tensorflow2.0

问题 def __init__(self, **kwargs): self.__dict__.update(self._defaults) # set up default values self.__dict__.update(kwargs) # and update with user overrides self.class_names = self._get_class() self.anchors = self._get_anchors() self.sess = K.get_session() RuntimeError: get_session is not available when using TensorFlow 2.0. 回答1: Tensorflow 2.0 does not expose the backend.get_session directly any more but the code still there and expose for tf1. https://github.com/tensorflow/tensorflow/blob/r2.0

问题 def __init__(self, **kwargs): self.__dict__.update(self._defaults) # set up default values self.__dict__.update(kwargs) # and update with user overrides self.class_names = self._get_class() self.anchors = self._get_anchors() self.sess = K.get_session() RuntimeError: get_session is not available when using TensorFlow 2.0. 回答1: Tensorflow 2.0 does not expose the backend.get_session directly any more but the code still there and expose for tf1. https://github.com/tensorflow/tensorflow/blob/r2.0

问题 How to install tf on coral dev? Getting errors following this on coral dev board like compile.sh not found etc. Please give detailed explaination. 回答1: It is really not going to be possible to help if you don't give details on what you've done or what errors you ran into while trying to install it. However, since the objective is to install tensorflow on the board, you can just do this using this pre-built package: $ wget https://github.com/lhelontra/tensorflow-on-arm/releases/download/v2.0.0

问题 I am using Tensorflow 2.0 and facing the following situation: @tf.function def my_fn(items): .... #do stuff return If items is a dict of Tensors like for example: item1 = tf.zeros([1, 1]) item2 = tf.zeros(1) items = {"item1": item1, "item2": item2} Is there a way of using input_signature argument of tf.function so I can force tf2 to avoid creating multiple graphs when item1 is for example tf.zeros([2,1]) ? 来源： https://stackoverflow.com/questions/60827999/use-dictionary-in-tf-function-input

问题 I am using Tensorflow 2.0 and facing the following situation: @tf.function def my_fn(items): .... #do stuff return If items is a dict of Tensors like for example: item1 = tf.zeros([1, 1]) item2 = tf.zeros(1) items = {"item1": item1, "item2": item2} Is there a way of using input_signature argument of tf.function so I can force tf2 to avoid creating multiple graphs when item1 is for example tf.zeros([2,1]) ? 来源： https://stackoverflow.com/questions/60827999/use-dictionary-in-tf-function-input

问题 I am using Tensorflow 2.0 and facing the following situation: @tf.function def my_fn(items): .... #do stuff return If items is a dict of Tensors like for example: item1 = tf.zeros([1, 1]) item2 = tf.zeros(1) items = {"item1": item1, "item2": item2} Is there a way of using input_signature argument of tf.function so I can force tf2 to avoid creating multiple graphs when item1 is for example tf.zeros([2,1]) ? 来源： https://stackoverflow.com/questions/60827999/use-dictionary-in-tf-function-input

问题 When using TensorFlow's tf.data.experimental.sample_from_datasets to equally sample from two very unbalanced Datasets, I end up getting a DirectedInterleave selected an exhausted input: 0 warning. Based on this GitHub issue, it appears that this is occurring when one of the Datasets inside the sample_from_datasets has been depleted of examples, and would need to sample already seen examples. Does the depleted dataset then still produce samples (thereby maintaining the desired balanced

问题 When using TensorFlow's tf.data.experimental.sample_from_datasets to equally sample from two very unbalanced Datasets, I end up getting a DirectedInterleave selected an exhausted input: 0 warning. Based on this GitHub issue, it appears that this is occurring when one of the Datasets inside the sample_from_datasets has been depleted of examples, and would need to sample already seen examples. Does the depleted dataset then still produce samples (thereby maintaining the desired balanced

问题 When using TensorFlow's tf.data.experimental.sample_from_datasets to equally sample from two very unbalanced Datasets, I end up getting a DirectedInterleave selected an exhausted input: 0 warning. Based on this GitHub issue, it appears that this is occurring when one of the Datasets inside the sample_from_datasets has been depleted of examples, and would need to sample already seen examples. Does the depleted dataset then still produce samples (thereby maintaining the desired balanced

问题 I'm following this guide. It shows how to download datasets from the new TensorFlow Datasets using tfds.load() method: import tensorflow_datasets as tfds SPLIT_WEIGHTS = (8, 1, 1) splits = tfds.Split.TRAIN.subsplit(weighted=SPLIT_WEIGHTS) (raw_train, raw_validation, raw_test), metadata = tfds.load( 'cats_vs_dogs', split=list(splits), with_info=True, as_supervised=True) The next steps shows how to apply a function to each item in the dataset using map method: def format_example(image, label):