a guest Oct 11th, 2018 56 Never
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- import tensorflow as tf
- import tensorflow_datasets as tfds
- # tfds works with Eager and Graph modes
- # 0. Select the dataset you'd like to use
- # 1. Construct the DatasetBuilder
- # Each dataset is implemented as a DatasetBuilder and can be fetched by
- # string name.
- mnist_builder = tfds.builder(name="mnist", data_dir="~/tfds/data")
- # 2. Download and prepare the dataset into a format ready for a tf.data pipeline
- # 3. Build a tf.data.Dataset from the prepared data
- train_dataset = mnist_builder.as_dataset(split=tfds.Split.TRAIN)
- # 4. Build the rest of your input pipeline using the tf.data API
- train_dataset = train_dataset.repeat().shuffle(1024).batch(32).prefetch(100)
- # If we looked at a single batch, it has a features dictionary with keys
- # "input" and "target"
- features, = train_dataset.take(1)
- images, labels = features["input"], features["target"]
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