In [1]:
import tensorflow as tf
print(tf.__version__)
2.2.0
In [2]:
import numpy as np
np.random.seed(2022)
In [3]:
# Windows で music21 を使うための余分なコード
import os
from music21 import environment
if os.name == 'nt':
us = environment.UserSettings()
us['musescoreDirectPNGPath']='C:/Program Files/MuseScore 3/bin/MuseScore3.exe'
us['musicxmlPath']='C:/Program Files/MuseScore 3/bin/MuseScore3.exe'
In [4]:
data_dir = 'D:\\data\\gdl_book14\\MIDI\\jsbach'
In [5]:
if os.name == 'nt':
LS_CMD = 'dir /s'
else:
LS_CMD = 'ls -lR'
!{LS_CMD} {data_dir}
ドライブ D のボリューム ラベルがありません。
ボリューム シリアル番号は 606C-349E です
D:\data\gdl_book14\MIDI\jsbach のディレクトリ
2021/12/22 08:37 <DIR> .
2021/12/22 08:37 <DIR> ..
2021/12/11 10:53 <DIR> bwv1007
2021/12/22 08:37 7,206 bwv1007.zip
2021/12/11 10:53 <DIR> bwv1008
2021/12/22 08:37 8,021 bwv1008.zip
2021/12/11 10:53 <DIR> bwv1009
2021/12/22 08:37 8,783 bwv1009.zip
2021/12/11 10:53 <DIR> bwv1010
2021/12/22 08:37 8,622 bwv1010.zip
2021/12/11 10:53 <DIR> bwv1011
2021/12/22 08:37 8,973 bwv1011.zip
2021/12/11 10:53 <DIR> bwv1012
2021/12/22 08:37 10,242 bwv1012.zip
6 個のファイル 51,847 バイト
D:\data\gdl_book14\MIDI\jsbach\bwv1007 のディレクトリ
2021/12/11 10:53 <DIR> .
2021/12/11 10:53 <DIR> ..
1997/02/22 21:59 4,922 cs1-1pre.mid
1997/02/22 21:59 7,088 cs1-2all.mid
1997/02/22 22:00 6,159 cs1-3cou.mid
1997/02/22 22:25 3,209 cs1-4sar.mid
1997/02/22 23:13 5,704 cs1-5men.mid
1997/02/22 22:01 3,966 cs1-6gig.mid
6 個のファイル 31,048 バイト
D:\data\gdl_book14\MIDI\jsbach\bwv1008 のディレクトリ
2021/12/11 10:53 <DIR> .
2021/12/11 10:53 <DIR> ..
1997/02/22 22:03 4,957 cs2-1pre.mid
1997/02/22 22:03 5,765 cs2-2all.mid
1997/02/22 22:03 5,508 cs2-3cou.mid
1997/02/22 22:24 4,049 cs2-4sar.mid
1997/02/22 23:14 6,014 cs2-5men.mid
1997/02/22 22:27 6,183 cs2-6gig.mid
6 個のファイル 32,476 バイト
D:\data\gdl_book14\MIDI\jsbach\bwv1009 のディレクトリ
2021/12/11 10:53 <DIR> .
2021/12/11 10:53 <DIR> ..
1997/02/22 22:27 7,331 cs3-1pre.mid
1997/02/22 22:28 7,097 cs3-2all.mid
1997/02/22 22:27 8,019 cs3-3cou.mid
1997/02/22 22:28 4,021 cs3-4sar.mid
1997/02/22 22:28 7,484 cs3-5bou.mid
1997/02/22 22:34 7,850 cs3-6gig.mid
6 個のファイル 41,802 バイト
D:\data\gdl_book14\MIDI\jsbach\bwv1010 のディレクトリ
2021/12/11 10:53 <DIR> .
2021/12/11 10:53 <DIR> ..
1997/02/22 22:29 6,701 cs4-1pre.mid
1997/02/22 22:10 7,718 cs4-2all.mid
1997/02/22 22:29 7,528 cs4-3cou.mid
1997/02/22 22:29 3,880 cs4-4sar.mid
1997/02/22 22:11 9,968 cs4-5bou.mid
1997/02/22 22:12 7,770 cs4-6gig.mid
6 個のファイル 43,565 バイト
D:\data\gdl_book14\MIDI\jsbach\bwv1011 のディレクトリ
2021/12/11 10:53 <DIR> .
2021/12/11 10:53 <DIR> ..
1997/02/22 22:30 9,777 cs5-1pre.mid
1997/02/22 22:37 6,091 cs5-2all.mid
1997/02/22 22:38 4,673 cs5-3cou.mid
1997/02/22 22:14 2,538 cs5-4sar.mid
1997/02/22 22:39 10,660 cs5-5gav.mid
1997/02/22 22:31 3,900 cs5-6gig.mid
6 個のファイル 37,639 バイト
D:\data\gdl_book14\MIDI\jsbach\bwv1012 のディレクトリ
2021/12/11 10:53 <DIR> .
2021/12/11 10:53 <DIR> ..
