kokawa2003 score 153
2019/09/17 11:44 投稿
| tensorflowの停止条件が分からない |
| tensorflowのビギナーです |
| 適当な面白そうな奴を見つけて走らせればどうか？とおもったので |
| https://github.com/vanhuyz/CycleGAN-TensorFlow |
| を走らせてみました。 |
| これトレーニングのソースを見ると |
| ```python |
| import tensorflow as tf |
| from model import CycleGAN |
| from reader import Reader |
| from datetime import datetime |
| import os |
| import logging |
| from utils import ImagePool |
| FLAGS = tf.flags.FLAGS |
| tf.flags.DEFINE_integer('batch_size', 1, 'batch size, default: 1') |
| tf.flags.DEFINE_integer('image_size', 256, 'image size, default: 256') |
| tf.flags.DEFINE_bool('use_lsgan', True, |
| 'use lsgan (mean squared error) or cross entropy loss, default: True') |
| tf.flags.DEFINE_string('norm', 'instance', |
| '[instance, batch] use instance norm or batch norm, default: instance') |
| tf.flags.DEFINE_integer('lambda1', 10, |
| 'weight for forward cycle loss (X->Y->X), default: 10') |
| tf.flags.DEFINE_integer('lambda2', 10, |
| 'weight for backward cycle loss (Y->X->Y), default: 10') |
| tf.flags.DEFINE_float('learning_rate', 2e-4, |
| 'initial learning rate for Adam, default: 0.0002') |
| tf.flags.DEFINE_float('beta1', 0.5, |
| 'momentum term of Adam, default: 0.5') |
| tf.flags.DEFINE_float('pool_size', 50, |
| 'size of image buffer that stores previously generated images, default: 50') |
| tf.flags.DEFINE_integer('ngf', 64, |
| 'number of gen filters in first conv layer, default: 64') |
| tf.flags.DEFINE_string('X', 'data/tfrecords/apple.tfrecords', |
| 'X tfrecords file for training, default: data/tfrecords/apple.tfrecords') |
| tf.flags.DEFINE_string('Y', 'data/tfrecords/orange.tfrecords', |
| 'Y tfrecords file for training, default: data/tfrecords/orange.tfrecords') |
| tf.flags.DEFINE_string('load_model', None, |
| 'folder of saved model that you wish to continue training (e.g. 20170602-1936), default: None') |
| def train(): |
| if FLAGS.load_model is not None: |
| checkpoints_dir = "checkpoints/" + FLAGS.load_model.lstrip("checkpoints/") |
| else: |
| current_time = datetime.now().strftime("%Y%m%d-%H%M") |
| checkpoints_dir = "checkpoints/{}".format(current_time) |
| try: |
| os.makedirs(checkpoints_dir) |
| except os.error: |
| pass |
| graph = tf.Graph() |
| with graph.as_default(): |
| cycle_gan = CycleGAN( |
| X_train_file=FLAGS.X, |
| Y_train_file=FLAGS.Y, |
| batch_size=FLAGS.batch_size, |
| image_size=FLAGS.image_size, |
| use_lsgan=FLAGS.use_lsgan, |
| norm=FLAGS.norm, |
| lambda1=FLAGS.lambda1, |
| lambda2=FLAGS.lambda2, |
| learning_rate=FLAGS.learning_rate, |
| beta1=FLAGS.beta1, |
| ngf=FLAGS.ngf |
| ) |
| G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x = cycle_gan.model() |
| optimizers = cycle_gan.optimize(G_loss, D_Y_loss, F_loss, D_X_loss) |
| summary_op = tf.summary.merge_all() |
| train_writer = tf.summary.FileWriter(checkpoints_dir, graph) |
| saver = tf.train.Saver() |
| with tf.Session(graph=graph) as sess: |
| if FLAGS.load_model is not None: |
