質問編集履歴
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エラーメッセージとソースコードの記入場所を変更しました。
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@@ -16,15 +16,17 @@
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エラーメッセージ
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Traceback (most resent call last):
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File "run_train.py", line 85, in <module>
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layers = model.build_model(show_model=args.show_architecture)
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AttributeError: 'function' object has no attribute 'build_model'
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```
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File "run_train.py", line 85, in <module>
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layers = model.build_model(show_model=args.show_architecture)
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AttributeError: 'function' object has no attribute 'build_model'
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### 該当のソースコード
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@@ -34,173 +36,175 @@
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ソースコード
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from __future__ import print_function
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import os
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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import argparse
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from cca_layer.utils.data import load_iapr, load_audio_score
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from cca_layer.config.settings import EXP_ROOT
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from cca_layer.utils.plotting import BColors
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col = BColors()
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def select_model(model_path):
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""" select model and train function """
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model_str = os.path.basename(model_path)
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print("1", model_str)
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model_str = model_str.split('.py')[0]
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exec('from models import ' + model_str + ' as model')
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print("2", model_str)
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from utils.train_utils import fit
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select_model.EXP_NAME = model_str
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return select_model, fit
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def select_data(data_name, seed=23):
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""" select train data """
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if str(data_name) == "iapr":
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data = load_iapr(seed=seed)
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elif str(data_name) == "audio_score":
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data = load_audio_score(seed=seed)
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else:
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pass
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return data
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if __name__ == '__main__':
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""" main """
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parser = argparse.ArgumentParser(description='Train multi-modality model.')
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parser.add_argument('--model', help='select model to train.')
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parser.add_argument('--data', help='select data for training.')
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parser.add_argument('--seed', help='query direction.', type=int, default=23)
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parser.add_argument('--no_dump', help='do not dump model file.', action='store_true')
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parser.add_argument('--tag', help='add tag to grid search dump file.', type=str, default=None)
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parser.add_argument('--show_architecture', help='print model architecture.', action='store_true')
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args = parser.parse_args()
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if not os.path.exists(EXP_ROOT):
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os.makedirs(EXP_ROOT)
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model, fit = select_model(args.model)
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print("\nLoading data...")
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data = select_data(args.data, args.seed)
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out_path = os.path.join(os.path.join(EXP_ROOT), model.EXP_NAME)
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dump_file = 'params.pkl' if args.tag is None else 'params_%s.pkl' % args.tag
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dump_file = os.path.join(out_path, dump_file)
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log_file = 'results.pkl' if args.tag is None else 'results_%s.pkl' % args.tag
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log_file = os.path.join(out_path, log_file)
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print("\nBuilding network...")
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layers = model.build_model(show_model=args.show_architecture) //該当箇所
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dump_file = None if args.no_dump else dump_file
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train_batch_iter = model.train_batch_iterator(model.BATCH_SIZE)
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valid_batch_iter = model.valid_batch_iterator()
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layers, va_loss = fit(layers, data, model.objectives,
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train_batch_iter=train_batch_iterator, valid_batch_iter=valid_batch_iterator,
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num_epochs=model.MAX_EPOCHS, patience=model.PATIENCE,
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learn_rate=model.INI_LEARNING_RATE, update_learning_rate=model.update_learning_rate,
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compute_updates=model.compute_updates, l_2=model.L2, l_1=model.L1,
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exp_name=model.EXP_NAME, out_path=out_path, dump_file=dump_file, log_file=log_file,
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refinement_steps=model.REFINEMENT_STEPS, lr_multiplier=model.LR_MULTIPLIER, do_raise=True)
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```
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import os
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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import argparse
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from cca_layer.utils.data import load_iapr, load_audio_score
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from cca_layer.config.settings import EXP_ROOT
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from cca_layer.utils.plotting import BColors
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col = BColors()
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def select_model(model_path):
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""" select model and train function """
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model_str = os.path.basename(model_path)
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print("1", model_str)
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model_str = model_str.split('.py')[0]
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exec('from models import ' + model_str + ' as model')
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print("2", model_str)
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from utils.train_utils import fit
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select_model.EXP_NAME = model_str
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return select_model, fit
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def select_data(data_name, seed=23):
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""" select train data """
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if str(data_name) == "iapr":
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data = load_iapr(seed=seed)
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elif str(data_name) == "audio_score":
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data = load_audio_score(seed=seed)
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else:
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pass
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return data
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if __name__ == '__main__':
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""" main """
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parser = argparse.ArgumentParser(description='Train multi-modality model.')
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parser.add_argument('--model', help='select model to train.')
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parser.add_argument('--data', help='select data for training.')
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parser.add_argument('--seed', help='query direction.', type=int, default=23)
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parser.add_argument('--no_dump', help='do not dump model file.', action='store_true')
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parser.add_argument('--tag', help='add tag to grid search dump file.', type=str, default=None)
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parser.add_argument('--show_architecture', help='print model architecture.', action='store_true')
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args = parser.parse_args()
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if not os.path.exists(EXP_ROOT):
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os.makedirs(EXP_ROOT)
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model, fit = select_model(args.model)
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print("\nLoading data...")
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data = select_data(args.data, args.seed)
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out_path = os.path.join(os.path.join(EXP_ROOT), model.EXP_NAME)
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dump_file = 'params.pkl' if args.tag is None else 'params_%s.pkl' % args.tag
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dump_file = os.path.join(out_path, dump_file)
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log_file = 'results.pkl' if args.tag is None else 'results_%s.pkl' % args.tag
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log_file = os.path.join(out_path, log_file)
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print("\nBuilding network...")
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layers = model.build_model(show_model=args.show_architecture) //該当箇所
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dump_file = None if args.no_dump else dump_file
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train_batch_iter = model.train_batch_iterator(model.BATCH_SIZE)
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valid_batch_iter = model.valid_batch_iterator()
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layers, va_loss = fit(layers, data, model.objectives,
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train_batch_iter=train_batch_iterator, valid_batch_iter=valid_batch_iterator,
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num_epochs=model.MAX_EPOCHS, patience=model.PATIENCE,
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learn_rate=model.INI_LEARNING_RATE, update_learning_rate=model.update_learning_rate,
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compute_updates=model.compute_updates, l_2=model.L2, l_1=model.L1,
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exp_name=model.EXP_NAME, out_path=out_path, dump_file=dump_file, log_file=log_file,
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refinement_steps=model.REFINEMENT_STEPS, lr_multiplier=model.LR_MULTIPLIER, do_raise=True)
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### 試したこと
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