1997/02/22 22:15 10,451 cs6-1pre.mid
1997/02/22 22:16 8,236 cs6-2all.mid
1997/02/22 22:31 9,135 cs6-3cou.mid
1997/02/22 22:49 5,424 cs6-4sar.mid
1997/02/22 22:18 9,907 cs6-5gav.mid
1997/02/22 22:32 9,656 cs6-6gig.mid
6 個のファイル 52,809 バイト
ファイルの総数:
42 個のファイル 291,186 バイト
20 個のディレクトリ 1,749,686,632,448 バイトの空き領域
In [6]:
import os
import glob
midi_paths = glob.glob(os.path.join(data_dir, '*/*.mid'))
In [7]:
print(midi_paths[:2])
['D:\\data\\gdl_book14\\MIDI\\jsbach\\bwv1007\\cs1-1pre.mid', 'D:\\data\\gdl_book14\\MIDI\\jsbach\\bwv1007\\cs1-2all.mid']
In [8]:
data_filepath = 'run/music_params.pkl'
In [9]:
if os.name == 'nt':
CAT_CMD = 'type'
else:
CAT_CMD = 'cat'
! {CAT_CMD} {os.path.join('nw', 'LSTMMusic.py')}
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import os
import glob
import pickle as pkl
import datetime
import music21
import itertools
###################################################################################
# DataLoader
###################################################################################
class ScoreDataset(tf.keras.utils.Sequence):
def __init__(self, save_path='music_param.pkl', midi_paths=None, seq_len=32):
if not midi_paths is None:
self._build(midi_paths, seq_len)
self.save(save_path)
else:
self.load(save_path)
def _build(self, midi_paths, seq_len=32):
self.seq_len = seq_len
self.notes_list, self.durations_list = ScoreDataset.makeMusicData(midi_paths)
notes_set = sorted(set(itertools.chain.from_iterable(self.notes_list))) # flatten 2D -> 1D, Unique, Sort
durations_set = sorted(set(itertools.chain.from_iterable(self.durations_list)))
self.note_to_index, self.index_to_note = ScoreDataset.createLookups(notes_set)
self.duration_to_index, self.index_to_duration = ScoreDataset.createLookups(durations_set)
self.c_notes = len(self.note_to_index)
self.c_durations = len(self.duration_to_index)
self.notes_index_list = ScoreDataset.convertIndex(self.notes_list, self.note_to_index)
self.durations_index_list = ScoreDataset.convertIndex(self.durations_list, self.duration_to_index)
self.n_music = len(self.notes_list)
self.index = 0
self._build_tbl()
@staticmethod
def extractMidi(midi_path):
notes, durations = [], []
score = music21.converter.parse(midi_path).chordify()
for element in score.flat:
if isinstance(element, music21.note.Note): # Note
if element.isRest: # Rest
notes.append(str(element.name)) # no pitch, then name only
else: # note with pitch
notes.append(str(element.nameWithOctave))
durations.append(element.duration.quaterLength) # 1/4 unit
if isinstance(element, music21.chord.Chord): # chord contains multiple notes
notes.append('.'.join(n.nameWithOctave for n in element.pitches)) # connect with '.'
durations.append(element.duration.quarterLength) # 1/4 unit
return notes, durations
@staticmethod
# [notes1, ..., notesN], [durations1, ..., durationsN]
def makeMusicData(midi_paths):
notes_list, durations_list = [], []
for path in midi_paths:
notes, durations = ScoreDataset.extractMidi(path) # notes, durations
notes_list.append(notes)
durations_list.append(durations)
return notes_list, durations_list
@staticmethod
def createLookups(names): # Lookup Table
element_to_index = dict((element, idx) for idx, element in enumerate(names))
index_to_element = dict((idx, element) for idx, element in enumerate(names))
return element_to_index, index_to_element
@staticmethod
def convertIndex(data, element_to_index):
return [ [ element_to_index[element] for element in x] for x in data]
def getMidiStream(self, g_notes, g_durations): # [note_index, ...], [duration_index, ...]
midi_stream = music21.stream.Stream()
for note_idx, duration_idx in zip(g_notes, g_durations):
note = self.index_to_note[note_idx]
duration = self.index_to_duration[duration_idx]
if ('.' in note): # chord
notes_in_chord = note.split('.')
chord_notes = []
for n_ in notes_in_chord:
new_note = music21.note.Note(n_)
new_note.duration = music21.duration.Duration(duration)
new_note.storeInstrument = music21.instrument.Violoncello()
chord_notes.append(new_note)
new_chord = music21.chord.Chord(chord_notes)
midi_stream.append(new_chord)
elif note == 'rest':
new_note = music21.note.Rest()
new_note.duration = music21.duration.Duration(duration)
new_note.storedInstrument = music21.instrument.Violoncello()
midi_stream.append(new_note)
else:
new_note = music21.note.Note(note)
new_note.duration = music21.duration.Duration(duration)
new_note.storedInstrument = music21.instrument.Violoncello()
midi_stream.append(new_note)
return midi_stream
def _build_tbl(self):
a = [ len(x)-self.seq_len for x in self.notes_list ] # [int, int, ...]