| checkpoint = tf.train.get_checkpoint_state(checkpoints_dir) |
| meta_graph_path = checkpoint.model_checkpoint_path + ".meta" |
| restore = tf.train.import_meta_graph(meta_graph_path) |
| restore.restore(sess, tf.train.latest_checkpoint(checkpoints_dir)) |
| step = int(meta_graph_path.split("-")[2].split(".")[0]) |
| else: |
| sess.run(tf.global_variables_initializer()) |
| step = 0 |
| coord = tf.train.Coordinator() |
| threads = tf.train.start_queue_runners(sess=sess, coord=coord) |
| try: |
| fake_Y_pool = ImagePool(FLAGS.pool_size) |
| fake_X_pool = ImagePool(FLAGS.pool_size) |
| while not coord.should_stop(): |
| # get previously generated images |
| fake_y_val, fake_x_val = sess.run([fake_y, fake_x]) |
| # train |
| _, G_loss_val, D_Y_loss_val, F_loss_val, D_X_loss_val, summary = ( |
| sess.run( |
| [optimizers, G_loss, D_Y_loss, F_loss, D_X_loss, summary_op], |
| feed_dict={cycle_gan.fake_y: fake_Y_pool.query(fake_y_val), |
| cycle_gan.fake_x: fake_X_pool.query(fake_x_val)} |
| ) |
| ) |
| train_writer.add_summary(summary, step) |
| train_writer.flush() |
| if step % 100 == 0: |
| logging.info('-----------Step %d:-------------' % step) |
| logging.info(' G_loss : {}'.format(G_loss_val)) |
| logging.info(' D_Y_loss : {}'.format(D_Y_loss_val)) |
| logging.info(' F_loss : {}'.format(F_loss_val)) |
| logging.info(' D_X_loss : {}'.format(D_X_loss_val)) |
| if step % 10000 == 0: |
| save_path = saver.save(sess, checkpoints_dir + "/model.ckpt", global_step=step) |
| logging.info("Model saved in file: %s" % save_path) |
| step += 1 |
| except KeyboardInterrupt: |
| logging.info('Interrupted') |
| coord.request_stop() |
| except Exception as e: |
| coord.request_stop(e) |
| finally: |
| save_path = saver.save(sess, checkpoints_dir + "/model.ckpt", global_step=step) |
| logging.info("Model saved in file: %s" % save_path) |
| # When done, ask the threads to stop. |
| coord.request_stop() |
| coord.join(threads) |
| def main(unused_argv): |
| train() |
| if __name__ == '__main__': |
| logging.basicConfig(level=logging.INFO) |
| tf.app.run() |
| ``` |
| こうなっています。 |
| これをみるとＫＥＹの割り込み（ＣＴＬ＋Ｃ）か処理が例外のときに終了するらしいのはわかったのですがそれ以外にも停止することがあるのでしょうか？ってのがまず一個目の分からない分からないことです。 |
| （このソースで割り込み以外で処理を終了させることができるのだろうか？ってことです。それともＴＦ独特の終わらせ方があるのか？） |
| 次に |
| 学習中に |
| logging.info('-----------Step %d:-------------' % step) |
| logging.info(' G_loss : {}'.format(G_loss_val)) |
| logging.info(' D_Y_loss : {}'.format(D_Y_loss_val)) |
| logging.info(' F_loss : {}'.format(F_loss_val)) |
| logging.info(' D_X_loss : {}'.format(D_X_loss_val)) |
| の値を出力しますが、 |
| 現在 |
| INFO:root:-----------Step 22700:------------- |
| INFO:root: G_loss : 2.449359893798828 |
| INFO:root: D_Y_loss : 0.11760234832763672 |
| INFO:root: F_loss : 2.8169190883636475 |
| INFO:root: D_X_loss : 0.05803598836064339 |
| みたいな値ですがこれをどう評価すべきか分かりません。 |
| 次の分からないことは |
| これ学習に人の顔（２５６Ｘ２５６）をＡ アニメ少女の顔をＢ（２５６Ｘ２５６） |
| に突っ込んでいます。 |
| 進捗をtensorboardで見ると |
| 現在 |
|  |
| このような感じですが |
