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Functions48 in github.com/danieldjohnson/biaxial-rnn-music-composition

⨍ Functions 48◇ Types & classes 3
↓ 8 callersFunctionget_last_layer
(result)
model.py:65
↓ 7 callersFunctionhas_hidden
Whether a layer has a trainable initial hidden state.
model.py:9
↓ 5 callersFunctionensure_list
(result)
model.py:71
↓ 5 callersFunctioninitial_state_with_taps
Optionally wrap tensor variable into a dict with taps=[-1]
model.py:34
↓ 4 callersFunctionprobAndSuccessToImgArray
(prob, succ, idx)
visualize.py:15
↓ 2 callersFunctionadd_cur
(ipt)
visualize.py:55
↓ 2 callersFunctiongetPieceSegment
(pieces)
multi_training.py:31
↓ 2 callersFunctioninternalMatrixToImgArray
(inmat)
visualize.py:10
↓ 2 callersFunctionnoteStateMatrixToMidi
(statematrix, name="example")
midi_to_statematrix.py:64
↓ 2 callersFunctionnoteStateSingleToInputForm
(state,time)
data.py:45
↓ 2 callersFunctionsigmoid
(x)
visualize.py:4
↓ 1 callersFunctionactToColor
(memcell, activation)
visualize.py:7
↓ 1 callersFunctionbuildBeat
(time)
data.py:29
↓ 1 callersFunctionbuildContext
(state)
data.py:21
↓ 1 callersFunctiondrawPast
(probs, succs)
visualize.py:37
↓ 1 callersFunctiongetOrDefault
(l, i, d)
data.py:15
↓ 1 callersFunctiongetPieceBatch
(pieces)
multi_training.py:41
↓ 1 callersFunctioninitial_state
Initalizes the recurrence relation with an initial hidden state if needed, else replaces with a "None" to tell Theano that the network **
model.py:22
↓ 1 callersFunctionmatrixify
(vector, n)
model.py:16
↓ 1 callersFunctionmidiToNoteStateMatrix
(midifile)
midi_to_statematrix.py:6
↓ 1 callersFunctionnoteInputForm
(note, state, context, beat)
data.py:32
↓ 1 callersFunctionnoteSentinel
(note)
data.py:5
↓ 1 callersFunctionnoteStateMatrixToInputForm
(statematrix)
data.py:50
↓ 1 callersFunctionpastColor
(prob, succ)
visualize.py:34
↓ 1 callersMethodsetup_predict
(self)
model.py:264
↓ 1 callersMethodsetup_slow_walk
(self)
model.py:336
↓ 1 callersMethodsetup_train
(self)
model.py:128
↓ 1 callersMethodstart_slow_walk
(self, seed)
model.py:381
Method__init__
(self)
model.py:47
Method__init__
(self, t_layer_sizes, p_layer_sizes, dropout=0)
model.py:80
Method_predict_step_note
(self, in_data_from_time, *states)
model.py:237
Methodactivate
(self, x)
model.py:53
Methodcreate_variables
(self)
model.py:50
Functionfetch_train_thoughts
(m,pcs,batches,name="trainthoughts")
main.py:32
Functiongen_adaptive
(m,pcs,times,keep_thoughts=False,name="final")
main.py:9
Methodlearned_config
(self)
model.py:118
FunctionloadPieces
(dirpath)
multi_training.py:12
Methodmake_node
(self, state, time)
out_to_in_op.py:11
Methodparams
(self)
model.py:57
Methodparams
(self)
model.py:108
Methodperform
(self, node, inputs_storage, output_storage)
out_to_in_op.py:17
Functionsignal_handler
(signame, sf)
multi_training.py:47
FunctionstartSentinel
()
data.py:4
Methodstep_note
(in_data, *other)
model.py:145
Methodstep_time
(in_data, *other)
model.py:137
FunctionthoughtsAndPastToStackedArray
(thoughts, probs, succs, len_past)
visualize.py:46
FunctionthoughtsToImageArray
(thoughts)
visualize.py:18
FunctiontrainPiece
(model,pieces,epochs,start=0)
multi_training.py:45