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Google Magenta
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1. Motivation
2. Google Magenta
3. NN
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1. Motivation
2. Google Magenta
3. NN
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My iTunesTop4
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My iTunesTop4
2001 2015
2013
2009
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Google Magenta
Deep Learning
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1. Motivation
2. Google Magenta
3. NN
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Google Magenta
Tensorflowgithub
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: Magenta
https://www.youtube.com/watch?v=QlVoR1jQrPk
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midi tfrecords sequenceexample
tensorflowprogram
tensorflowprogram
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midi tfrecords sequenceexample
tensorflowprogram
tensorflowprogram
melody_rnn_create_dataset--config= --input=(tfrecords file
path)--output_dir=(seq file path)
melody_rnn_train--config=(name of neural network)
--run_dir=(model output dir)--sequence_example_file=(seq file
path)
convert_dir_to_note_sequences--input_dir=$INPUT_DIRECTORY
--output_file=$SEQUENCES
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midi tfrecords sequenceexample
tensorflowprogram
tensorflowprogram
melody_rnn_generate--config=(name of neural network)
--run_dir=(model file path)--output_dir=(midi output dir)
https://github.com/tensorflow/magenta/tree/master/magenta/models/melody_rnn
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No.1
Google
F
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(Input data) 30
epoch() 500
NN(LSTM) 2 128
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(1)
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(2)
Input Data
""
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Basic RNNLookback RNNAttention RNN
Recurrent Neural Network(LSTM)DeepLearning Basic
RNNLookbackAttention LookbackAttention
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Basic RNNLookback RNNAttention RNN
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BasicRNN Recurrent Neural Network
Recurrent Neural Network(LSTM)DeepLearning (State)/ DLFw
https://magenta.tensorflow.org/2016/06/10/recurrent-neural-network-generation-tutorial/
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BasicRNN Input(One-hot vector)
One-hot Vector 11.5Bit
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
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Basic RNNLookback RNNAttention RNN
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Lookback RNN Input
12
(note-off)
(note-on)
(note-on)
(no)
(note-on)
(4/4)
Basic RNN Lookback RNN
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Basic RNNLookback RNNAttention RNN
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Attention RNN
Attention
" = %'")
= *
(i)
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:
1. Motivation
2. Google Magenta
3. NN
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magenta
()""
magenta
shuffle onshuffle off
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Input()
(note-off)
(note-on)
(no)Basic RNN
+
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(PoC) - ()NN
LSTM
LSTM
LSTM/ tLSTMPoC
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
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34SONG FROM PI: A MUSICALLY PLAUSIBLE NETWORK FOR POP MUSIC
GENERATION (Hang Chu, Raquel Urtasun, Sanja Fidler 2016)
()
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(PoC) -
one hot vector 4 +
/2/4/8/16/32/647 ()
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0
0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0
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(PoC) -
LSTMone hot LSTM LSTM
60%
30%
10%
""
60%
30%
10%
""
LSTM LSTM
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class RNN(Chain): def __init__(self):
super(RNN,self).__init__( embed=L.EmbedID(num_of_input_nodes,
n_units), l1=L.LSTM(n_units, n_units),l2=L.LSTM(n_units, n_units),
fc=L.Linear(n_units, num_of_input_nodes),
)
def
reset_state(self):self.l1.reset_state()self.l2.reset_state
def __call__(self, input_vector): h0 = self.embed(input_vector)
h1 = self.l1(h0)h2 = self.l2(h1)y = self.fc(h2)
return y
chainer 20LSTM
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PoC
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(Input data) 30 F
epoch() 300
NN(LSTM) 2 15
. 2
20 B.S
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1: (F )
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2: (B.S )
FHR/HM EE
ED->E
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3: XXX(F )
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4: (B.S. )
5(4
)
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: ...
Softmax
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:
1. Motivation
2. Google Magenta
3. NN
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NN
LSTM one hot vector
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() magentaNN(1) : Drums RNN
RNN
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() magentaNN(2) : imporv RNN
RNN
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() magentaNN(3) : Rl Tuner
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() : Wavenet
https://deepmind.com/blog/wavenet-generative-model-raw-audio/
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() : Wavenet
https://deepmind.com/blog/wavenet-generative-model-raw-audio/
CD1
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() (QRNN?) Midi magenta Drums RNN
=>
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