for i in range(1, len(a)): # cumulative frequency of data
a[i] = a[i-1] + a[i]
self.cumulative_freq = a
#print(f'cumulative_freq: {self.cumulative_freq}')
def searchTbl(self, index):
index = index % self.__len__()
low = 0
high = self.n_music - 1
for i in range(self.n_music):
mid = (low + high) // 2
#print(f'{i}/{self.n_music}: {high} {low} {mid} {index}')
if self.cumulative_freq[mid] > index:
if mid == 0 or self.cumulative_freq[mid-1] <= index:
return mid
high = mid - 1
else:
low = mid + 1
def __len__(self):
return self.cumulative_freq[-1]
def __getitem__(self, index):
if isinstance(index, slice):
start, stop, step = index.indices(self.__len__())
if start == None: start = 0
if stop == None: stop = self.__len__()
if step == None:
if start < stop:
step = 1
elif start > stop:
step = -1
else:
step = 0
return self.__getitemList__(range(start, stop, step))
elif isinstance(index, int):
return self.__getitemInt__(index)
else:
return self.__getitemList__(index)
def __getitemList__(self, indices):
x_notes, x_durations, y_notes, y_durations = [], [], [], []
for i in indices:
[x_note, x_duration], [y_note, y_duration] = self.__getitemInt__(i)
x_notes.append(x_note)
x_durations.append(x_duration)
y_notes.append(y_note)
y_durations.append(y_duration)
return (x_notes, x_durations), (y_notes, y_durations)
def __getitemInt__(self, index):
index = index % self.__len__()
#print(f'index = {index} {self.__len__()}')
tbl_idx = self.searchTbl(index)
#print(f'tbl_idx = {tbl_idx}')
tgt = index
if (tbl_idx > 0):
tgt -= self.cumulative_freq[tbl_idx - 1]
#print(f'tgt = {tgt}')
x_note = self.notes_index_list[tbl_idx][tgt: (tgt + self.seq_len)]
y_note = self.notes_index_list[tbl_idx][tgt + self.seq_len]
x_duration = self.durations_index_list[tbl_idx][tgt: (tgt + self.seq_len)]
y_duration = self.durations_index_list[tbl_idx][tgt + self.seq_len]
#ohv_y_note = tf.keras.utils.to_categorical(y_note, self.c_notes)
#ohv_y_duration = tf.keras.utils.to_categorical(y_duration, self.c_durations)
return (x_note, x_duration), (y_note, y_duration)
def __next__(self):
self.index += 1
return self.__getitem__(self.index-1)
def save(self, filepath):
dpath, fname = os.path.split(filepath)
if not os.path.exists(dpath):
os.makedirs(dpath)
with open(filepath, 'wb') as f:
pkl.dump([
self.seq_len,
self.notes_list,
self.durations_list,
self.note_to_index,
self.index_to_note,
self.duration_to_index,
self.index_to_duration,
self.c_notes,
self.c_durations,
self.notes_index_list,
self.durations_index_list,
self.n_music,
self.index,
self.cumulative_freq
], f)
def load(self, filepath):
with open(filepath, 'rb') as f:
params = pkl.load(f)
[
self.seq_len,
self.notes_list,
self.durations_list,
self.note_to_index,
self.index_to_note,
self.duration_to_index,
self.index_to_duration,
self.c_notes,
self.c_durations,
self.notes_index_list,
self.durations_index_list,
self.n_music,
self.index,
self.cumulative_freq
] = params
###################################################################################
# Model
###################################################################################
class LSTMMusic():
def __init__(self,
c_notes,
c_durations,
seq_len = 32,
optimizer='adam',
learning_rate = 0.001,
embed_size = 100,
rnn_units = 256,
use_attention = True,
epochs = 0,
losses = [],
n_losses = [],
d_losses = [],
val_losses = [],
val_n_losses = [],
val_d_losses = []
):
self.c_notes = c_notes
self.c_durations = c_durations
self.seq_len = seq_len
self.optimizer = optimizer
self.learning_rate = learning_rate
self.embed_size = embed_size
self.rnn_units = rnn_units
self.use_attention = use_attention
self.epochs = epochs
self.losses = losses
self.n_losses = n_losses
self.d_losses = d_losses
self.val_losses = val_losses
self.val_n_losses = val_n_losses
self.val_d_losses = val_d_losses
self.model, self.att_model = self._create_network(c_notes, c_durations, embed_size, rnn_units, use_attention)
self.cce1 = tf.keras.losses.CategoricalCrossentropy(),
self.cce2 = tf.keras.losses.CategoricalCrossentropy(),
def _create_network(
self,
n_notes,
n_durations,
embed_size,
rnn_units,
use_attention
):
notes_in = tf.keras.layers.Input(shape=(None,))
durations_in = tf.keras.layers.Input(shape=(None,))
x1 = tf.keras.layers.Embedding(n_notes, embed_size)(notes_in)
x2 = tf.keras.layers.Embedding(n_durations, embed_size)(durations_in)
x = tf.keras.layers.Concatenate()([x1, x2])
x = tf.keras.layers.LSTM(rnn_units, return_sequences=True)(x)
# x = tf.keras.layers.Dropout(0.2)(x)
if use_attention:
x = tf.keras.layers.LSTM(rnn_units, return_sequences=True)(x)
# x = tf.keras.layers.Dropout(0.2)(x)
e = tf.keras.layers.Dense(1, activation='tanh')(x)
