tensorflow-datasets

问题 I have trained my custom data-set using the Tensor Flow Object Detection API. I run my "prediction" script and it works fine on the GPU. Now , I want to convert the model to lite and run it on the Google Coral Edge TPU Board to detect my custom objects. I have gone through the documentation that Google Coral Board Website provides but I found it very confusing. How to convert and run it on the Google Coral Edge TPU Board? Thanks 回答1: Without reading the documentation, it will be very hard to

问题 The input and labels of my dataset is stored in 10000 .npy files each. For example inputs/0000.npy,...inputs/9999.npy and labels/0000.npy,...labels/9999.npy . While each file independently can be stored in memory, the whole dataset of 20k arrays cannot be stored in memory. I would like to implement multi-threaded CPU pipeline to import the dataset as batches of say batch_size=8 . I have tried to implement the functions mentioned in the new Tensorflow data API but haven't found any example for

问题 The input and labels of my dataset is stored in 10000 .npy files each. For example inputs/0000.npy,...inputs/9999.npy and labels/0000.npy,...labels/9999.npy . While each file independently can be stored in memory, the whole dataset of 20k arrays cannot be stored in memory. I would like to implement multi-threaded CPU pipeline to import the dataset as batches of say batch_size=8 . I have tried to implement the functions mentioned in the new Tensorflow data API but haven't found any example for

问题 When I load a dataset, I wonder if there is any quick way to find the number of samples or batches in that dataset. I know that if I load a dataset with with_info=True , I can see for example total_num_examples=6000, but this information is not available if I split a dataset. Currently, I count the number of samples as follows, but wondering if there is any better solution: train_subsplit_1, train_subsplit_2, train_subsplit_3 = tfds.Split.TRAIN.subsplit(3) cifar10_trainsub3 = tfds.load(

问题 I have a TFRecords file which contains images with their labels, name, size, etc. My goal is to extract the label and the image as a numpy array. I do the following to load the file: def extract_fn(data_record): features = { # Extract features using the keys set during creation "image/class/label": tf.FixedLenFeature([], tf.int64), "image/encoded": tf.VarLenFeature(tf.string), } sample = tf.parse_single_example(data_record, features) #sample = tf.cast(sample["image/encoded"], tf.float32)

问题 I have a TFRecords file which contains images with their labels, name, size, etc. My goal is to extract the label and the image as a numpy array. I do the following to load the file: def extract_fn(data_record): features = { # Extract features using the keys set during creation "image/class/label": tf.FixedLenFeature([], tf.int64), "image/encoded": tf.VarLenFeature(tf.string), } sample = tf.parse_single_example(data_record, features) #sample = tf.cast(sample["image/encoded"], tf.float32)

问题 I have a TFRecords file which contains images with their labels, name, size, etc. My goal is to extract the label and the image as a numpy array. I do the following to load the file: def extract_fn(data_record): features = { # Extract features using the keys set during creation "image/class/label": tf.FixedLenFeature([], tf.int64), "image/encoded": tf.VarLenFeature(tf.string), } sample = tf.parse_single_example(data_record, features) #sample = tf.cast(sample["image/encoded"], tf.float32)

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