| 待っていたら改善しますか？ |
| シュワルツネッガーが美少女になりますか？ |
kokawa2003 score 153
2019/09/17 09:30 投稿
| tensorflowの停止条件が分からない |
| tensorflowのビギナーです |
| 適当な面白そうな奴を見つけて走らせればどうか？とおもったので |
| https://github.com/vanhuyz/CycleGAN-TensorFlow |
| を走らせてみました。 |
| これトレーニングのソースを見ると |
| ```python |
| import tensorflow as tf |
| from model import CycleGAN |
| from reader import Reader |
| from datetime import datetime |
| import os |
| import logging |
| from utils import ImagePool |
| FLAGS = tf.flags.FLAGS |
| tf.flags.DEFINE_integer('batch_size', 1, 'batch size, default: 1') |
| tf.flags.DEFINE_integer('image_size', 256, 'image size, default: 256') |
| tf.flags.DEFINE_bool('use_lsgan', True, |
| 'use lsgan (mean squared error) or cross entropy loss, default: True') |
| tf.flags.DEFINE_string('norm', 'instance', |
| '[instance, batch] use instance norm or batch norm, default: instance') |
| tf.flags.DEFINE_integer('lambda1', 10, |
| 'weight for forward cycle loss (X->Y->X), default: 10') |
| tf.flags.DEFINE_integer('lambda2', 10, |
| 'weight for backward cycle loss (Y->X->Y), default: 10') |
| tf.flags.DEFINE_float('learning_rate', 2e-4, |
| 'initial learning rate for Adam, default: 0.0002') |
| tf.flags.DEFINE_float('beta1', 0.5, |
| 'momentum term of Adam, default: 0.5') |
| tf.flags.DEFINE_float('pool_size', 50, |
| 'size of image buffer that stores previously generated images, default: 50') |
| tf.flags.DEFINE_integer('ngf', 64, |
| 'number of gen filters in first conv layer, default: 64') |
| tf.flags.DEFINE_string('X', 'data/tfrecords/apple.tfrecords', |
| 'X tfrecords file for training, default: data/tfrecords/apple.tfrecords') |
| tf.flags.DEFINE_string('Y', 'data/tfrecords/orange.tfrecords', |
| 'Y tfrecords file for training, default: data/tfrecords/orange.tfrecords') |
| tf.flags.DEFINE_string('load_model', None, |
| 'folder of saved model that you wish to continue training (e.g. 20170602-1936), default: None') |
| def train(): |
| if FLAGS.load_model is not None: |
| checkpoints_dir = "checkpoints/" + FLAGS.load_model.lstrip("checkpoints/") |
| else: |
| current_time = datetime.now().strftime("%Y%m%d-%H%M") |
| checkpoints_dir = "checkpoints/{}".format(current_time) |
| try: |
| os.makedirs(checkpoints_dir) |
| except os.error: |
| pass |
| graph = tf.Graph() |
| with graph.as_default(): |
| cycle_gan = CycleGAN( |
| X_train_file=FLAGS.X, |
| Y_train_file=FLAGS.Y, |
| batch_size=FLAGS.batch_size, |
| image_size=FLAGS.image_size, |
| use_lsgan=FLAGS.use_lsgan, |
| norm=FLAGS.norm, |
| lambda1=FLAGS.lambda1, |
| lambda2=FLAGS.lambda2, |
| learning_rate=FLAGS.learning_rate, |
| beta1=FLAGS.beta1, |
| ngf=FLAGS.ngf |
| ) |
| G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x = cycle_gan.model() |
| optimizers = cycle_gan.optimize(G_loss, D_Y_loss, F_loss, D_X_loss) |
| summary_op = tf.summary.merge_all() |
| train_writer = tf.summary.FileWriter(checkpoints_dir, graph) |
| saver = tf.train.Saver() |
| with tf.Session(graph=graph) as sess: |
