def exp_train_concat_svm():
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=[
'lr',
'l2_weight',
'batch_size',
'glove_dim',
'embed_linear_feat',
'embed_quadratic_feat',
'distance_linear_feat',
'distance_quadratic_feat',
'visual_only',
],
optional_args=[
'exp_name', 'out_base_dir',
'embeddings_h5py',
'word_to_idx_json'
])
if args.exp_name is None:
exp_name = 'trial'
else:
exp_name = args.exp_name
if args.out_base_dir is None:
out_base_dir = os.path.join(os.getcwd(),
'symlinks/exp/semeval_2018_10/concat_svm')
else:
out_base_dir = args.out_base_dir
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.log_dir = os.path.join(exp_const.exp_dir, 'log')
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.num_epochs = 20
exp_const.batch_size = args.batch_size
exp_const.lr = args.lr
data_const = SemEval201810DatasetConstants()
if args.embeddings_h5py is None:
glove_const = GloveConstantsFactory.create()
data_const.embeddings_h5py = glove_const.embeddings_h5py
data_const.word_to_idx_json = glove_const.word_to_idx_json
else:
data_const.embeddings_h5py = args.embeddings_h5py
data_const.word_to_idx_json = args.word_to_idx_json
embed_dim = h5py.File(data_const.embeddings_h5py,
'r')['embeddings'].shape[1]
model_const = Constants()
model_const.concat_svm = ConcatSVMConstants()
model_const.concat_svm.l2_weight = args.l2_weight
model_const.concat_svm.embedding_dim = embed_dim
model_const.concat_svm.glove_dim = args.glove_dim
model_const.concat_svm.layer_units = []
model_const.concat_svm.use_embedding_linear_feats = args.embed_linear_feat
model_const.concat_svm.use_embedding_quadratic_feats = \
args.embed_quadratic_feat
model_const.concat_svm.use_distance_linear_feats = args.distance_linear_feat
model_const.concat_svm.use_distance_quadratic_feats = \
args.distance_quadratic_feat
model_const.concat_svm.visual_only = args.visual_only
train_concat_svm.main(exp_const, data_const, model_const)
def exp_eval_concat_svm():
args = parser.parse_args()
not_specified_args = manage_required_args(args,
parser,
required_args=[
'batch_size',
'glove_dim',
'embed_linear_feat',
'embed_quadratic_feat',
'distance_linear_feat',
'distance_quadratic_feat',
'visual_only',
'visual_vocab_json',
],
optional_args=[
'exp_name', 'out_base_dir',
'embeddings_h5py',
'word_to_idx_json'
])
if args.exp_name is None:
exp_name = 'trial'
else:
exp_name = args.exp_name
if args.out_base_dir is None:
out_base_dir = os.path.join(os.getcwd(),
'symlinks/exp/semeval_2018_10/concat_svm')
else:
out_base_dir = args.out_base_dir
exp_const = ExpConstants(exp_name=exp_name, out_base_dir=out_base_dir)
exp_const.model_dir = os.path.join(exp_const.exp_dir, 'models')
exp_const.batch_size = 2560
data_const = SemEval201810DatasetConstants()
data_const.subset = 'test'
if args.embeddings_h5py is None:
glove_const = GloveConstantsFactory.create()
data_const.embeddings_h5py = glove_const.embeddings_h5py
data_const.word_to_idx_json = glove_const.word_to_idx_json
data_const.visual_vocab_json = data_const.word_to_idx_json
else:
data_const.embeddings_h5py = args.embeddings_h5py
data_const.word_to_idx_json = args.word_to_idx_json
data_const.visual_vocab_json = args.visual_vocab_json
# data_const.vocab_json = os.path.join(
# os.getcwd(),
# 'symlinks/data/visualgenome/proc/all_word_freqs.json')
# data_const.visual_vocab_json = os.path.join(
# os.getcwd(),
# 'symlinks/exp/combine_glove_visual_reps/concat_glove_visual_avg_reps/visual_words.json')
embed_dim = h5py.File(data_const.embeddings_h5py,
'r')['embeddings'].shape[1]
model_const = Constants()
model_const.concat_svm = ConcatSVMConstants()
model_const.concat_svm.embedding_dim = embed_dim
model_const.concat_svm.glove_dim = args.glove_dim
model_const.concat_svm.layer_units = []
model_const.concat_svm.use_embedding_linear_feats = args.embed_linear_feat
model_const.concat_svm.use_embedding_quadratic_feats = \
args.embed_quadratic_feat
model_const.concat_svm.use_distance_linear_feats = args.distance_linear_feat
model_const.concat_svm.use_distance_quadratic_feats = \
args.distance_quadratic_feat
model_const.concat_svm.visual_only = args.visual_only
eval_concat_svm.main(exp_const, data_const, model_const)
