Python keras.utils方法代碼示例

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def test_ApogeeKplerEchelle(self):
        """
        Test ApogeeKplerEchelle models
        - training, testing
        """
        # Data preparation, keep the data size large (>800 data points to prevent issues)
        (x_train, y_train), (x_test, y_test) = mnist.load_data()
        y_train = utils.to_categorical(y_train, 10)
        y_test = utils.to_categorical(y_test, 10)
        # To convert to desirable type
        y_train = y_train.astype(np.float32)
        y_test = y_test.astype(np.float32)
        x_train = x_train.astype(np.float32)
        x_test = x_test.astype(np.float32)

        print("======ApogeeKplerEchelle======")
        apokasc_nn = ApogeeKplerEchelle()
        apokasc_nn.max_epochs = 2
        apokasc_nn.dropout_rate = 0.
        apokasc_nn.input_norm_mode = {'input': 255, 'aux': 0}
        apokasc_nn.labels_norm_mode = 0
        apokasc_nn.task = 'classification'
        apokasc_nn.callbacks = ErrorOnNaN()
        apokasc_nn.train({'input': x_train, 'aux': y_train}, {'output': y_train})
        prediction = apokasc_nn.test({'input': x_train, 'aux': y_train})
        # we ave the answer as aux input so the prediction should be near perfect
        total_num = y_train.shape[0]
        assert np.sum((prediction>0.5) == (y_train>0.5)) > total_num * 0.99
        apokasc_nn.save(name='apokasc_nn')

        apokasc_nn_reloaded = load_folder('apokasc_nn')
        prediction_reloaded = apokasc_nn_reloaded.test({'input': x_train, 'aux': y_train})
        np.testing.assert_array_equal(prediction, prediction_reloaded) 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def test_mnist(self):
        # create model instance
        mnist_test = Cifar10CNN()
        mnist_test.max_epochs = 1
        mnist_test.callbacks = ErrorOnNaN()

        mnist_test.train(x_train, y_train)
        output_shape = mnist_test.output_shape
        pred = mnist_test.test(x_test)
        test_num = y_test.shape[0]
        assert (np.sum(np.argmax(pred, axis=1) == y_test)) / test_num > 0.9  # assert accurancy
        mnist_test.evaluate(x_test, utils.to_categorical(y_test, 10))

        # create model instance for binary classification
        mnist_test = Cifar10CNN()
        mnist_test.max_epochs = 2
        mnist_test.task = 'binary_classification'

        mnist_test.train(x_train, y_train.astype(bool))
        prediction = mnist_test.test(x_test)
        assert (np.sum(np.argmax(prediction, axis=1) == y_test)) / test_num > 0.9  # assert accuracy
        mnist_test.save('mnist_test')
        mnist_reloaded = load_folder("mnist_test")
        prediction_loaded = mnist_reloaded.test(x_test)
        eval_result = mnist_reloaded.evaluate(x_test, utils.to_categorical(y_test, 10))

        # Cifar10_CNN without dropout is deterministic
        np.testing.assert_array_equal(prediction, prediction_loaded)

        # test verbose metrics
        mnist_reloaded.metrics = ['accuracy']
        mnist_reloaded.compile()
        mnist_test.save('mnist_test_accuracy')
        mnist_reloaded_again = load_folder("mnist_test_accuracy")
        # test with astype boolean deliberately
        eval_result_again = mnist_reloaded_again.evaluate(x_test, utils.to_categorical(y_test, 10).astype(bool))
        # assert saving again wont affect the model
        self.assertAlmostEqual(eval_result_again['loss'], eval_result['loss'], places=3) 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def test_bayesian_mnist(self):
        import pylab as plt

        # Create a astroNN neural network instance and set the basic parameter
        net = MNIST_BCNN()
        net.task = 'classification'
        net.callbacks = ErrorOnNaN()
        net.max_epochs = 1

        # Train the neural network
        net.train(x_train, y_train)
        net.save('mnist_bcnn_test')
        net.plot_dense_stats()
        plt.close()  # Travis-CI memory error??
        net.evaluate(x_test, utils.to_categorical(y_test, 10))

        pred, pred_err = net.test(x_test)
        test_num = y_test.shape[0]
        assert (np.sum(pred == y_test)) / test_num > 0.9  # assert accuracy

        net_reloaded = load_folder("mnist_bcnn_test")
        net_reloaded.mc_num = 3  # prevent memory issue on Tavis CI
        prediction_loaded = net_reloaded.test(x_test[:200])

        net_reloaded.folder_name = None  # set to None so it can be saved
        net_reloaded.save()

