b78d0685b201fa433507a4d4e1d0a0f70a0dc783,scripts/keras_benchmarks/models/lstm_text_generation_benchmark.py,LstmTextGenBenchmark,benchmarkLstmTextGen,#LstmTextGenBenchmark#Any#Any#,37

Before Change

    path = get_file("nietzsche.txt", origin="https://s3.amazonaws.com/text-datasets/nietzsche.txt")
    text = open(path).read().lower()
    print("corpus length:", len(text))

    chars = sorted(list(set(text)))
    print("total chars:", len(chars))
    char_indices = dict((c, i) for i, c in enumerate(chars))

After Change

    optimizer = RMSprop(lr=0.01)

    if str(keras_backend) is "tensorflow" and gpu_count > 1:
        model = multi_gpu_model(model, gpus=gpu_count)

    model.compile(loss="categorical_crossentropy", optimizer=optimizer)

    // train the model, output generated text after each iteration
    start_time = time.time()

Instances