e = tf.keras.layers.Reshape([-1])(e) # batch_size * N
alpha = tf.keras.layers.Activation('softmax')(e)
alpha_repeated = tf.keras.layers.Permute([2,1])(tf.keras.layers.RepeatVector(rnn_units)(alpha))
c = tf.keras.layers.Multiply()([x, alpha_repeated])
c = tf.keras.layers.Lambda(lambda xin: tf.keras.backend.sum(xin, axis=1), output_shape=(rnn_units,))(c)
else:
c = tf.keras.layers.LSTM(rnn_units)(x)
#c = tf.keras.layers.Dropout(0.2)(c)
notes_out = tf.keras.layers.Dense(n_notes, activation='softmax', name='pitch')(c)
durations_out = tf.keras.layers.Dense(n_durations, activation='softmax', name='duration')(c)
model = tf.keras.models.Model([notes_in, durations_in], [notes_out, durations_out])
if use_attention:
att_model = tf.keras.models.Model([notes_in, durations_in], alpha)
else:
att_model = None
return model, att_model
def get_opti(self, learning_rate):
if self.optimizer == 'adam':
opti = tf.keras.optimizers.Adam(learning_rate=learning_rate, beta_1 = 0.5)
elif self.optimizer == 'rmsprop':
opti = tf.keras.optimizers.RMSprop(learning_rate=learning_rate)
else:
opti = tf.keras.optimizers.Adam(learning_rate=learning_rate)
return opti
def _compile(self):
opti = self.get_opti(self.learning_rate)
self.model.compile(
loss=[
self.cce1,
self.cce2
],
optimizer = tf.keras.optimizers.RMSprop(learning_rate = self.learning_rate)
)
def train_with_fit(
self,
xs, # (x_notes,x_durations)
ys, # (y_notes, y_durations)
epochs=1,
batch_size=32,
run_folder='run/',
shuffle=False,
validation_data = None
):
history = self.model.fit(
xs,
ys,
initial_epoch = self.epochs,
epochs = epochs,
batch_size = batch_size,
shuffle = shuffle,
validation_data = validation_data
)
self.epochs = epochs
h = history.history
self.losses += h['loss']
self.n_losses += h['pitch_loss']
self.d_losses += h['duration_loss']
if not validation_data is None:
self.val_losses += h['val_loss']
self.val_n_losses += h['val_pitch_loss']
self.val_d_losses += h['val_duration_loss']
self.save(run_folder)
self.save(run_folder, self.epochs)
if validation_data is None:
return self.losses, self.n_losses, self.d_losses
else:
return self.losses, self.n_losses, self.d_losses, self.val_losses, self.val_n_losses, self.val_d_losses,
@tf.function
def loss_fn(self, y_notes, y_durations, p_notes, p_durations):
#print(y_notes.shape, p_notes.shape, y_durations.shape, p_durations.shape)
#y_notes = np.array(y_notes, dtype='float32')
#p_notes = np.array(p_notes, dtype='float32')
n_loss = tf.keras.losses.CategoricalCrossentropy()(y_notes, p_notes)
d_loss = tf.keras.losses.CategoricalCrossentropy()(y_durations, p_durations)
#n_loss = self.cce1(y_notes, p_notes)
#d_loss = self.cce2(y_durations, p_durations)
loss = tf.add(n_loss, d_loss)
return loss, n_loss, d_loss
@tf.function
def train_step(
self,
x_notes,
x_durations,
y_notes,
y_durations,
optimizer
):
with tf.GradientTape() as tape:
p_notes, p_durations = self.model([x_notes, x_durations])
loss, note_loss, duration_loss = self.loss_fn(y_notes, y_durations, p_notes, p_durations)
variables = self.model.trainable_variables
gradients = tape.gradient(loss, variables)
optimizer.apply_gradients(zip(gradients, variables))
return loss, note_loss, duration_loss
def train(
self,
xs,
ys,
epochs=1,
batch_size=32,
optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
shuffle=False,
run_folder='run/',
print_step_interval = 100,
save_epoch_interval = 100,
validation_data = None
):
start_time = datetime.datetime.now()
x_notes_all, x_durations_all = xs
y_notes_all, y_durations_all = ys
steps = len(x_notes_all) // batch_size
for epoch in range(self.epochs, epochs):
indices = tf.range(len(x_notes_all), dtype=tf.int32)
if shuffle:
indices = tf.random.shuffle(indices)
step_losses, step_n_losses, step_d_losses = [], [], []
for step in range(steps):
start = batch_size * step
end = start + batch_size
idxs = indices[start:end]
x_notes = x_notes_all[idxs]
x_durations = x_durations_all[idxs]
y_notes = np.array(y_notes_all[idxs])
y_durations = np.array(y_durations_all[idxs])
n_ = np.array(x_notes, dtype='float32')
d_ = np.array(x_durations, dtype='float32')
batch_loss, batch_n_loss, batch_d_loss = self.train_step(
n_,
d_,
y_notes,
y_durations,
optimizer
)
step_losses.append(np.mean(batch_loss))
step_n_losses.append(np.mean(batch_n_loss))
step_d_losses.append(np.mean(batch_d_loss))
elapsed_time = datetime.datetime.now() - start_time
if (step+1) % print_step_interval == 0:
print(f'{epoch+1}/{epochs} {step+1}/{steps} loss {step_losses[-1]:.3f} pitch_loss {step_n_losses[-1]:.3f} duration_loss {step_d_losses[-1]:.3f} {elapsed_time}')
