def get_note_from_softmax(self, softmax):
"""Extracts a one-hot encoding of the most probable note.
Args:
softmax: Softmax probabilities over possible next notes.
Returns:
One-hot encoding of most probable note.
"""
note_idx = np.argmax(softmax)
note_enc = rl_tuner_ops.make_onehot([note_idx], rl_tuner_ops.NUM_CLASSES)
return np.reshape(note_enc, (rl_tuner_ops.NUM_CLASSES))
def get_note_from_softmax(self, softmax):
"""Extracts a one-hot encoding of the most probable note.
Args:
softmax: Softmax probabilities over possible next notes.
Returns:
One-hot encoding of most probable note.
"""
note_idx = np.argmax(softmax)
note_enc = rl_tuner_ops.make_onehot([note_idx], rl_tuner_ops.NUM_CLASSES)
return np.reshape(note_enc, (rl_tuner_ops.NUM_CLASSES))
def fitness(self, should_print=False):
RL = True
if not RL:
# self.create_midi_file()
repertory = "output/"
file = repertory + str(self.ind) + ".mid"
return -abs(self.vae.get_distance(file, self.ind))
else:
'''
self.rlt.train(num_steps=100000, exploration_period=500000)
stat_dict = self.rlt.evaluate_music_theory_metrics(num_compositions=100)
self.rlt.plot_rewards()
stat3 = self.rlt.generate_music_sequence(visualize_probs=True, title='post_rl')
print(stat3)
exit(0)
'''
# print("NOTE COMPO: ")
self.rlt.num_notes_in_melody = self.number_of_notes
self.rlt.reset_composition()
to_mean_note_reward = []
to_mean_rnn = []
for note in self.sequence:
one_hot = np.array(
rl_tuner_ops.make_onehot([note.bit.pitch], 38)).flatten()
note_reward = self.rlt.reward_music_theory(one_hot)
# if should_print:
# print(one_hot,note_reward )
self.rlt.composition.append(np.argmax(one_hot))
self.rlt.beat += 1
to_mean_note_reward.append(note_reward)
a, b, c = self.rlt.action(one_hot)
reward_scores = np.reshape(c, (38))
# print(to_mean_rnn)
note_rnn_reward = self.rlt.reward_from_reward_rnn_scores(
one_hot, reward_scores)
to_mean_rnn.append(note_rnn_reward)
# print(self.rlt.composition)
mean_note_reward = np.mean(to_mean_note_reward)
to_mean_rnn = np.mean(to_mean_rnn)
return to_mean_rnn + mean_note_reward
def testRewardNetwork(self):
graph = tf.Graph()
self.session = tf.Session(graph=graph)
note_rnn = note_rnn_loader.NoteRNNLoader(
graph, scope='test',
checkpoint_dir=None) #, midi_primer='/tmp/RL/nice.mid')
note_rnn.initialize_new(self.session)
#print("NOTESEQ", note_rnn.primer)
#print("AFER")
rlt = rl_tuner.RLTuner(self.output_dir,
note_rnn_checkpoint_dir=self.checkpoint_dir)
initial_note = rlt.prime_internal_models()
print("INITIAL NOTE", initial_note)
print("FIRST SCORE", rlt.reward_key(initial_note))
action = rlt.action(initial_note, 100, enable_random=True)
print("ACTION CHOSEN ", action[0])
print("ACTION REWARD ", action[1])
print("ACTION NEXT OBS ", action[2])
print("FINAL", rlt.reward_key(action[2]))
print("ONE HOT CREATED")
x = np.array(rl_tuner_ops.make_onehot([10], 24)).flatten()
print(x)
print("FINAL", rlt.reward_key(x))
last_observation = rlt.prime_internal_models()
rlt.num_notes_in_melody = 12
rlt.reset_composition()
for _ in range(rlt.num_notes_in_melody):
_, new_observation, reward_scores = rlt.action(
last_observation,
0,
enable_random=False,
sample_next_obs=False)
music_theory_reward = rlt.reward_music_theory(new_observation)
#music_theory_rewards = music_theory_reward * rlt.reward_scaler
print(music_theory_reward)
print(new_observation)
rlt.composition.append(np.argmax(new_observation))
rlt.beat += 1
last_observation = new_observation
print("num note", rlt.num_notes_in_melody)
rlt.reset_composition()
final = []
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
final.append(test)
avg = []
print("BEGIN")
for x in final:
reward = rlt.reward_music_theory(x)
print(x, reward)
rlt.composition.append(np.argmax(x))
rlt.beat += 1
avg.append(reward)
rlt.music_theory_reward_last_n += reward * rlt.reward_scaler
print("AVG", np.mean(avg))
rlt.reset_composition()
final = []
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0]
final.append(test)
test = [0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
test = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
final.append(test)
test = [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
final.append(test)
print("SECOND")
avg = []
for x in final:
reward = rlt.reward_music_theory(x)
print(x, reward)
rlt.composition.append(np.argmax(x))
rlt.beat += 1
avg.append(reward)
rlt.music_theory_reward_last_n += reward * rlt.reward_scaler
print("AVG", np.mean(avg))
