from core.utils.general import args, apply_options
from core.dataset.pickle import make_datasets
from core.models.input import EmbeddingsInput
from core.models.layer import FullyConnected, Dropout, Flatten, \
ReLu, Conv2d, MaxPool, Combine, Reduce, Embedding
# load config
options = args("embeddings")
config = importlib.import_module("configs." + options.config)
config = apply_options(config, options)
# data featurizer
featurizer = embedding_features(config.modes)
preprocess = lambda cluster: (featurizer(cluster), cluster["nparts"], cluster[
"props"])
featurizer_raw = wrap_extractor(config.extractor)
preprocess_raw = lambda cluster: (featurizer_raw(cluster), cluster["nparts"])
# get data
train_examples, dev_set, test_set, test_raw = make_datasets(
config, preprocess, preprocess_raw)
# data processing
processing = get_default_processing(train_examples, config.n_features,
preprocess_y(1, 3), config.max_n_cells,
config.pad_tok, config.preprocessing_mode)
# model
model = EmbeddingsInput(config)
model.build("light")
import importlib
from core.utils.general import args, apply_options
from core.utils.evaluate import featurized_export_result
from core.utils.preprocess import preprocess_y
from core.dataset.pickle import make_datasets
from core.features.layers import wrap_extractor, get_default_processing
from core.models.input import SquareInput
# load config
options = args("fc")
config = importlib.import_module("configs." + options.config)
config = apply_options(config, options)
# data featurizers
extractor = config.extractor
featurizer = wrap_extractor(extractor)
preprocess = lambda cluster: (featurizer(cluster), cluster["nparts"], cluster[
"props"])
# get data
train_examples, dev_set, test_set, test_raw = make_datasets(
config, preprocess, preprocess)
# data processing
processing = get_default_processing(train_examples, extractor,
preprocess_y(1, 3),
config.preprocessing_mode)
# model
model = SquareInput(config, config.n_eta, config.n_phi, config.n_features)
model.build("light")