| if FLAGS.load_model is not None: |
| checkpoint = tf.train.get_checkpoint_state(checkpoints_dir) |
| meta_graph_path = checkpoint.model_checkpoint_path + ".meta" |
| restore = tf.train.import_meta_graph(meta_graph_path) |
| restore.restore(sess, tf.train.latest_checkpoint(checkpoints_dir)) |
| step = int(meta_graph_path.split("-")[2].split(".")[0]) |
| else: |
| sess.run(tf.global_variables_initializer()) |
| step = 0 |
| coord = tf.train.Coordinator() |
| threads = tf.train.start_queue_runners(sess=sess, coord=coord) |
| try: |
| fake_Y_pool = ImagePool(FLAGS.pool_size) |
| fake_X_pool = ImagePool(FLAGS.pool_size) |
| while not coord.should_stop(): |
| # get previously generated images |
| fake_y_val, fake_x_val = sess.run([fake_y, fake_x]) |
| # train |
| _, G_loss_val, D_Y_loss_val, F_loss_val, D_X_loss_val, summary = ( |
| sess.run( |
| [optimizers, G_loss, D_Y_loss, F_loss, D_X_loss, summary_op], |
| feed_dict={cycle_gan.fake_y: fake_Y_pool.query(fake_y_val), |
| cycle_gan.fake_x: fake_X_pool.query(fake_x_val)} |
| ) |
| ) |
| train_writer.add_summary(summary, step) |
| train_writer.flush() |
| if step % 100 == 0: |
| logging.info('-----------Step %d:-------------' % step) |
| logging.info(' G_loss : {}'.format(G_loss_val)) |
| logging.info(' D_Y_loss : {}'.format(D_Y_loss_val)) |
| logging.info(' F_loss : {}'.format(F_loss_val)) |
| logging.info(' D_X_loss : {}'.format(D_X_loss_val)) |
| if step % 10000 == 0: |
| save_path = saver.save(sess, checkpoints_dir + "/model.ckpt", global_step=step) |
| logging.info("Model saved in file: %s" % save_path) |
| step += 1 |
| except KeyboardInterrupt: |
| logging.info('Interrupted') |
| coord.request_stop() |
| except Exception as e: |
| coord.request_stop(e) |
| finally: |
| save_path = saver.save(sess, checkpoints_dir + "/model.ckpt", global_step=step) |
| logging.info("Model saved in file: %s" % save_path) |
| # When done, ask the threads to stop. |
| coord.request_stop() |
| coord.join(threads) |
| def main(unused_argv): |
| train() |
| if __name__ == '__main__': |
| logging.basicConfig(level=logging.INFO) |
| tf.app.run() |
| ``` |
| こうなっています。 |
| これをみるとＫＥＹの割り込み（ＣＴＬ＋Ｃ）か処理が例外のときに終了するらしいのはわかったのですがそれ以外にも停止することがあるのでしょうか？ってのがまず一個目の分からない分からないことです。 |
| （このソースで割り込み以外で処理を終了させることができるのだろうか？ってことです。それともＴＦ独特の終わらせ方があるのか？） |
| 次に |
| 学習中に |
| logging.info('-----------Step %d:-------------' % step) |
| logging.info(' G_loss : {}'.format(G_loss_val)) |
| logging.info(' D_Y_loss : {}'.format(D_Y_loss_val)) |
| logging.info(' F_loss : {}'.format(F_loss_val)) |
| logging.info(' D_X_loss : {}'.format(D_X_loss_val)) |
| の値を出力しますが、 |
| 現在 |
| INFO:root:-----------Step 22700:------------- |
| INFO:root: G_loss : 2.449359893798828 |
| INFO:root: D_Y_loss : 0.11760234832763672 |
| INFO:root: F_loss : 2.8169190883636475 |
| INFO:root: D_X_loss : 0.05803598836064339 |
| みたいな値ですがこれをどう評価すべきか分かりません。 |
| 次の分からないことは |
| これ学習に人の顔（２５６Ｘ２５６）をＡ アニメ少女の顔をＢ（２５６Ｘ２５６） |
| に突っ込んでいます。 |
| 現在 |
|  |
| このような感じですが |