epoch_loss = np.mean(step_losses)
epoch_n_loss = np.mean(step_n_losses)
epoch_d_loss = np.mean(step_d_losses)
self.losses.append(epoch_loss)
self.n_losses.append(epoch_n_loss)
self.d_losses.append(epoch_d_loss)
val_str = ''
if not validation_data is None:
(val_x_notes, val_x_durations), (val_y_notes, val_y_durations) = validation_data
p_notes, p_durations = self.model([val_x_notes, val_x_durations])
val_loss, val_n_loss, val_d_loss = self.loss_fn(
val_y_notes,
val_y_durations,
p_notes,
p_durations
)
val_loss = np.mean(val_loss)
val_n_loss = np.mean(val_n_loss)
val_d_loss = np.mean(val_d_loss)
self.val_losses.append(val_loss)
self.val_n_losses.append(val_n_loss)
self.val_d_losses.append(val_d_loss)
val_str = f'val_loss {val_loss:.3f} val_pitch_loss {val_n_loss:.3f} val_duration_loss {val_d_loss:.3f}'
self.epochs += 1
elapsed_time = datetime.datetime.now() - start_time
print(f'{self.epochs}/{epochs} loss {epoch_loss:.3f} pitch_loss {epoch_n_loss:.3f} duration_loss {epoch_d_loss:.3f} {val_str} {elapsed_time}')
if self.epochs % save_epoch_interval == 0:
self.save(run_folder)
self.save(run_folder, self.epochs)
self.save(run_folder)
self.save(run_folder, self.epochs)
return self.losses, self.n_losses, self.d_losses, self.val_losses, self.val_n_losses, self.val_d_losses
@staticmethod
def sample_with_temperature(preds, temperature):
if temperature == 0:
return np.argmax(preds)
else:
preds = np.log(preds) / temperature
exp_preds = np.exp(preds)
preds = exp_preds / np.sum(exp_preds)
return np.random.choice(len(preds), p=preds)
def generate(self, s_notes, s_durations, count=64, note_temperature = 0.5, duration_temperature = 0.5):
x_notes = np.array([s_notes], dtype='float32')
x_durations = np.array([s_durations], dtype='float32')
g_notes, g_durations = [], []
for i in range(count):
p_notes, p_durations = self.model([x_notes, x_durations])
note = LSTMMusic.sample_with_temperature(p_notes[0], note_temperature)
duration = LSTMMusic.sample_with_temperature(p_durations[0], duration_temperature)
g_notes.append(note)
g_durations.append(duration)
x_notes = np.roll(x_notes, -1)
x_durations = np.roll(x_durations, -1)
x_notes[0,-1] = note
x_durations[0,-1] = duration
return g_notes, g_durations # [note_index, ...], [duration_index, ...]
def save(self, folder, epoch=None):
self.save_params(folder, epoch)
self.save_weights(folder, epoch)
@staticmethod
def load(folder, epoch=None):
params = LSTMMusic.load_params(folder, epoch)
music = LSTMMusic(*params)
music.load_weights(folder, epoch)
return music
def save_weights(self, run_folder, epoch=None):
if epoch is None:
self.save_model_weights(self.model, os.path.join(run_folder, 'weights/weights.h5'))
self.save_model_weights(self.att_model, os.path.join(run_folder, 'weights/weights_att.h5'))
else:
self.save_model_weights(self.model, os.path.join(run_folder, f'weights/weights_{epoch}.h5'))
self.save_model_weights(self.att_model, os.path.join(run_folder, f'weights/weights_att_{epoch}.h5'))
def load_weights(self, run_folder, epoch=None):
if epoch is None:
self.load_model_weights(self.model, os.path.join(run_folder, 'weights/weights.h5'))
self.load_model_weights(self.att_model, os.path.join(run_folder, 'weights/weights_att.h5'))
else:
self.load_model_weights(self.model, os.path.join(run_folder, f'weights/weights_{epoch}.h5'))
self.load_model_weights(self.att_model, os.path.join(run_folder, f'weights/weights_att_{epoch}.h5'))
def save_model_weights(self, model, filepath):
dpath, fname = os.path.split(filepath)
if dpath != '' and not os.path.exists(dpath):
os.makedirs(dpath)
model.save_weights(filepath)
def load_model_weights(self, model, filepath):
model.load_weights(filepath)
def save_params(self, folder, epoch=None):
if not os.path.exists(folder):
os.makedirs(folder)
if epoch is None:
filepath = os.path.join(folder, 'params.pkl')
else:
filepath = os.path.join(folder, f'params_{epoch}.pkl')
with open(filepath, 'wb') as f:
pkl.dump([
self.c_notes,
self.c_durations,
self.seq_len,
self.optimizer,
self.learning_rate,
self.embed_size,
self.rnn_units,
self.use_attention,
self.epochs,
self.losses,
self.n_losses,
self.d_losses,
self.val_losses,
self.val_n_losses,
self.val_d_losses
], f)
@staticmethod
def load_params(folder, epoch=None):
if epoch is None:
filepath = os.path.join(folder, 'params.pkl')
else:
filepath = os.path.join(folder, f'params_{epoch}.pkl')
with open(filepath, 'rb') as f:
params = pkl.load(f)
return params
@staticmethod
def plot_history(vals, labels):
colors = ['red', 'blue', 'green', 'black', 'orange', 'pink', 'purple', 'olive', 'cyan']
n = len(vals)
fig, ax = plt.subplots(1, 1, figsize=(12,6))
