Documentation
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Overview ¶
Package lstmutils contains some useful functions for processing trained LSTM models in the particular format used by aural2.
Index ¶
- func LoadGraph(graph *tf.Graph, sess *tf.Session, scopeName string) (input, output tf.Output, statePlaceholders, stateFetches []tf.Output, ...)
- func MakeRenderLSTMstate(oSession *tftrain.OnlineSess) (renderLSTMstate func(*tf.Tensor) ([]byte, error), err error)
- func MakeSeqInference(savedModel tf.SavedModel) (seqInference func(*tf.Tensor) (*tf.Tensor, error), err error)
- func MakeStepInference(oSession tftrain.OnlineSess) (stepInference func(*tf.Tensor) ([]float32, error), err error)
Constants ¶
This section is empty.
Variables ¶
This section is empty.
Functions ¶
func LoadGraph ¶
func LoadGraph(graph *tf.Graph, sess *tf.Session, scopeName string) (input, output tf.Output, statePlaceholders, stateFetches []tf.Output, stateFeeds map[tf.Output]*tf.Tensor, err error)
LoadGraph loads the TF graph from the bytes of an LSTM graphdef, and returns a
func MakeRenderLSTMstate ¶
func MakeSeqInference ¶
func MakeSeqInference(savedModel tf.SavedModel) (seqInference func(*tf.Tensor) (*tf.Tensor, error), err error)
MakeSeqInference returns a function that takes an output of a slice of mfccs of one clip, and returns a [][]float32 labels.
func MakeStepInference ¶
func MakeStepInference(oSession tftrain.OnlineSess) (stepInference func(*tf.Tensor) ([]float32, error), err error)
MakeStepInference returns a function that takes a tensor of one mfccs, and returns a []float32 labels.
Types ¶
This section is empty.
Source Files
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