| 待っていたら改善しますか？ |
| シュワルツネッガーが美少女になりますか？ |
kokawa2003 score 153
2019/09/17 09:23 投稿
| tensorflowの停止条件が分からない |
| tensorflowのビギナーです |
| 適当な面白そうな奴を見つけて走らせればどうか？とおもったので |
| https://github.com/vanhuyz/CycleGAN-TensorFlow |
| を走らせてみました。 |
| これトレーニングのソースを見ると |
| ```python |
| import tensorflow as tf |
| from model import CycleGAN |
| from reader import Reader |
| from datetime import datetime |
| import os |
| import logging |
| from utils import ImagePool |
| FLAGS = tf.flags.FLAGS |
| tf.flags.DEFINE_integer('batch_size', 1, 'batch size, default: 1') |
| tf.flags.DEFINE_integer('image_size', 256, 'image size, default: 256') |
| tf.flags.DEFINE_bool('use_lsgan', True, |
| 'use lsgan (mean squared error) or cross entropy loss, default: True') |
| tf.flags.DEFINE_string('norm', 'instance', |
| '[instance, batch] use instance norm or batch norm, default: instance') |
| tf.flags.DEFINE_integer('lambda1', 10, |
| 'weight for forward cycle loss (X->Y->X), default: 10') |
| tf.flags.DEFINE_integer('lambda2', 10, |
| 'weight for backward cycle loss (Y->X->Y), default: 10') |
| tf.flags.DEFINE_float('learning_rate', 2e-4, |
| 'initial learning rate for Adam, default: 0.0002') |
| tf.flags.DEFINE_float('beta1', 0.5, |
| 'momentum term of Adam, default: 0.5') |
| tf.flags.DEFINE_float('pool_size', 50, |
| 'size of image buffer that stores previously generated images, default: 50') |
| tf.flags.DEFINE_integer('ngf', 64, |
| 'number of gen filters in first conv layer, default: 64') |
| tf.flags.DEFINE_string('X', 'data/tfrecords/apple.tfrecords', |
| 'X tfrecords file for training, default: data/tfrecords/apple.tfrecords') |
| tf.flags.DEFINE_string('Y', 'data/tfrecords/orange.tfrecords', |
| 'Y tfrecords file for training, default: data/tfrecords/orange.tfrecords') |
| tf.flags.DEFINE_string('load_model', None, |
| 'folder of saved model that you wish to continue training (e.g. 20170602-1936), default: None') |
| def train(): |
| if FLAGS.load_model is not None: |
| checkpoints_dir = "checkpoints/" + FLAGS.load_model.lstrip("checkpoints/") |
| else: |
| current_time = datetime.now().strftime("%Y%m%d-%H%M") |
| checkpoints_dir = "checkpoints/{}".format(current_time) |
| try: |
| os.makedirs(checkpoints_dir) |
| except os.error: |
| pass |
| graph = tf.Graph() |
| with graph.as_default(): |
| cycle_gan = CycleGAN( |
| X_train_file=FLAGS.X, |
| Y_train_file=FLAGS.Y, |
| batch_size=FLAGS.batch_size, |
| image_size=FLAGS.image_size, |
| use_lsgan=FLAGS.use_lsgan, |
| norm=FLAGS.norm, |
| lambda1=FLAGS.lambda1, |
| lambda2=FLAGS.lambda2, |
| learning_rate=FLAGS.learning_rate, |
| beta1=FLAGS.beta1, |
| ngf=FLAGS.ngf |
| ) |
| G_loss, D_Y_loss, F_loss, D_X_loss, fake_y, fake_x = cycle_gan.model() |
| optimizers = cycle_gan.optimize(G_loss, D_Y_loss, F_loss, D_X_loss) |
| summary_op = tf.summary.merge_all() |
| train_writer = tf.summary.FileWriter(checkpoints_dir, graph) |
| saver = tf.train.Saver() |
| with tf.Session(graph=graph) as sess: |