        load_folder(net_reloaded.folder_name)  # ignore pycharm warning, its not None 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def get_kwargs():
        return {
            'backend': tfkeras.backend,
            'layers': tfkeras.layers,
            'models': tfkeras.models,
            'utils': tfkeras.utils,
        } 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def get_submodules_from_kwargs(kwargs):
    backend = kwargs.get('backend', _KERAS_BACKEND)
    layers = kwargs.get('layers', _KERAS_LAYERS)
    models = kwargs.get('models', _KERAS_MODELS)
    utils = kwargs.get('utils', _KERAS_UTILS)
    for key in kwargs.keys():
        if key not in ['backend', 'layers', 'models', 'utils']:
            raise TypeError('Invalid keyword argument: %s', key)
    return backend, layers, models, utils 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def inject_keras_modules(func):
    import keras
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        kwargs['backend'] = keras.backend
        kwargs['layers'] = keras.layers
        kwargs['models'] = keras.models
        kwargs['utils'] = keras.utils
        return func(*args, **kwargs)

    return wrapper 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def inject_tfkeras_modules(func):
    import tensorflow.keras as tfkeras
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        kwargs['backend'] = tfkeras.backend
        kwargs['layers'] = tfkeras.layers
        kwargs['models'] = tfkeras.models
        kwargs['utils'] = tfkeras.utils
        return func(*args, **kwargs)

    return wrapper 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def init_keras_custom_objects():
    import keras
    from . import model

    custom_objects = {
        'swish': inject_keras_modules(model.get_swish)(),
        'FixedDropout': inject_keras_modules(model.get_dropout)()
    }

    keras.utils.generic_utils.get_custom_objects().update(custom_objects) 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def init_tfkeras_custom_objects():
    import tensorflow.keras as tfkeras
    from . import model

    custom_objects = {
        'swish': inject_tfkeras_modules(model.get_swish)(),
        'FixedDropout': inject_tfkeras_modules(model.get_dropout)()
    }

    tfkeras.utils.get_custom_objects().update(custom_objects) 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def init_keras_custom_objects():
    import keras
    import efficientnet as model

    custom_objects = {
        'swish': inject_keras_modules(model.get_swish)(),
        'FixedDropout': inject_keras_modules(model.get_dropout)()
    }

    keras.utils.generic_utils.get_custom_objects().update(custom_objects) 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def init_tfkeras_custom_objects():
    import tensorflow.keras as tfkeras
    import efficientnet as model

    custom_objects = {
        'swish': inject_tfkeras_modules(model.get_swish)(),
        'FixedDropout': inject_tfkeras_modules(model.get_dropout)()
    }

    tfkeras.utils.get_custom_objects().update(custom_objects) 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def inject_global_submodules(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        kwargs['backend'] = _KERAS_BACKEND
        kwargs['layers'] = _KERAS_LAYERS
        kwargs['models'] = _KERAS_MODELS
        kwargs['utils'] = _KERAS_UTILS
        return func(*args, **kwargs)

    return wrapper 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def filter_kwargs(func):
    @functools.wraps(func)
    def wrapper(*args, **kwargs):
        new_kwargs = {k: v for k, v in kwargs.items() if k in ['backend', 'layers', 'models', 'utils']}
        return func(*args, **new_kwargs)

    return wrapper 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def get_preprocessing(name):
    preprocess_input = Backbones.get_preprocessing(name)
    # add bakcend, models, layers, utils submodules in kwargs
    preprocess_input = inject_global_submodules(preprocess_input)
    # delete other kwargs
    # keras-applications preprocessing raise an error if something
    # except `backend`, `layers`, `models`, `utils` passed in kwargs
    preprocess_input = filter_kwargs(preprocess_input)
    return preprocess_input 

# 需要導入模塊: from tensorflow import keras [as 別名]
# 或者: from tensorflow.keras import utils [as 別名]
def set_framework(name):
    """Set framework for Segmentation Models

    Args:
        name (str): one of ``keras``, ``tf.keras``, case insensitive.

    Raises:
        ValueError: in case of incorrect framework name.
        ImportError: in case framework is not installed.

    """
    name = name.lower()

    if name == _KERAS_FRAMEWORK_NAME:
        import keras
        import efficientnet.keras  # init custom objects
    elif name == _TF_KERAS_FRAMEWORK_NAME:
        from tensorflow import keras
        import efficientnet.tfkeras  # init custom objects
    else:
        raise ValueError('Not correct module name `{}`, use `{}` or `{}`'.format(
            name, _KERAS_FRAMEWORK_NAME, _TF_KERAS_FRAMEWORK_NAME))

    global _KERAS_BACKEND, _KERAS_LAYERS, _KERAS_MODELS
    global _KERAS_UTILS, _KERAS_LOSSES, _KERAS_FRAMEWORK

    _KERAS_FRAMEWORK = name
    _KERAS_BACKEND = keras.backend
    _KERAS_LAYERS = keras.layers
    _KERAS_MODELS = keras.models
    _KERAS_UTILS = keras.utils
    _KERAS_LOSSES = keras.losses

    # allow losses/metrics get keras submodules
    base.KerasObject.set_submodules(
        backend=keras.backend,
        layers=keras.layers,
        models=keras.models,
        utils=keras.utils,
    )


# set default framework