for i in range(n):
ax.plot(vals[i], c=colors[i], label=labels[i])
ax.legend(loc='upper right')
ax.set_xlabel('epochs')
plt.show()
In [10]:
import sys
sys.path.append('./nw')
from LSTMMusic import ScoreDataset
if not os.path.exists(data_filepath):
data_seq = ScoreDataset(save_path=data_filepath, midi_paths=midi_paths, seq_len=32)
else:
data_seq = ScoreDataset(save_path=data_filepath)
In [11]:
import sys
sys.path.append('./nw')
from LSTMMusic import LSTMMusic
lstm_music = LSTMMusic(
data_seq.c_notes,
data_seq.c_durations
)
In [22]:
lstm_music.model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
embedding (Embedding) (None, None, 100) 46000 input_1[0][0]
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, None, 100) 1800 input_2[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, None, 200) 0 embedding[0][0]
embedding_1[0][0]
__________________________________________________________________________________________________
lstm (LSTM) (None, None, 256) 467968 concatenate[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (None, None, 256) 525312 lstm[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, None, 1) 257 lstm_1[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, None) 0 dense[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, None) 0 reshape[0][0]
__________________________________________________________________________________________________
repeat_vector (RepeatVector) (None, 256, None) 0 activation[0][0]
__________________________________________________________________________________________________
permute (Permute) (None, None, 256) 0 repeat_vector[0][0]
__________________________________________________________________________________________________
multiply (Multiply) (None, None, 256) 0 lstm_1[0][0]
permute[0][0]
__________________________________________________________________________________________________
lambda (Lambda) (None, 256) 0 multiply[0][0]
__________________________________________________________________________________________________
pitch (Dense) (None, 460) 118220 lambda[0][0]
__________________________________________________________________________________________________
duration (Dense) (None, 18) 4626 lambda[0][0]
==================================================================================================
Total params: 1,164,183
Trainable params: 1,164,183
Non-trainable params: 0
__________________________________________________________________________________________________
In [23]:
lstm_music.att_model.summary()
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
input_2 (InputLayer) [(None, None)] 0
__________________________________________________________________________________________________
embedding (Embedding) (None, None, 100) 46000 input_1[0][0]
__________________________________________________________________________________________________
embedding_1 (Embedding) (None, None, 100) 1800 input_2[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, None, 200) 0 embedding[0][0]
embedding_1[0][0]
__________________________________________________________________________________________________
lstm (LSTM) (None, None, 256) 467968 concatenate[0][0]
__________________________________________________________________________________________________
lstm_1 (LSTM) (None, None, 256) 525312 lstm[0][0]
__________________________________________________________________________________________________
dense (Dense) (None, None, 1) 257 lstm_1[0][0]
__________________________________________________________________________________________________
reshape (Reshape) (None, None) 0 dense[0][0]
__________________________________________________________________________________________________
activation (Activation) (None, None) 0 reshape[0][0]
==================================================================================================
Total params: 1,041,337
Trainable params: 1,041,337
Non-trainable params: 0
__________________________________________________________________________________________________
In [12]:
# split data into train and val data.
import numpy as np
val_split = 0.05
N_DATA = len(data_seq)
N_VAL = int(N_DATA * val_split)
arr = np.arange(N_DATA)
np.random.shuffle(arr)
train_indices = sorted(arr[:-N_VAL])
val_indices = sorted(arr[-N_VAL:])
(train_x_notes, train_x_durations), (train_y_notes, train_y_durations) = data_seq[train_indices]
(val_x_notes, val_x_durations), (val_y_notes, val_y_durations) = data_seq[val_indices]
print(len(train_indices), len(val_indices))
25184 1325
In [13]:
print(train_indices[:20])