| if FLAGS.load_model is not None: |
| checkpoint = tf.train.get_checkpoint_state(checkpoints_dir) |
| meta_graph_path = checkpoint.model_checkpoint_path + ".meta" |
| restore = tf.train.import_meta_graph(meta_graph_path) |
| restore.restore(sess, tf.train.latest_checkpoint(checkpoints_dir)) |
| step = int(meta_graph_path.split("-")[2].split(".")[0]) |
| else: |
| sess.run(tf.global_variables_initializer()) |
| step = 0 |
| coord = tf.train.Coordinator() |
| threads = tf.train.start_queue_runners(sess=sess, coord=coord) |
| try: |
| fake_Y_pool = ImagePool(FLAGS.pool_size) |
| fake_X_pool = ImagePool(FLAGS.pool_size) |
| while not coord.should_stop(): |
| # get previously generated images |
| fake_y_val, fake_x_val = sess.run([fake_y, fake_x]) |
| # train |
| _, G_loss_val, D_Y_loss_val, F_loss_val, D_X_loss_val, summary = ( |
| sess.run( |
| [optimizers, G_loss, D_Y_loss, F_loss, D_X_loss, summary_op], |
| feed_dict={cycle_gan.fake_y: fake_Y_pool.query(fake_y_val), |
| cycle_gan.fake_x: fake_X_pool.query(fake_x_val)} |
| ) |
| ) |
| train_writer.add_summary(summary, step) |
| train_writer.flush() |
| if step % 100 == 0: |
| logging.info('-----------Step %d:-------------' % step) |
| logging.info(' G_loss : {}'.format(G_loss_val)) |
| logging.info(' D_Y_loss : {}'.format(D_Y_loss_val)) |
| logging.info(' F_loss : {}'.format(F_loss_val)) |
| logging.info(' D_X_loss : {}'.format(D_X_loss_val)) |
| if step % 10000 == 0: |
| save_path = saver.save(sess, checkpoints_dir + "/model.ckpt", global_step=step) |
| logging.info("Model saved in file: %s" % save_path) |
| step += 1 |
| except KeyboardInterrupt: |
| logging.info('Interrupted') |
| coord.request_stop() |
| except Exception as e: |
| coord.request_stop(e) |
| finally: |
| save_path = saver.save(sess, checkpoints_dir + "/model.ckpt", global_step=step) |
| logging.info("Model saved in file: %s" % save_path) |
| # When done, ask the threads to stop. |
| coord.request_stop() |
| coord.join(threads) |
| def main(unused_argv): |
| train() |
| if __name__ == '__main__': |
| logging.basicConfig(level=logging.INFO) |
| tf.app.run() |
| ``` |
| こうなっています。 |
| これをみるとＫＥＹの割り込み（ＣＴＬ＋Ｃ）か処理が例外のときに終了するらしいのはわかったのですがそれ以外にも停止することがあるのでしょうか？ってのがまず一個目の分からない分からないことです。 |
| 次に |
| 学習中に |
| logging.info('-----------Step %d:-------------' % step) |
| logging.info(' G_loss : {}'.format(G_loss_val)) |
| logging.info(' D_Y_loss : {}'.format(D_Y_loss_val)) |
| logging.info(' F_loss : {}'.format(F_loss_val)) |
| logging.info(' D_X_loss : {}'.format(D_X_loss_val)) |
| の値を出力しますが、 |
| 現在 |
| INFO:root:-----------Step 22700:------------- |
| INFO:root: G_loss : 2.449359893798828 |
| INFO:root: D_Y_loss : 0.11760234832763672 |
| INFO:root: F_loss : 2.8169190883636475 |
| INFO:root: D_X_loss : 0.05803598836064339 |
| みたいな値ですがこれをどう評価すべきか分かりません。 |
| 次の分からないことは |
| これ学習に人の顔（２５６Ｘ２５６）をＡ アニメ少女の顔をＢ（２５６Ｘ２５６） |
| に突っ込んでいます。 |
| に突っ込んでいます。 |
| 現在 |
|  |
| このような感じですが |
| 待っていたら改善しますか？ |
| シュワルツネッガーが美少女になりますか？ |
