def test_ConditionalLSTM_dot():
params = load_tests_params()
# Current test params: Single layered LSTM - ConditionalGRU
params['BIDIRECTIONAL_ENCODER'] = True
params['N_LAYERS_ENCODER'] = 1
params['BIDIRECTIONAL_DEEP_ENCODER'] = True
params['ENCODER_RNN_TYPE'] = 'LSTM'
params['DECODER_RNN_TYPE'] = 'ConditionalLSTM'
params['N_LAYERS_DECODER'] = 1
params['ATTENTION_MODE'] = 'dot'
params['REBUILD_DATASET'] = True
dataset = build_dataset(params)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
params['MODEL_NAME'] = \
params['TASK_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '_' + params['MODEL_TYPE'] + \
'_src_emb_' + str(params['SOURCE_TEXT_EMBEDDING_SIZE']) + \
'_bidir_' + str(params['BIDIRECTIONAL_ENCODER']) + \
'_enc_' + params['ENCODER_RNN_TYPE'] + '_*' + str(params['N_LAYERS_ENCODER']) + '_' + str(
params['ENCODER_HIDDEN_SIZE']) + \
'_dec_' + params['DECODER_RNN_TYPE'] + '_*' + str(params['N_LAYERS_DECODER']) + '_' + str(
params['DECODER_HIDDEN_SIZE']) + params['ATTENTION_MODE'] + \
'_deepout_' + '_'.join([layer[0] for layer in params['DEEP_OUTPUT_LAYERS']]) + \
'_trg_emb_' + str(params['TARGET_TEXT_EMBEDDING_SIZE']) + \
'_' + params['OPTIMIZER'] + '_' + str(params['LR'])
params['STORE_PATH'] = os.path.join(K.backend() + '_test_train_models', params['MODEL_NAME'])
# Test several NMT-Keras utilities: train, sample, sample_ensemble, score_corpus...
print("Training model")
train_model(params)
params['RELOAD'] = 1
print("Done")
parser = argparse.ArgumentParser('Parser for unit testing')
parser.dataset = os.path.join(
params['DATASET_STORE_PATH'],
'Dataset_' + params['DATASET_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
parser.text = os.path.join(params['DATA_ROOT_PATH'], params['TEXT_FILES']['val'] + params['SRC_LAN'])
parser.splits = ['val']
parser.config = params['STORE_PATH'] + '/config.pkl'
parser.models = [params['STORE_PATH'] + '/epoch_' + str(1)]
parser.verbose = 0
parser.dest = None
parser.source = os.path.join(params['DATA_ROOT_PATH'], params['TEXT_FILES']['val'] + params['SRC_LAN'])
parser.target = os.path.join(params['DATA_ROOT_PATH'], params['TEXT_FILES']['val'] + params['TRG_LAN'])
parser.weights = []
parser.glossary = None
for n_best in [True, False]:
parser.n_best = n_best
print("Sampling with n_best = %s " % str(n_best))
sample_ensemble(parser, params)
print("Done")
print("Scoring corpus")
score_corpus(parser, params)
print("Done")
def test_unk_replace_1():
params = load_tests_params()
params['REBUILD_DATASET'] = True
params['INPUT_VOCABULARY_SIZE'] = 0
params['OUTPUT_VOCABULARY_SIZE'] = 50
params['POS_UNK'] = True
params['HEURISTIC'] = 1
params['ALIGN_FROM_RAW'] = True
dataset = build_dataset(params)
# params['MAPPING'] = DATA_ROOT_PATH + '/mapping.%s_%s.pkl' % (SRC_LAN, TRG_LAN)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[params['OUTPUTS_IDS_DATASET'][0]]
params['MODEL_NAME'] = \
params['TASK_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '_' + params['MODEL_TYPE'] + \
'_src_emb_' + str(params['SOURCE_TEXT_EMBEDDING_SIZE']) + \
'_bidir_' + str(params['BIDIRECTIONAL_ENCODER']) + \
'_enc_' + params['ENCODER_RNN_TYPE'] + '_*' + str(params['N_LAYERS_ENCODER']) + '_' + str(
params['ENCODER_HIDDEN_SIZE']) + \
'_dec_' + params['DECODER_RNN_TYPE'] + '_*' + str(params['N_LAYERS_DECODER']) + '_' + str(
params['DECODER_HIDDEN_SIZE']) + \
'_deepout_' + '_'.join([layer[0] for layer in params['DEEP_OUTPUT_LAYERS']]) + \
'_trg_emb_' + str(params['TARGET_TEXT_EMBEDDING_SIZE']) + \
'_' + params['OPTIMIZER'] + '_' + str(params['LR'])
params['STORE_PATH'] = os.path.join(K.backend() + '_test_train_models', params['MODEL_NAME'])
