tensorflow-datasets

问题 In all the tutorials i've seen for tensorflow, they've used the MNIST dataset, i've understood the modelling but how do i load this dataset into tensorflow? https://www.nist.gov/itl/iad/image-group/emnist-dataset 回答1: The EMNIST dataset uses the same binary format as the original MNIST dataset. Therefore you can take the input pipeline code from any tutorial that uses the original MNIST dataset, and point it at the set of files you get from downloading the EMNIST dataset to train on that

问题 I made a TensorFlow CsvDataset , and I'm trying to tokenize the data as such: import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' from tensorflow import keras import tensorflow as tf from tensorflow.keras.preprocessing.text import Tokenizer import os os.chdir('/home/nicolas/Documents/Datasets') fname = 'rotten_tomatoes_reviews.csv' def preprocess(target, inputs): tok = Tokenizer(num_words=5_000, lower=True) tok.fit_on_texts(inputs) vectors = tok.texts_to_sequences(inputs) return vectors,

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 It is recommended to use tensorflow dataset as the input pipeline which can be set up as follows: # Specify dataset dataset = tf.data.Dataset.from_tensor_slices((features, labels)) # Suffle dataset = dataset.shuffle(buffer_size=1e5) # Specify batch size dataset = dataset.batch(128) # Create an iterator iterator = dataset.make_one_shot_iterator() # Get next batch next_batch = iterator.get_next() I should be able to get the batch size (either from dataset itself or from an iterator created from

问题 It is recommended to use tensorflow dataset as the input pipeline which can be set up as follows: # Specify dataset dataset = tf.data.Dataset.from_tensor_slices((features, labels)) # Suffle dataset = dataset.shuffle(buffer_size=1e5) # Specify batch size dataset = dataset.batch(128) # Create an iterator iterator = dataset.make_one_shot_iterator() # Get next batch next_batch = iterator.get_next() I should be able to get the batch size (either from dataset itself or from an iterator created from

问题 there are plenty of examples how to create and use TensorFlow datasets, e.g. dataset = tf.data.Dataset.from_tensor_slices((images, labels)) My question is how to get back the data/labels from the TF dataset in numpy form? In other words want would be reverse operation of the line above, i.e. I have a TF dataset and want to get back images and labels from it. 回答1: Supposing our tf.data.Dataset is called train_dataset , with eager_execution on, you can retrieve images and labels like this: for

问题 there are plenty of examples how to create and use TensorFlow datasets, e.g. dataset = tf.data.Dataset.from_tensor_slices((images, labels)) My question is how to get back the data/labels from the TF dataset in numpy form? In other words want would be reverse operation of the line above, i.e. I have a TF dataset and want to get back images and labels from it. 回答1: Supposing our tf.data.Dataset is called train_dataset , with eager_execution on, you can retrieve images and labels like this: for

问题 there are plenty of examples how to create and use TensorFlow datasets, e.g. dataset = tf.data.Dataset.from_tensor_slices((images, labels)) My question is how to get back the data/labels from the TF dataset in numpy form? In other words want would be reverse operation of the line above, i.e. I have a TF dataset and want to get back images and labels from it. 回答1: Supposing our tf.data.Dataset is called train_dataset , with eager_execution on, you can retrieve images and labels like this: for

问题 I'm attempting to train a Unet to provide each pixel of a 256x256 image with a label, similar to the tutorial given here. In the example, the predictions of the Unet are a (128x128x3) output where the 3 denotes one of the classifications assigned to each pixel. In my case, I need a (256x256x10) output having 10 different classifications (Essentially a one-hot encoded array for each pixel in the image). I can load the images but I'm struggling to convert each image's corresponding segmentation