8911d5b7ead77719b8e3c998228558c4eb73e0dd,tests/NMT_architectures/attention_ConditionalGRU.py,,test_ConditionalGRU_add,#,12

Before Change

    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)

After Change

    print("Scoring corpus")
    score_corpus(parser, params)
    print("Done")
    clean_dirs(params)


def test_ConditionalGRU_dot():
    params = load_tests_params()

Instances