tensorflow

问题 I am trying to implement a face recognition Siamese Network using the Labelled Faces in the Wild (LFW Dataset in Kaggle). The training data image pairs is stored in the format of : ndarray[ndarray[image1,image2],ndarray[image1,image2]...] and so on. The images are RGB channelled with size of 224*224. There are 2200 training pairs with 1100 match image pairs and 1100 mismatch image pairs. Also, there are 1000 test pairs with 500 match image pairs and 500 mismatch image pairs. I have designed

问题 On a jupyter notebook with Tensorflow-2.0.0, a train-validation-test split of 80-10-10 was performed in this way: import tensorflow_datasets as tfds from os import getcwd splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10)) filePath = f"{getcwd()}/../tmp2/" splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits, data_dir=filePath) However, when trying to run the same code locally I get the error AttributeError: type object 'Split' has no attribute 'ALL'

问题 On a jupyter notebook with Tensorflow-2.0.0, a train-validation-test split of 80-10-10 was performed in this way: import tensorflow_datasets as tfds from os import getcwd splits = tfds.Split.ALL.subsplit(weighted=(80, 10, 10)) filePath = f"{getcwd()}/../tmp2/" splits, info = tfds.load('fashion_mnist', with_info=True, as_supervised=True, split=splits, data_dir=filePath) However, when trying to run the same code locally I get the error AttributeError: type object 'Split' has no attribute 'ALL'

问题 The bounty expires in 6 days . Answers to this question are eligible for a +50 reputation bounty. Sarah Messer is looking for a canonical answer : TFX BulkInferrer seems designed to work with database-scale prediction, but that's not the output format. What is the recommended approach when making millions of predictions at once? I'm very new to TFX, but have an apparently-working ML Pipeline which is to be used via BulkInferrer. That seems to produce output exclusively in Protobuf format, but

问题 The bounty expires in 6 days . Answers to this question are eligible for a +50 reputation bounty. Sarah Messer is looking for a canonical answer : TFX BulkInferrer seems designed to work with database-scale prediction, but that's not the output format. What is the recommended approach when making millions of predictions at once? I'm very new to TFX, but have an apparently-working ML Pipeline which is to be used via BulkInferrer. That seems to produce output exclusively in Protobuf format, but

问题 The bounty expires in 6 days . Answers to this question are eligible for a +50 reputation bounty. Sarah Messer is looking for a canonical answer : TFX BulkInferrer seems designed to work with database-scale prediction, but that's not the output format. What is the recommended approach when making millions of predictions at once? I'm very new to TFX, but have an apparently-working ML Pipeline which is to be used via BulkInferrer. That seems to produce output exclusively in Protobuf format, but

问题 I'm trying to use tensorboard on my local machine to read tensorflow logs on S3. Everything works but tensorboard continuously throws the following errors to the console. According to this the reason is that when Tensorflow s3 client checks if directory exists it firstly run Stat on it since s3 have no possibility to check whether directory exists. Then it checks if key with such name exists and fails with such error messages. While this could be a wanted behavior for model serving to look

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception

问题 I trained an SSD MobileNet v2 network using the TensorFlow Object Detection API with TensorFlow 2 and then converted the trained model into a SavedModel. Now I need to convert the SavedModel to a FrozenGraph in order to make the model compatible with external libraries like OpenCV. I use this example for conversion and I cannot even load the Keras model. from keras.models import load_model model = load_model("training/model/saved_model") Calling load_model() produces an exception: Exception