# Test several NMT-Keras utilities: train, sample, sample_ensemble, score_corpus...
print("Training model")
train_model(params)
params['RELOAD'] = 1
print("Done")
parser = argparse.ArgumentParser('Parser for unit testing')
parser.dataset = os.path.join(
params['DATASET_STORE_PATH'],
'Dataset_' + params['DATASET_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
parser.text = os.path.join(params['DATA_ROOT_PATH'], params['TEXT_FILES']['val'] + params['SRC_LAN'])
parser.splits = ['val']
parser.config = os.path.join(params['STORE_PATH'], 'config.pkl')
parser.models = [os.path.join(params['STORE_PATH'], 'epoch_' + str(1))]
parser.verbose = 0
parser.dest = None
parser.source = os.path.join(params['DATA_ROOT_PATH'], params['TEXT_FILES']['val'] + params['SRC_LAN'])
parser.target = os.path.join(params['DATA_ROOT_PATH'], params['TEXT_FILES']['val'] + params['TRG_LAN'])
parser.weights = []
parser.glossary = None
for n_best in [True, False]:
parser.n_best = n_best
print("Sampling with n_best = %s " % str(n_best))
sample_ensemble(parser, params)
print("Done")
print("Scoring corpus")
score_corpus(parser, params)
print("Done")
def load_transformer_test_params():
params = load_tests_params()
params['MODEL_TYPE'] = 'Transformer'
params['N_LAYERS_ENCODER'] = 2
params['N_LAYERS_DECODER'] = 2
params['MULTIHEAD_ATTENTION_ACTIVATION'] = 'relu'
params['MODEL_SIZE'] = 8
params['FF_SIZE'] = params['MODEL_SIZE'] * 4
params['N_HEADS'] = 2
params['REBUILD_DATASET'] = True
params['OPTIMIZED_SEARCH'] = True
params['POS_UNK'] = False
return params
def test_sampling_maxlikelihood():
params = load_tests_params()
