def ctc_data(): """ Returns a provider that can be used with `ctc_model()`. """ number_of_samples = 11 vocab_size = 4 # Same as above output_timesteps = 10 maximum_transcription_length = 4 # Must be <= output_timesteps return BatchProvider( sources={ 'TEST_input': VanillaSource( numpy.random.uniform(low=-1, high=1, size=(number_of_samples, output_timesteps, 2))), 'TEST_transcription': VanillaSource( numpy.random.random_integers(0, vocab_size - 1, size=(number_of_samples, maximum_transcription_length ))), 'TEST_input_length': VanillaSource( numpy.ones(shape=(number_of_samples, 1)) * output_timesteps), 'TEST_transcription_length': VanillaSource( numpy.random.random_integers(1, maximum_transcription_length, size=(number_of_samples, 1))) })
def simple_data(): """ Returns a small provider that can be used to train the `simple_model()`. """ return BatchProvider( sources={ 'TEST_input': VanillaSource(numpy.random.uniform(size=(100, 10))), 'TEST_output': VanillaSource(numpy.random.uniform(size=(100, 1))) })
def embedding_data(): """ Returns a small provider that can be used to train the `embedding_model()`. """ return BatchProvider( sources={ 'TEST_input': VanillaSource(numpy.random.random_integers(0, 99, size=(5, 10))), 'TEST_output': VanillaSource(numpy.random.uniform(size=(5, 3))) })
def uber_data(): """ In the land of Mordor, where the shadows lie. Data for the uber model. """ return BatchProvider( sources={ 'TEST_input': VanillaSource( numpy.random.uniform(low=-1, high=1, size=(2, 32, 32))), 'TEST_output': VanillaSource(numpy.random.uniform(low=-1, high=1, size=(2, 140))) })
def ctc_eval_data(ctc_data): """ Provides CTC data with only the evaluation-time input data available. """ for key, source in zip(ctc_data.keys, ctc_data.sources): if key == 'TEST_input': return BatchProvider(sources={key: source})