print(val_indices[:20])
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 17, 18, 19, 20] [16, 30, 38, 48, 57, 61, 65, 121, 150, 155, 206, 211, 213, 309, 311, 339, 352, 389, 397, 429]
In [14]:
train_x_notes = np.array(train_x_notes)
train_x_durations = np.array(train_x_durations)
val_x_notes = np.array(val_x_notes)
val_x_durations = np.array(val_x_durations)
In [15]:
import tensorflow as tf
train_y_notes_ohv = tf.keras.utils.to_categorical(train_y_notes, data_seq.c_notes)
train_y_durations_ohv = tf.keras.utils.to_categorical(train_y_durations, data_seq.c_durations)
val_y_notes_ohv = tf.keras.utils.to_categorical(val_y_notes, data_seq.c_notes)
val_y_durations_ohv = tf.keras.utils.to_categorical(val_y_durations, data_seq.c_durations)
In [16]:
save_path = 'run'
In [17]:
lstm_music._compile()
h = lstm_music.train_with_fit(
(train_x_notes, train_x_durations),
(train_y_notes_ohv, train_y_durations_ohv),
epochs=1,
batch_size=32,
run_folder = save_path,
validation_data = ((val_x_notes, val_x_durations), (val_y_notes_ohv, val_y_durations_ohv))
)
787/787 [==============================] - 7s 9ms/step - loss: 4.3482 - pitch_loss: 3.5334 - duration_loss: 0.8148 - val_loss: 6.1381 - val_pitch_loss: 4.5460 - val_duration_loss: 1.5922
In [18]:
h = lstm_music.train(
(train_x_notes, train_x_durations),
(train_y_notes_ohv, train_y_durations_ohv),
epochs=2,
batch_size=32,
run_folder = save_path,
print_step_interval=300,
validation_data = ((val_x_notes, val_x_durations), (val_y_notes_ohv, val_y_durations_ohv))
)
2/2 300/787 loss 3.889 pitch_loss 2.942 duration_loss 0.947 0:00:04.748380 2/2 600/787 loss 3.419 pitch_loss 2.823 duration_loss 0.595 0:00:07.178785 2/2 loss 4.427 pitch_loss 3.568 duration_loss 0.860 val_loss 5.513 val_pitch_loss 4.372 val_duration_loss 1.141 0:00:08.715082
In [19]:
h = lstm_music.train(
(train_x_notes, train_x_durations),
(train_y_notes_ohv, train_y_durations_ohv),
epochs=5,
batch_size=32,
run_folder = save_path,
print_step_interval=1000,
validation_data = ((val_x_notes, val_x_durations), (val_y_notes_ohv, val_y_durations_ohv))
)
3/5 loss 4.274 pitch_loss 3.473 duration_loss 0.802 val_loss 5.688 val_pitch_loss 4.479 val_duration_loss 1.209 0:00:06.292834 4/5 loss 4.197 pitch_loss 3.428 duration_loss 0.769 val_loss 5.219 val_pitch_loss 4.314 val_duration_loss 0.905 0:00:12.556787 5/5 loss 4.142 pitch_loss 3.408 duration_loss 0.734 val_loss 5.211 val_pitch_loss 4.323 val_duration_loss 0.889 0:00:18.786610
In [20]:
! {LS_CMD} {save_path}
ドライブ G のボリューム ラベルは nitta@gm.tsuda.ac.jp - Google... です
ボリューム シリアル番号は 1983-1116 です
G:\マイドライブ\DeepLearning\book14\ch07.self\run のディレクトリ
2022/01/13 09:25 <DIR> .
2021/12/20 11:19 <DIR> ..
2021/12/18 12:03 762,452 music_params.pkl
2022/01/13 09:25 651 params.pkl
2022/01/13 09:25 <DIR> weights
2022/01/13 09:24 121 params_1.pkl
2022/01/13 09:25 327 params_2.pkl
2022/01/13 09:25 651 params_5.pkl
2021/12/18 21:05 1,191 params_10.pkl
2021/12/18 21:52 627 output_0_10.mid
2021/12/18 21:33 611 output_1_10.mid
2021/12/18 21:33 611 output_2_10.mid
2021/12/18 21:46 7,434 params_50.pkl
2021/12/18 21:54 611 output_0_50.mid
2021/12/18 21:49 611 output_1_50.mid
2021/12/18 21:49 611 output_2_50.mid
2021/12/18 21:55 15,534 params_100.pkl
2021/12/18 22:02 23,634 params_150.pkl
2021/12/18 22:10 611 output_0_150.mid
2021/12/18 22:10 611 output_1_150.mid
2021/12/18 22:10 611 output_2_150.mid
2021/12/18 22:10 31,734 params_200.pkl
2021/12/18 22:18 39,834 params_250.pkl
2021/12/18 22:25 47,935 params_300.pkl
2021/12/18 22:33 56,035 params_350.pkl
2021/12/18 22:49 611 output_0_350.mid
2021/12/18 22:37 611 output_1_350.mid
2021/12/18 22:37 611 output_2_350.mid
2021/12/18 22:41 64,135 params_400.pkl
2021/12/18 22:48 72,235 params_450.pkl
2021/12/18 22:51 611 output_0_450.mid
2021/12/18 22:56 80,335 params_500.pkl
2021/12/18 22:57 611 output_0_500.mid
2021/12/18 22:57 611 output_1_500.mid
2021/12/18 22:57 611 output_2_500.mid
2021/12/18 23:04 88,435 params_550.pkl
2021/12/18 23:07 611 output_0_550.mid
2021/12/18 23:08 611 output_1_550.mid
2021/12/18 23:08 611 output_2_550.mid
2021/12/18 23:11 96,535 params_600.pkl
2021/12/18 23:11 611 output_0_600.mid
2021/12/18 23:11 611 output_1_600.mid
2021/12/18 23:11 611 output_2_600.mid
2021/12/18 23:19 104,635 params_650.pkl
2021/12/18 23:26 112,735 params_700.pkl
2021/12/18 23:34 120,835 params_750.pkl
2021/12/18 23:41 128,935 params_800.pkl
2021/12/20 10:33 611 output_0_800.mid
2021/12/20 10:33 611 output_1_800.mid
2021/12/20 10:33 627 output_2_800.mid
47 個のファイル 1,871,655 バイト
G:\マイドライブ\DeepLearning\book14\ch07.self\run\weights のディレクトリ
2022/01/13 09:25 <DIR> .
