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chord_model.py
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chord_model.py
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from settings import *
from keras.models import load_model
import keras
import numpy as np
from numpy import array
import _pickle as pickle
from keras import backend as K
from data_processing import get_chord_dict
class Chord_Model:
def __init__(self,
model_path,
prediction_mode='sampling',
first_chords=[1,3,2,1,1,3,2,1],
resample='none',
dim_factor=2,
temperature=1.0):
print('loading chord model ...')
self.model = keras.models.load_model(model_path)
self.model.reset_states()
self.embed_layer_output = K.function([self.model.layers[0].input], [self.model.layers[0].output])
self.embed_model = keras.models.Model(inputs=self.model.input,outputs=self.model.get_layer(name="embedding").output)
self.chord_to_index, self.index_to_chords = get_chord_dict()
self.prediction_mode = prediction_mode
self.temperature = temperature
self.resample = resample
self.dim_factor = dim_factor
self.song = []
for chord in first_chords[:-1]:
# print(chord)
self.model.predict(array([[chord]]))
self.song.append(chord)
chord = first_chords[-1]
self.song.append(chord)
self.current_chord = array([[chord]])
def predict_next(self):
prediction = self.model.predict(self.current_chord)[0]
if self.resample=='hard':
prediction[self.current_chord] = 0
prediction = prediction/np.sum(prediction)
elif self.resample=='soft':
prediction[self.current_chord] /= self.dim_factor
prediction = prediction/np.sum(prediction)
# print(prediction)
prediction = np.log(prediction) / self.temperature
prediction = np.exp(prediction) / np.sum(np.exp(prediction))
if self.prediction_mode == 'argmax':
# print('argmax')
while True:
next_chord = np.argmax(prediction)
if next_chord !=0:
break
# print(next_chord)
elif self.prediction_mode == 'sampling':
while True:
next_chord = np.random.choice(len(prediction), p=prediction)
# print(next_chord)
if next_chord !=0:
break
# print(next_chord)
self.song.append(next_chord)
self.current_chord = np.array([next_chord])
return self.current_chord[0]
def embed_chord(self, chord):
return self.embed_layer_output([[[chord]]])[0][0][0]
def embed_chords_song(self, chords):
embeded_chords = []
for chord in chords:
embeded_chords.append(self.embed_chord(chord))
return embeded_chords
class Embed_Chord_Model:
def __init__(self, model_path):
print('loading chord model ...')
model = keras.models.load_model(model_path)
model.reset_states()
self.embed_layer_output = K.function([model.layers[0].input], [model.layers[0].output])
self.chord_to_index, self.index_to_chords = get_chord_dict()
def embed_chord(self, chord):
return self.embed_layer_output([[[chord]]])[0][0][0]
def embed_chords_song(self, chords):
embeded_chords = []
for chord in chords:
embeded_chords.append(self.embed_chord(chord))
return embeded_chords
if __name__=="__main__":
# Paths:
model_folder = 'models/chords/standart_lr_0.00003/'
model_name = 'modelEpoch10'
model = Chord_Model(model_folder + model_name + '.h5', prediction_mode='sampling')
for i in range(0, 16):
model.predict_next()
print(model.song)