from keras_bert import get_custom_objects model = load_model("model_weight.h5",custom_objects=get_custom_objects()) 这里说一个小插曲,因为如果不将model导出,直接将训练后的model用于预测是可以使用的,笔者一开始不知道如何解决这个问题就想,索性不使用model.save("model_weight.h5")来导出模型,直接用pick...
converter = lite.TFLiteConverter.from_keras_model_file(h5_file, custom_objects={'contrastive_loss': contrastive_loss}) tflite_model = converter.convert() with open(tflite_file,'wb')asf: f.write(tflite_model) 但是会出现问题,就是报错,转不了。。。原因还未找到,如果不加损失函数,则没有问题,...
'FixedDropout': inject_keras_modules(model.get_dropout)() } get_custom_objects().update(custom_objects) 主程序:比较麻烦,先是用tf.keras加载模型,再存成saved_model格式,同时初始化必要的算子,参考这里: init_keras_custom_objects() keras_model_name = 'efficient_net_b0.h5' keras_model_path = os...
请确保将此对象传递给`custom_objects`参数。EN导入tensorflow_hub作为集线器#提供预先训练的模型需求：使用...
model = load_model('mobilenet.h5', custom_objects={ 'relu6': mobilenet.relu6, 'DepthwiseConv2D': mobilenet.DepthwiseConv2D}) 1. 2. 3. 4. 5. 6. 预训练权值基于Apache License。 DenseNet: 可以选择载入在 ImageNet 上的预训练权值。如果你在使用 TensorFlow 为了发挥最佳性能,请在~/.keras/keras...
custom_objects={"huber_fn": create_huber(2.0)} ) 你可以通过创建tf.keras.losses.Loss类的子类,然后实现它的get_config()方法来解决这个问题: classHuberLoss(tf.keras.losses.Loss):def__init__(self, threshold=1.0, **kwargs): self.threshold = thresholdsuper().__init__(**kwargs)defcall(self...
: Provides a scope that changes to _GLOBAL_CUSTOM_OBJECTS cannot escape. deserialize_keras_object(...) get_custom_objects(...): Retrieves a live reference to the global dictionary of custom objects. get_file(...): Downloads a file from a URL if it not already in the cache. get_...
custom_objects should be a dictionary mapping the names of custom losses / layers / etc to the corresponding functions / classes. Arguments: **kwargs: Additional keyword arguments to be passed to yaml.dump(). Returns: A YAML string. Raises: ImportError: if yaml module is not found. train_...
def get_updates(self, loss, params):grads = self.get_gradients(loss, params)self.updates = [state_ops.assign_add(self.iterations, 1)]t = math_ops.cast(self.iterations, K.floatx()) + 1 # Due to the recommendations in [2], i.e. warming momentum schedule momentum_cache_t = self....
(config, module_objects, custom_objects, printable_module_name) 384 if cls is None: 385 raise ValueError( --> 386 f"Unknown {printable_module_name}: '{class_name}'. " 387 "Please ensure you are using a `keras.utils.custom_object_scope` " 388 "and that this object is included in ...