2022/01/13 09:25 <DIR> ..
2022/01/13 09:25 4,693,472 weights.h5
2022/01/13 09:25 4,193,232 weights_att.h5
2022/01/13 09:24 4,693,472 weights_1.h5
2022/01/13 09:24 4,193,232 weights_att_1.h5
2022/01/13 09:25 4,693,472 weights_2.h5
2022/01/13 09:25 4,193,232 weights_att_2.h5
2022/01/13 09:25 4,693,472 weights_5.h5
2022/01/13 09:25 4,193,232 weights_att_5.h5
2021/12/18 21:05 4,693,480 weights_10.h5
2021/12/18 21:05 4,193,280 weights_att_10.h5
2021/12/18 21:46 4,693,472 weights_50.h5
2021/12/18 21:46 4,193,232 weights_att_50.h5
2021/12/18 21:55 4,693,472 weights_100.h5
2021/12/18 21:55 4,193,232 weights_att_100.h5
2021/12/18 22:02 4,693,472 weights_150.h5
2021/12/18 22:02 4,193,232 weights_att_150.h5
2021/12/18 22:10 4,693,472 weights_200.h5
2021/12/18 22:10 4,193,232 weights_att_200.h5
2021/12/18 22:18 4,693,472 weights_250.h5
2021/12/18 22:18 4,193,232 weights_att_250.h5
2021/12/18 22:25 4,693,472 weights_300.h5
2021/12/18 22:25 4,193,232 weights_att_300.h5
2021/12/18 22:33 4,693,472 weights_350.h5
2021/12/18 22:33 4,193,232 weights_att_350.h5
2021/12/18 22:41 4,693,472 weights_400.h5
2021/12/18 22:41 4,193,232 weights_att_400.h5
2021/12/18 22:48 4,693,472 weights_450.h5
2021/12/18 22:48 4,193,232 weights_att_450.h5
2021/12/18 22:56 4,693,472 weights_500.h5
2021/12/18 22:56 4,193,232 weights_att_500.h5
2021/12/18 23:04 4,693,472 weights_550.h5
2021/12/18 23:04 4,193,232 weights_att_550.h5
2021/12/18 23:11 4,693,472 weights_600.h5
2021/12/18 23:11 4,193,232 weights_att_600.h5
2021/12/18 23:19 4,693,472 weights_650.h5
2021/12/18 23:19 4,193,232 weights_att_650.h5
2021/12/18 23:26 4,693,472 weights_700.h5
2021/12/18 23:26 4,193,232 weights_att_700.h5
2021/12/18 23:34 4,693,472 weights_750.h5
2021/12/18 23:34 4,193,232 weights_att_750.h5
2021/12/18 23:41 4,693,472 weights_800.h5
2021/12/18 23:41 4,193,232 weights_att_800.h5
42 個のファイル 186,620,840 バイト
ファイルの総数:
89 個のファイル 188,492,495 バイト
5 個のディレクトリ 106,950,303,744 バイトの空き領域
In [21]:
%matplotlib inline
import matplotlib.pyplot as plt
LSTMMusic.plot_history(
[
lstm_music.losses, lstm_music.n_losses, lstm_music.d_losses,
lstm_music.val_losses, lstm_music.val_n_losses, lstm_music.val_d_losses
],
[
'loss', 'pitch_loss', 'duration_loss', 'val_loss', 'val_pitch_loss', 'val_duration_loss'
]
)
In [21]:
lstm_music2 = LSTMMusic.load(save_path)
print(lstm_music2.epochs)
5
In [22]:
h=lstm_music2.train(
(train_x_notes, train_x_durations),
(train_y_notes_ohv, train_y_durations_ohv),
epochs=10,
batch_size=32,
run_folder = save_path,
print_step_interval=1000,
validation_data = ((val_x_notes, val_x_durations), (val_y_notes_ohv, val_y_durations_ohv))
)
6/10 loss 4.278 pitch_loss 3.496 duration_loss 0.782 val_loss 4.873 val_pitch_loss 3.975 val_duration_loss 0.898 0:00:11.048121 7/10 loss 4.089 pitch_loss 3.376 duration_loss 0.713 val_loss 4.705 val_pitch_loss 3.898 val_duration_loss 0.807 0:00:25.836999 8/10 loss 3.983 pitch_loss 3.316 duration_loss 0.667 val_loss 4.600 val_pitch_loss 3.832 val_duration_loss 0.768 0:00:41.062060 9/10 loss 3.973 pitch_loss 3.308 duration_loss 0.665 val_loss 4.449 val_pitch_loss 3.697 val_duration_loss 0.752 0:00:49.064355 10/10 loss 3.887 pitch_loss 3.253 duration_loss 0.635 val_loss 4.450 val_pitch_loss 3.702 val_duration_loss 0.748 0:00:57.467114
In [23]:
%matplotlib inline
import matplotlib.pyplot as plt
LSTMMusic.plot_history(
[
lstm_music2.losses, lstm_music2.n_losses, lstm_music2.d_losses,
lstm_music2.val_losses, lstm_music2.val_n_losses, lstm_music2.val_d_losses
],
[
'loss', 'pitch_loss', 'duration_loss',
'val_loss', 'val_pitch_loss', 'val_duration_loss'
]
)
In [ ]: