG86a1/tensorflow/tensorflow-0.6.0.data/purelib/tensorflow/models/embedding/gen_word2vec.pyt neg_trains RcCs1tjdd|d|d|d|d|d|ƒS(srParses a text file and creates a batch of examples. Args: filename: A `string`. The corpus's text file name. batch_size: An `int`. The size of produced batch. window_size: An optional `int`. Defaults to `5`. The number of words to predict to the left and right of the target. min_count: An optional `int`. Defaults to `5`. The minimum number of word occurrences for it to be included in the vocabulary. subsample: An optional `float`. Defaults to `0.001`. Threshold for word occurrence. Words that appear with higher frequency will be randomly down-sampled. Set to 0 to disable. name: A name for the operation (optional). Returns: A tuple of `Tensor` objects (vocab_word, vocab_freq, words_per_epoch, current_epoch, total_words_processed, examples, labels). vocab_word: A `Tensor` of type `string`. A vector of words in the corpus. vocab_freq: A `Tensor` of type `int32`. Frequencies of words. Sorted in the non-ascending order. words_per_epoch: A `Tensor` of type `int64`. Number of words per epoch in the data file. current_epoch: A `Tensor` of type `int32`. The current epoch number. total_words_processed: A `Tensor` of type `int64`. The total number of words processed so far. examples: A `Tensor` of type `int32`. A vector of word ids. labels: A `Tensor` of type `int32`. A vector of word ids. tSkipgramtfilenamet batch_sizet window_sizet min_countt subsampleR (RR(RRRRRR ((sj/tmp/pip-build-UG86a1/tensorflow/tensorflow-0.6.0.data/purelib/tensorflow/models/embedding/gen_word2vec.pytskipgram's  RcCsItjƒ}tjtj|ƒtj|ƒtj ƒ}|j |ƒ|S(N( RtOpListRtMerget_InitOpDefLibraryt op_list_asciiRtregister_op_listRt OpDefLibraryt add_op_list(top_listt op_def_lib((sj/tmp/pip-build-UG86a1/tensorflow/tensorflow-0.6.0.data/purelib/tensorflow/models/embedding/gen_word2vec.pyRIs     sop { name: "NegTrain" input_arg { name: "w_in" type: DT_FLOAT is_ref: true } input_arg { name: "w_out" type: DT_FLOAT is_ref: true } input_arg { name: "examples" type: DT_INT32 } input_arg { name: "labels" type: DT_INT32 } input_arg { name: "lr" type: DT_FLOAT } attr { name: "vocab_count" type: "list(int)" } attr { name: "num_negative_samples" type: "int" } } op { name: "Skipgram" output_arg { name: "vocab_word" type: DT_STRING } output_arg { name: "vocab_freq" type: DT_INT32 } output_arg { name: "words_per_epoch" type: DT_INT64 } output_arg { name: "current_epoch" type: DT_INT32 } output_arg { name: "total_words_processed" type: DT_INT64 } output_arg { name: "examples" type: DT_INT32 } output_arg { name: "labels" type: DT_INT32 } attr { name: "filename" type: "string" } attr { name: "batch_size" type: "int" } attr { name: "window_size" type: "int" default_value { i: 5 } } attr { name: "min_count" type: "int" default_value { i: 5 } } attr { name: "subsample" type: "float" default_value { f: 0.001 } } } N(t__doc__tgoogle.protobufRttensorflow.core.frameworkRttensorflow.python.frameworkRRttensorflow.python.opsRtNoneRt RegisterShapeRRRR(((sj/tmp/pip-build-UG86a1/tensorflow/tensorflow-0.6.0.data/purelib/tensorflow/models/embedding/gen_word2vec.pyts   f