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chord_lstm_training.py
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chord_lstm_training.py
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# Author: Jonas Wiesendanger, Andres Konrad, Gino Brunner ([email protected])
from settings import *
from keras.models import Sequential
from keras.layers import LSTM
from keras.layers import Dense, Activation
from keras.layers import Embedding
from keras.optimizers import RMSprop, Adam
import keras.utils
from keras.utils import np_utils
from keras.layers.wrappers import Bidirectional
from random import shuffle
import progressbar
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import _pickle as pickle
import os
import data_class
import time
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
# Uncomment next block if you only want to use a fraction of the GPU memory:
# config = tf.ConfigProto()
# config.gpu_options.visible_device_list = "2"
# config.gpu_options.per_process_gpu_memory_fraction = 0.9
# set_session(tf.Session(config=config))
#Path where the models are saved:
model_path = 'models/chords/'
model_filetype = '.pickle'
epochs = 20
train_set_size = 4
test_set_size = 1
test_step = 800 # Calculate error for test set every this many songs
verbose = False
show_plot = False
save_plot = True
lstm_size = 512
batch_size = 1
learning_rate = 0.00001
step_size = 1
save_step = 10
shuffle_train_set = True
bidirectional = False
optimizer = 'Adam'
fd = {'shifted': shifted, 'lr': learning_rate, 'emdim': chord_embedding_dim, 'opt': optimizer,
'bi': bidirectional, 'lstms': lstm_size, 'trainsize': train_set_size, 'testsize': test_set_size, 'samples_per_bar': samples_per_bar}
t = str(int(round(time.time())))
model_name = t+ '-Shifted_%(shifted)s_Lr_%(lr)s_EmDim_%(emdim)s_opt_%(opt)s_bi_%(bi)s_lstmsize_%(lstms)s_trainsize_%(trainsize)s_testsize_%(testsize)s_samples_per_bar%(samples_per_bar)s' % fd
model_path = model_path + model_name + '/'
if not os.path.exists(model_path):
os.makedirs(model_path)
print('loading data...')
train_set, test_set = data_class.get_chord_train_and_test_set(train_set_size, test_set_size)
print('creating model...')
model = Sequential()
model.add(Embedding(num_chords, chord_embedding_dim, input_length=step_size, name="embedding", batch_input_shape=(batch_size,step_size)))
# model.add(Embedding(num_chords, chord_embedding_dim, input_length=step_size))
# if bidirectional: model.add(Bidirectional(LSTM(lstm_size, stateful=True)))
# else: model.add(LSTM(lstm_size, stateful=True))
model.add(LSTM(lstm_size, stateful=True))
model.add(Dense(num_chords))
model.add(Activation('softmax'))
if optimizer == 'Adam':
optimizer = Adam(lr=learning_rate)
elif optimizer == 'RMS':
optimizer = RMSprop(lr=learning_rate)
loss = 'categorical_crossentropy'
print("compiling model")
model.compile(optimizer, loss)
total_test_loss_array = []
total_train_loss_array = []
total_test_loss = 0
def test():
print('\nTesting:')
total_test_loss = 0
bar = progressbar.ProgressBar(maxval=test_set_size, redirect_stdout=False)
for i, test_song in enumerate(test_set):
X_test = test_song[:-1]
Y_test = np_utils.to_categorical(test_song[1:], num_classes=num_chords)
loss = model.evaluate(X_test, Y_test, batch_size=batch_size, verbose=verbose)
model.reset_states()
total_test_loss += loss
bar.update(i+1)
total_test_loss_array.append(total_test_loss/test_set_size)
print('\nTotal test loss: ', total_test_loss/test_set_size)
print('-'*50)
plt.plot(total_test_loss_array, 'b-', label='test loss')
plt.plot(total_train_loss_array, 'r-', label='train loss')
# plt.legend()
plt.ylabel(model_path)
# plt.axis([0, 50, 3, 5])
plt.grid()
if show_plot: plt.show()
if save_plot: plt.savefig(model_path+'plot.png')
pickle.dump(total_test_loss_array,open(model_path+'total_test_loss_array.pickle', 'wb'))
pickle.dump(total_train_loss_array,open(model_path+'total_train_loss_array.pickle', 'wb'))
def train():
print('training model...')
total_train_loss = 0
for e in range(1, epochs+1):
print('Epoch ', e, 'of ', epochs, 'Epochs\nTraining:')
if shuffle_train_set:
shuffle(train_set)
bar = progressbar.ProgressBar(maxval=train_set_size)
for i, song in enumerate(train_set):
# bar.start()
X = song[:-1]
Y = np_utils.to_categorical(song[1:], num_classes=num_chords)
hist = model.fit(X, Y, batch_size=batch_size, shuffle=False, verbose=verbose)
model.reset_states()
bar.update(i+1)
# print(hist.history)
total_train_loss += hist.history['loss'][0]
if (i+1)%test_step is 0:
total_train_loss = total_train_loss/test_step
total_train_loss_array.append(total_train_loss)
test()
total_train_loss = 0
if e%save_step is 0:
print('saving model')
model_save_path = model_path + 'model_' + 'Epoch' + str(e) + '_' + str(i+1) + model_filetype
model.save(model_save_path)
def save_params():
with open(model_path + 'params.txt', "w") as text_file:
text_file.write("epochs: %s" % epochs + '\n')
text_file.write("train_set_size: %s" % train_set_size + '\n')
text_file.write("test_set_size: %s" % test_set_size + '\n')
text_file.write("lstm_size: %s" % lstm_size + '\n')
text_file.write("embedding_dim: %s" % chord_embedding_dim + '\n')
text_file.write("learning_rate: %s" % learning_rate + '\n')
#text_file.write("save_step: %s" % save_step + '\n')
text_file.write("shuffle_train_set: %s" % shuffle_train_set + '\n')
text_file.write("test_step: %s" % test_step + '\n')
text_file.write("bidirectional: %s" % bidirectional + '\n')
text_file.write("num_chords: %s" % num_chords + '\n')
text_file.write("chord_n: %s" % chord_n + '\n')
print("saving params")
save_params()
print("starting training..")
train()