ここまでの経過：
<![CDATA[$ source \[venv のパス\]/venv/bin/activate
(venv) $ python
>>> import tensorflow as tf
>>> tf.enable_eager_execution()
>>> TRAIN_DATA_URL = \
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz'
>>> data_root_orig = \
tf.keras.utils.get_file(origin=TRAIN_DATA_URL, fname='flower_photos', untar=True)
>>> import pathlib
>>> data_root = pathlib.Path(data_root_orig)
>>> all_image_paths = list(data_root.glob('*/*'))
>>> all_image_paths = [str(path) for path in all_image_paths]
>>> label_names = \
... sorted(item.name for item in data_root.glob('*/') if item.is_dir())
>>> label_to_index = \
... dict((name, index) for index,name in enumerate(label_names))
>>> all_image_labels = \
... [label_to_index[pathlib.Path(path).parent.name] for path in all_image_paths]
>>> def raw_to_tensor(path):
... image_raw = tf.io.read_file(path)
... return tf.image.decode_image(image_raw, channels=3)
...
>>> def load_and_preprocess_image(path):
... img_tensor = raw_to_tensor(path)
... img_final = tf.image.resize(img_tensor, [192, 192])
... img_final /= 255.0
... return image
...
>>> path_ds = \
... tf.data.Dataset.from_tensor_slices(all_image_paths)
>>> label_ds = \
... tf.data.Dataset.from_tensor_slices(tf.cast(all_image_labels, tf.int64))
>>> AUTOTUNE = tf.data.experimental.AUTOTUNE
>>> image_ds = \
... path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE)
>>> image_label_ds \
... = tf.data.Dataset.zip((image_ds, label_ds))]]>
○ shuffle
<![CDATA[>>> image_count = len(all_image_paths)
>>> ds = image_label_ds.shuffle(buffer_size=image_count)]]>
○ batch
<![CDATA[>>> BATCH_SIZE = 2
>>> ds = ds.batch(BATCH_SIZE)
>>>> ds
<DatasetV1Adapter shapes: ((?, 3, 3, 3), (?,)), types: (tf.float32, tf.string)>]]>
○ buffer
<![CDATA[>>>> ds = ds.prefetch(buffer_size=AUTOTUNE)
>>>> ds
]]>
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