public final class
Skipgram
Parses a text file and creates a batch of examples.
Nested Classes
class | Skipgram.Options | Optional attributes for Skipgram
|
Constants
String | OP_NAME | The name of this op, as known by TensorFlow core engine |
Public Methods
static Skipgram |
create(Scope scope, String filename, Long batchSize, Options... options)
Factory method to create a class wrapping a new Skipgram operation.
|
Output<TInt32> |
currentEpoch()
The current epoch number.
|
Output<TInt32> |
examples()
A vector of word ids.
|
Output<TInt32> |
labels()
A vector of word ids.
|
static Skipgram.Options |
minCount(Long minCount)
|
static Skipgram.Options |
subsample(Float subsample)
|
Output<TInt64> |
totalWordsProcessed()
The total number of words processed so far.
|
Output<TInt32> |
vocabFreq()
Frequencies of words.
|
Output<TString> |
vocabWord()
A vector of words in the corpus.
|
static Skipgram.Options |
windowSize(Long windowSize)
|
Output<TInt64> |
wordsPerEpoch()
Number of words per epoch in the data file.
|
Inherited Methods
Constants
public static final String OP_NAME
The name of this op, as known by TensorFlow core engine
Constant Value:
"Skipgram"
Public Methods
public static Skipgram create (Scope scope, String filename, Long batchSize, Options... options)
Factory method to create a class wrapping a new Skipgram operation.
Parameters
scope | current scope |
---|---|
filename | The corpus's text file name. |
batchSize | The size of produced batch. |
options | carries optional attributes values |
Returns
- a new instance of Skipgram
public static Skipgram.Options minCount (Long minCount)
Parameters
minCount | The minimum number of word occurrences for it to be included in the vocabulary. |
---|
public static Skipgram.Options subsample (Float subsample)
Parameters
subsample | Threshold for word occurrence. Words that appear with higher frequency will be randomly down-sampled. Set to 0 to disable. |
---|
public static Skipgram.Options windowSize (Long windowSize)
Parameters
windowSize | The number of words to predict to the left and right of the target. |
---|