x = to_categorical(x.flatten(), num_classes=DIM).\
reshape(x.shape[0], x.shape[1], DIM)
y = y.reshape(x.shape[0], x.shape[1], 1)
yield (x, y)
# model
model = StackedRNN(
timespan=LEN,
input_dim=DIM,
output_dim=DIM,
cells=[512, 512, 512],
)
# block_kwargs={'kernel_regularizer': l2(1e-5)})
model.build()
# model.model.load_weights('temp/stackedrnn_kernel_l2.h5')
# model.model.load_weights('temp/simple_rnn.h5')
# model.train(data_generator(),
# opt=1e-5,
# steps_per_epoch=30,
# epochs=100,
# save_path='temp/simple_rnn.h5')
model.build_generator('temp/e-comp_simple_rnn.h5')
res = model.generate(seed=32, length=2000)
mid = Song()
track = mid.add_track()
for msgi in AllInOneCoder().decode(res):
track.append(msgi)
mid.save_as('simple_rnn.mid')
import numpy as np
from DeepSymphony.utils.bleu import bleu
from DeepSymphony.models import StackedRNN
from keras.utils.np_utils import to_categorical
from DeepSymphony.coders import AllInOneCoder
from keras.models import load_model
from keras.layers import Embedding
PREFIX = 5
ANSWER = 50
LEN = PREFIX + ANSWER
TEST = 100
DIM = 128 + 128 + 100 + 7
coder = AllInOneCoder()
RNG = np.random.RandomState(64)
def tokenize(x):
if x.ndim == 1:
return map(coder.code_to_name, x)
elif x.ndim == 2:
return map(coder.code_to_name, x.argmax(1))
def test_emb_stackrnn(data, weight='temp/emb_stackrnn.h5'):
emb_w = load_model('temp/emb.h5').layers[2].layers[1].get_weights()
model = StackedRNN(timespan=LEN,
input_dim=DIM,
output_dim=DIM,
cells=[512, 512, 512],
x.append(np.array(pairs))
y.append(np.array(labels))
x = np.array(x)
y = np.array(y)
yield ([x[:, :, 0], x[:, :, 1]], y)
(x1, x2), sim = data_generator().next()
model = EmbeddingModel(input_dim=DIM, output_dim=512, seq_len=x1.shape[1])
# model.train(data_generator(),
# save_path='/tmp/emb.h5',
# steps_per_epoch=50,
# epochs=1000)
coder = AllInOneCoder()
x = np.arange(DIM).reshape(-1, 1)
gen = model.build_generator('temp/emb.h5')
res = model.generate(x).reshape(DIM, -1)
pca = decomposition.PCA(n_components=2, )
tsne = manifold.TSNE(n_components=2, )
plot_embedding(tsne.fit_transform(res), map(coder.code_to_name,
x.flatten()))
plt.show()
print res.shape
# while True:
# ind = np.random.randint(DIM)
for ind in range(DIM):
print('Most similar notes with {}'.format(coder.code_to_name(ind)))
from DeepSymphony.coders import AllInOneCoder
from DeepSymphony.models import StackedRNN
from DeepSymphony.utils import Song
from DeepSymphony.utils.stat import LCS
from tqdm import tqdm
from pprint import pprint
from keras import optimizers
if __name__ == '__main__':
LEN = 100 # length of input
DIM = 128+128+100+7
data, filelist = Song.load_from_dir(
"./datasets/easymusicnotes/",
encoder=AllInOneCoder(return_indices=True),
return_list=True)
def data_generator():
batch_size = 32
while True:
x = []
y = []
for _ in range(batch_size):
ind = np.random.randint(len(data))
start = np.random.randint(data[ind].shape[0]-LEN-1)
x.append(data[ind][start:start+LEN])
y.append(data[ind][start+1:start+LEN+1])
x = np.array(x)
y = np.array(y)
import numpy as np
from keras.utils.np_utils import to_categorical
from DeepSymphony.coders import AllInOneCoder
from DeepSymphony.models import StackedRNN
from DeepSymphony.utils import Song
from DeepSymphony.utils.constants import NOTE_NUMBER
from DeepSymphony.utils.stat import histogram, histogram_onehot,\
min_norm, norm
if __name__ == '__main__':
LEN = 2000 # length of input
DIM_IN = 128 + 128 + 100 + 7 + len(NOTE_NUMBER)
DIM_OUT = 128 + 128 + 100 + 7
coder = AllInOneCoder()
H_TEMP = 0.05
data = np.load('./datasets/e-comp-allinone.npz')['data']
hist = np.load('./datasets/e-comp-allinone-hist.npz')['hist']
def data_generator():
batch_size = 32
while True:
x = []
y = []
h = []
for _ in range(batch_size):
ind = np.random.randint(len(data))
start = np.random.randint(data[ind].shape[0] - LEN - 1)
x.append(data[ind][start:start + LEN])
y.append(data[ind][start + 1:start + LEN + 1])
import numpy as np
from keras.utils.np_utils import to_categorical
from DeepSymphony.coders import AllInOneCoder
from DeepSymphony.models import StackedRNN
from DeepSymphony.utils import Song
from DeepSymphony.utils.constants import NOTE_NUMBER
from DeepSymphony.utils.stat import histogram, histogram_onehot,\
min_norm, norm
if __name__ == '__main__':
LEN = 100 # length of input
DIM_IN = 128 + 128 + 100 + 7 + len(NOTE_NUMBER)
DIM_OUT = 128 + 128 + 100 + 7
coder = AllInOneCoder()
H_TEMP = 0.05
data, filelist = Song.load_from_dir(
"./datasets/easymusicnotes/",
encoder=AllInOneCoder(return_indices=True),
return_list=True)
hist = np.load('./datasets/e-comp-allinone-hist.npz')['hist']
def data_generator():
batch_size = 32
while True:
x = []
y = []
h = []
for _ in range(batch_size):
ind = np.random.randint(len(data))
from DeepSymphony.utils import Song
from DeepSymphony.utils.stat import LCS
from tqdm import tqdm
from pprint import pprint
from DeepSymphony.coders import AllInOneCoder
import numpy as np
import matplotlib.pyplot as plt
if __name__ == '__main__':
coder = AllInOneCoder()
song = Song('masterpiece/0035.mid')
song = coder.encode(song.midi)
data, filelist = Song.load_from_dir(
"./datasets/easymusicnotes/",
encoder=AllInOneCoder(return_indices=True),
return_list=True)
# LCS check
song = filter(lambda x: x < 128, song.argmax(1))
print len(song)
matches = []
for ind, ele in tqdm(enumerate(data)):
ele = filter(lambda x: x < 128, ele)
matches.append((LCS(ele, song, return_seq=True), filelist[ind]))
matches = sorted(matches, key=lambda x: x[0][0])[::-1]
# pprint(sorted(matches)[::-1])
for (count, lcs, p, q), filename in matches:
print count