def create_params(label2ids):
params = Params()
params.max_sentence_length = label2ids['max_sentence_length']
params.max_n_analyses = label2ids['max_n_analysis']
params.batch_size = 1
params.n_subepochs = 40
params.max_surface_form_length = label2ids['max_surface_form_length']
params.max_word_root_length = label2ids['max_word_root_length']
params.max_analysis_length = label2ids['max_analysis_length']
params.char_vocabulary_size = label2ids['character_unique_count']['value']
params.tag_vocabulary_size = label2ids['morph_token_unique_count']['value']
params.char_lstm_dim = 100
params.char_embedding_dim = 100
params.tag_lstm_dim = params.char_lstm_dim
params.tag_embedding_dim = 100
params.sentence_level_lstm_dim = 2 * params.char_lstm_dim
return params
params.n_subepochs = 40
params.max_surface_form_length = label2ids['max_surface_form_length']
params.max_word_root_length = label2ids['max_word_root_length']
params.max_analysis_length = label2ids['max_analysis_length']
params.char_vocabulary_size = label2ids['character_unique_count']['value']
params.tag_vocabulary_size = label2ids['morph_token_unique_count']['value']
params.char_lstm_dim = 100
params.char_embedding_dim = 100
params.tag_lstm_dim = params.char_lstm_dim
params.tag_embedding_dim = 100
params.sentence_level_lstm_dim = 2 * params.char_lstm_dim
# train_and_test_sentences, label2ids = read_datafile("test.merge.utf8", "test.merge.utf8")
model = build_model(params)
model.compile(optimizer='rmsprop',
loss='categorical_crossentropy',
metrics=['accuracy'])
from keras.callbacks import ModelCheckpoint, TensorBoard, ReduceLROnPlateau
checkpointer = ModelCheckpoint(filepath="./models/ntd-{run_name}".format(run_name=args.run_name)
+ '.epoch-{epoch:02d}-val_acc-{val_acc:.5f}.hdf5',
verbose=1,
monitor="val_acc",