params['REBUILD_DATASET'] = True
params['INPUT_VOCABULARY_SIZE'] = 550
params['OUTPUT_VOCABULARY_SIZE'] = 550
params['POS_UNK'] = True
params['HEURISTIC'] = 0
params['ALIGN_FROM_RAW'] = True
# Sampling params: Show some samples during training.
params['SAMPLE_ON_SETS'] = ['train', 'val']
params['N_SAMPLES'] = 10
params['START_SAMPLING_ON_EPOCH'] = 0
params['SAMPLE_EACH_UPDATES'] = 50
params['SAMPLING'] = 'max_likelihood'
dataset = build_dataset(params)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
params['MODEL_NAME'] = \
params['TASK_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '_' + params['MODEL_TYPE'] + \
'_src_emb_' + str(params['SOURCE_TEXT_EMBEDDING_SIZE']) + \
'_bidir_' + str(params['BIDIRECTIONAL_ENCODER']) + \
'_enc_' + params['ENCODER_RNN_TYPE'] + '_*' + str(params['N_LAYERS_ENCODER']) + '_' + str(
params['ENCODER_HIDDEN_SIZE']) + \
'_dec_' + params['DECODER_RNN_TYPE'] + '_*' + str(params['N_LAYERS_DECODER']) + '_' + str(
params['DECODER_HIDDEN_SIZE']) + \
'_deepout_' + '_'.join([layer[0] for layer in params['DEEP_OUTPUT_LAYERS']]) + \
'_trg_emb_' + str(params['TARGET_TEXT_EMBEDDING_SIZE']) + \
'_' + params['OPTIMIZER'] + '_' + str(params['LR'])
params['STORE_PATH'] = os.path.join(K.backend() + '_test_train_models',
params['MODEL_NAME'])
# Test several NMT-Keras utilities: train, sample, sample_ensemble, score_corpus...
print("Training model")
train_model(params)
print("Done")
def test_transformer():
params = load_tests_params()
# Current test params: Transformer
params['MODEL_TYPE'] = 'Transformer'
params['TIED_EMBEDDINGS'] = True
params['N_LAYERS_ENCODER'] = 2
params['N_LAYERS_DECODER'] = 2
params['MULTIHEAD_ATTENTION_ACTIVATION'] = 'relu'
params['MODEL_SIZE'] = 8
params['FF_SIZE'] = params['MODEL_SIZE'] * 4
params['N_HEADS'] = 2
params['REBUILD_DATASET'] = True
params['OPTIMIZED_SEARCH'] = True
params['POS_UNK'] = False
dataset = build_dataset(params)
params['INPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['INPUTS_IDS_DATASET'][0]]
params['OUTPUT_VOCABULARY_SIZE'] = dataset.vocabulary_len[
params['OUTPUTS_IDS_DATASET'][0]]
params['MODEL_NAME'] = \
params['TASK_NAME'] + '_' + params['SRC_LAN'] + params['TRG_LAN'] + '_' + params['MODEL_TYPE'] + \
'_model_size_' + str(params['MODEL_SIZE']) + \
'_ff_size_' + str(params['FF_SIZE']) + \
'_num_heads_' + str(params['N_HEADS']) + \
'_encoder_blocks_' + str(params['N_LAYERS_ENCODER']) + \
'_decoder_blocks_' + str(params['N_LAYERS_DECODER']) + \
'_deepout_' + '_'.join([layer[0] for layer in params['DEEP_OUTPUT_LAYERS']]) + \
'_' + params['OPTIMIZER'] + '_' + str(params['LR'])
# Test several NMT-Keras utilities: train, sample, sample_ensemble, score_corpus...
print("Training model")
train_model(params)
params['RELOAD'] = 1
print("Done")
parser = argparse.ArgumentParser('Parser for unit testing')
parser.dataset = os.path.join(
params['DATASET_STORE_PATH'], 'Dataset_' + params['DATASET_NAME'] +
'_' + params['SRC_LAN'] + params['TRG_LAN'] + '.pkl')
parser.text = os.path.join(params['DATA_ROOT_PATH'],
params['TEXT_FILES']['val'] + params['SRC_LAN'])
parser.splits = ['val']
parser.config = params['STORE_PATH'] + '/config.pkl'
parser.models = [params['STORE_PATH'] + '/epoch_' + str(1)]
parser.verbose = 0
parser.dest = None
parser.source = os.path.join(
params['DATA_ROOT_PATH'],
params['TEXT_FILES']['val'] + params['SRC_LAN'])
parser.target = os.path.join(
params['DATA_ROOT_PATH'],
params['TEXT_FILES']['val'] + params['TRG_LAN'])
parser.weights = []
parser.glossary = None
for n_best in [True, False]:
parser.n_best = n_best
print("Sampling with n_best = %s " % str(n_best))
sample_ensemble(parser, params)
print("Done")
print("Scoring corpus")
score_corpus(parser, params)
print("Done")
clean_dirs(params)
