melody_encoder = MelodyEncoder(m=m,
conv_f=4,
conv_win_size=min(mc, 3),
enc_lstm_size=16)
melody_net = MelodyNetwork(melody_encoder=melody_encoder,
rhythm_embed_size=r_embed_size,
dec_lstm_size=16,
V=mV,
dec_use_meta=True)
#%% COMBINED
combined_net = CombinedNetwork(context_size=rc,
melody_bar_len=m,
meta_embed_size=meta_embed_size,
bar_embedder=bar_embedder,
rhythm_net=rhythm_net,
melody_net=melody_net,
meta_predictor=meta_predictor)
#%% TRAIN
data_gen = without_lead(cg)
combined_net.fit_generator(data_gen, steps_per_epoch=cg.num_pieces, epochs=1)
#%% SAVE
#os.makedirs("test")
combined_net.save_model_custom("test")
dec_use_meta=True)
melody_net = MelodyNetwork(m=m,
V=V_melody,
rhythm_embed_size=out_size,
conv_f=conv_f,
conv_win_size=conv_win_size,
enc_lstm_size=melody_enc_lstm_size,
dec_lstm_1_size=melody_dec_lstm_1_size,
enc_use_meta=False,
dec_use_meta=True)
print("Individual networks set up...\n")
#
comb_net = CombinedNetwork(context_size, m, meta_data_len, be, rhythm_net,
melody_net)
print("Combined network set up...\n")
#
top_dir = "Nets/"
log_dir = "logs/"
weight_dir = "weights/"
cur_date_time = asctime().replace(" ", "_").replace(":", "-")
tb = TensorBoard(log_dir=top_dir + log_dir + cur_date_time)
os.makedirs(top_dir + weight_dir + cur_date_time)
#
for cur_iteration in range(int(num_epochs / j)):
# enc_use_meta=False, dec_use_meta=True)
#
# melody_net = MelodyNetwork(m=m, V=V_melody,
# rhythm_embed_size=out_size,
# conv_f=conv_f, conv_win_size=conv_win_size,
# enc_lstm_size=melody_enc_lstm_size, dec_lstm_1_size=melody_dec_lstm_1_size,
# enc_use_meta=False, dec_use_meta=True)
#
# print("Individual networks set up...\n")
#
#
# #
# comb_net = CombinedNetwork(context_size, m, meta_data_len, be, rhythm_net, melody_net)
#
comb_net = CombinedNetwork.from_saved_custom(
"Nets/weights/Fri_Mar_15_05-50-18_2019")
print("Combined network loaded...\n")
#
top_dir = "Nets/"
log_dir = "logs/"
weight_dir = "weights/"
cur_date_time = asctime().replace(" ", "_").replace(":", "-")
tb = TensorBoard(log_dir=top_dir + log_dir + cur_date_time)
os.makedirs(top_dir + weight_dir + cur_date_time)
#
for cur_iteration in range(int(num_epochs / j)):
meta_predictor.freeze()
#%%
music_dir = "../../Data/music21"
ch_gen = ChordGenerator(music_dir, save_conversion_params="/".join([top_dir, save_dir]),
to_list=False, meta_prep_f=None) # None
# data_iter = ch_gen.generate_forever(batch_size=24)
x, y = ch_gen.list_data()
#%%
comb_net = CombinedNetwork.from_saved_custom("/".join([top_dir, save_dir, "weights"]),
meta_predictor,
generation=True,
compile_now=False)
melody_enc = comb_net.melody_encoder
size_1 = 1
fresh_melody_enc = MelodyEncoder(m=48, conv_f=4, conv_win_size=size_1, enc_lstm_size=52, compile_now=False)
fresh_melody_enc.set_weights(get_smaller_weights(melody_enc, conv_win_size=size_1))
# for l in fresh_melody_enc.layers:
# l.trainable = False
fresh_melody_enc.compile_default()
#%%
chord_net = ChordNetwork(fresh_melody_enc, 28, ch_gen.V, compile_now=True)
# -*- coding: utf-8 -*- from Nets.MetaPredictor import MetaPredictor from Nets.CombinedNetwork import CombinedNetwork #%% save_dir = "Trainings/meta_no_embed_wrong_loss/" meta_pred = MetaPredictor.from_saved_custom(save_dir + "meta") #%% comb_net = CombinedNetwork.from_saved_custom(save_dir + "weights", meta_pred)
# -*- coding: utf-8 -*-
from Nets.CombinedNetwork import CombinedNetwork
import numpy as np
import numpy.random as rand
if __name__ == "__main__":
#%%
weights_folder = "Nets/weights/test_net"
comb_net = CombinedNetwork.from_saved_custom(weights_folder,
generation=True,
compile_now=False)
context_size = comb_net.params["context_size"]
V_rhythm = comb_net.params["bar_embed_params"][0]
m, V_melody = comb_net.params["melody_net_params"][0], comb_net.params["melody_net_params"][1]
meta_len = comb_net.params["meta_len"]
print("\n", "-"*40, "\nINFO FOR LOADED NET:", comb_net)
print("\n - Used context size: ", context_size)
print("\n - Number of voices: ",comb_net.rhythm_net.n_voices)
enc_lstm_size=melody_enc_lstm_size)
melody_net = MelodyNetwork(melody_encoder=melody_encoder,
rhythm_embed_size=out_size,
dec_lstm_size=melody_dec_lstm_size,
V=V_melody,
dec_use_meta=True,
compile_now=True)
print("Individual networks set up...\n")
# COMBINED NETWORK
comb_net = CombinedNetwork(context_size,
m,
meta_embed_size,
bar_embedder,
rhythm_net,
melody_net,
meta_predictor,
generation=False,
compile_now=True)
print("Combined network set up...\n")
print(comb_net.summary())
print("\n\n")
# TRAINING
if not save_dir or not os.path.isdir("/".join([top_dir, save_dir])):
raise FileNotFoundError(
"DIRECTORY TO SAVE IS NONE OR DOESN'T EXISTS, DON'T KNOW WHERE TO SAVE!"
+ "\nHINT: USE DIRECTORY WHERE META DATA IS SAVED")