Python models.load_model方法代碼示例

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def __init__(self, model_name='fcn'):
        """
        Initializes this classifier with a Keras model.

        :param model_name: model name from sub-package models. E.g. 'fma2018-meter'.
        """
        self._to_meter = lambda index: index + 2
        self.model_name = model_name
        # mazurka and deeptemp/shallowtempo models use a different kind of normalization
        self.normalize = std_normalizer if 'dt_maz_v' in self.model_name \
                                           or 'deeptemp' in self.model_name \
                                           or 'deepsquare' in self.model_name \
                                           or 'shallowtemp' in self.model_name \
            else max_normalizer
        resource = _to_model_resource(model_name)
        try:
            file = _extract_from_package(resource)
        except Exception as e:
            print('Failed to find a model named \'{}\'. Please check the model name.'.format(model_name),
                  file=sys.stderr)
            raise e
        try:
            self.model = load_model(file)
        finally:
            os.remove(file) 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def loadFullModel(self, prediction_speed="normal", num_objects=10):
        """
        'loadFullModel()' function is used to load the model structure into the program from the file path defined
        in the setModelPath() function. As opposed to the 'loadModel()' function, you don't need to specify the model type. This means you can load any Keras model trained with or without ImageAI and perform image prediction.
        - prediction_speed (optional), Acceptable values are "normal", "fast", "faster" and "fastest"
        - num_objects (required), the number of objects the model is trained to recognize

        :param prediction_speed:
        :param num_objects:
        :return:
        """

        self.numObjects = num_objects

        if (prediction_speed == "normal"):
            self.__input_image_size = 224
        elif (prediction_speed == "fast"):
            self.__input_image_size = 160
        elif (prediction_speed == "faster"):
            self.__input_image_size = 120
        elif (prediction_speed == "fastest"):
            self.__input_image_size = 100

        if (self.__modelLoaded == False):

            image_input = Input(shape=(self.__input_image_size, self.__input_image_size, 3))


            model = load_model(filepath=self.modelPath)
            self.__model_collection.append(model)
            self.__modelLoaded = True
            self.__modelType = "full" 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def load_model():
    global graph
    graph = tf.get_default_graph()

    global model
    model = VGG16(weights='imagenet',
                  input_shape=input_shape,
                  pooling='max',
                  include_top=False) 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def load_best_model():
    from tensorflow.python.keras.models import load_model
    model = load_model(model_all, custom_objects={'loss_function': dice_coef_loss})
    return model 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def load_checkpoint(self, path):
        # See https://stackoverflow.com/a/42763323
        del self.model
        self.model = load_model(path) 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def load(self, file_path):
        return load_model(file_path) 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def _patch_io_calls(Network, Sequential, keras_saving):
        try:
            if Sequential is not None:
                Sequential._updated_config = _patched_call(Sequential._updated_config,
                                                           PatchKerasModelIO._updated_config)
                if hasattr(Sequential.from_config, '__func__'):
                    # noinspection PyUnresolvedReferences
                    Sequential.from_config = classmethod(_patched_call(Sequential.from_config.__func__,
                                                                       PatchKerasModelIO._from_config))
                else:
                    Sequential.from_config = _patched_call(Sequential.from_config, PatchKerasModelIO._from_config)

            if Network is not None:
                Network._updated_config = _patched_call(Network._updated_config, PatchKerasModelIO._updated_config)
                if hasattr(Sequential.from_config, '__func__'):
                    # noinspection PyUnresolvedReferences
                    Network.from_config = classmethod(_patched_call(Network.from_config.__func__,
                                                                    PatchKerasModelIO._from_config))
                else:
                    Network.from_config = _patched_call(Network.from_config, PatchKerasModelIO._from_config)
                Network.save = _patched_call(Network.save, PatchKerasModelIO._save)
                Network.save_weights = _patched_call(Network.save_weights, PatchKerasModelIO._save_weights)
                Network.load_weights = _patched_call(Network.load_weights, PatchKerasModelIO._load_weights)

            if keras_saving is not None:
                keras_saving.save_model = _patched_call(keras_saving.save_model, PatchKerasModelIO._save_model)
                keras_saving.load_model = _patched_call(keras_saving.load_model, PatchKerasModelIO._load_model)
        except Exception as ex:
            LoggerRoot.get_base_logger(TensorflowBinding).warning(str(ex)) 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def load_model_from_disk(path_to_model_on_disk, compile=True):
    """ Load weights from disk and add to model """
    logging.info("Loading model from: %s" % path_to_model_on_disk)
    loaded_model = load_model(
        path_to_model_on_disk,
        compile=compile,
        custom_objects={
            'accuracy': accuracy,
            'top_k_accuracy': top_k_accuracy,
            'masked_loss_function':
                build_masked_loss(K.sparse_categorical_crossentropy)})
    return loaded_model 

# 需要導入模塊: from tensorflow.python.keras import models [as 別名]
# 或者: from tensorflow.python.keras.models import load_model [as 別名]
def check_model(model, model_name, x, y, check_model_io=True):
    """
    compile model,train and evaluate it,then save/load weight and model file.
    :param model:
    :param model_name:
    :param x:
    :param y:
    :param check_model_io: test save/load model file or not
    :return:
    """

    model.compile('adam', 'binary_crossentropy',
                  metrics=['binary_crossentropy'])
    model.fit(x, y, batch_size=100, epochs=1, validation_split=0.5)

    print(model_name + " test train valid pass!")
    model.save_weights(model_name + '_weights.h5')
    model.load_weights(model_name + '_weights.h5')
    os.remove(model_name + '_weights.h5')
    print(model_name + " test save load weight pass!")
    if check_model_io:
        save_model(model, model_name + '.h5')
        model = load_model(model_name + '.h5', custom_objects)
        os.remove(model_name + '.h5')
        print(model_name + " test save load model pass!")

    print(model_name + " test pass!")