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lstm_pop.py
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lstm_pop.py
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from music21 import*
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import LSTM
from keras.layers import Activation
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
import utildata as ud
import numpy as np
dataset = ud.get_melody(ud.loadobj('pop_dataset'))
encoding_dict = ud.loadobj('encoding')
num_timesteps = 16
#Input and output sequences of dataset
def get_sequences(dataset):
input_seq = []
output_seq = []
#Traverse each song in dataset
for song in dataset:
#Traverse each note (encoded) to form num_timesteps of note
for note_index in range(0,len(song)-num_timesteps,1):
input_seq.append(song[note_index:note_index+num_timesteps])
output_seq.append(song[note_index+num_timesteps])
input_seq,output_seq = transform_sequences(input_seq,output_seq)
return input_seq,output_seq
#Normalize Input and transform output sequences into categorical form(one-hot encoding)
def transform_sequences(input_seq,output_seq):
num = len(input_seq)
input_seq = np.reshape(input_seq,(num,num_timesteps,1))
input_seq = input_seq/float(len(encoding_dict))
output_seq = np_utils.to_categorical(output_seq)
return input_seq,output_seq
def train_network():
input_seq,output_seq = get_sequences(dataset)
#model = create_network(input_seq,output_seq.shape[1])
#train(model,input_seq,output_seq)
print(output_seq.shape[1])
def create_network(network_input,output_size):
model = Sequential()
model.add(LSTM(
512,
input_shape=(network_input.shape[1],network_input.shape[2]),
return_sequences=True
))
model.add(Dropout(0.5))
model.add(LSTM(1024,return_sequences=True))
model.add(Dropout(0.5))
model.add(LSTM(512))
model.add(Dense(512))
model.add(Dropout(0.5))
model.add(Dense(output_size))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',optimizer='rmsprop')
return model
def train(model,network_input,network_output):
filepath = "./TrainingData/KERAS-LSTM-POP/weights-{epoch:02d}-{loss:.4f}.hdf5"
checkpoint = ModelCheckpoint(
filepath,
monitor='loss',
verbose=0,
save_best_only=True,
mode='min'
)
callbacks_list = [checkpoint]
model.fit(network_input,network_output,epochs=2000,batch_size=25,callbacks=callbacks_list)
if __name__=='__main__':
#train_network()