Load and preprocess images

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This tutorial shows how to load and preprocess an image dataset in three ways:

Setup

importnumpyasnp
importos
importPIL
importPIL.Image
importtensorflowastf
importtensorflow_datasetsastfds

2024年08月16日 07:35:28.601200: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:485] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024年08月16日 07:35:28.622447: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:8454] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024年08月16日 07:35:28.628875: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1452] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered

print(tf.__version__)

2.17.0

Download the flowers dataset

This tutorial uses a dataset of several thousand photos of flowers. The flowers dataset contains five sub-directories, one per class:

flowers_photos/
 daisy/
 dandelion/
 roses/
 sunflowers/
 tulips/

importpathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
archive = tf.keras.utils.get_file(origin=dataset_url, extract=True)
data_dir = pathlib.Path(archive).with_suffix('')

Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
228813984/228813984 ━━━━━━━━━━━━━━━━━━━━ 2s 0us/step

After downloading (218MB), you should now have a copy of the flower photos available. There are 3,670 total images:

image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)

3670

Each directory contains images of that type of flower. Here are some roses:

roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))

png

roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[1]))

png

Load data using a Keras utility

Let's load these images off disk using the helpful tf.keras.utils.image_dataset_from_directory utility.

Create a dataset

Define some parameters for the loader:

batch_size = 32
img_height = 180
img_width = 180

It's good practice to use a validation split when developing your model. You will use 80% of the images for training and 20% for validation.

train_ds = tf.keras.utils.image_dataset_from_directory(
 data_dir,
 validation_split=0.2,
 subset="training",
 seed=123,
 image_size=(img_height, img_width),
 batch_size=batch_size)

Found 3670 files belonging to 5 classes.
Using 2936 files for training.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1723793736.323935 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.327688 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.331273 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.334862 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.346577 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.350078 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.353446 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.356758 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.360253 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.363838 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.367268 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793736.370622 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.615909 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.618086 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.620163 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.622223 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.624312 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.626332 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.628286 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.630249 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.632230 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.634261 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.636226 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.638171 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.677165 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.679297 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.681295 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.683291 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.685306 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.687341 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.689297 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.691269 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.693297 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.695724 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.698128 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
I0000 00:00:1723793737.700565 228334 cuda_executor.cc:1015] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355

val_ds = tf.keras.utils.image_dataset_from_directory(
 data_dir,
 validation_split=0.2,
 subset="validation",
 seed=123,
 image_size=(img_height, img_width),
 batch_size=batch_size)

Found 3670 files belonging to 5 classes.
Using 734 files for validation.

You can find the class names in the class_names attribute on these datasets.

class_names = train_ds.class_names
print(class_names)

['daisy', 'dandelion', 'roses', 'sunflowers', 'tulips']

Visualize the data

Here are the first nine images from the training dataset.

importmatplotlib.pyplotasplt
plt.figure(figsize=(10, 10))
for images, labels in train_ds.take(1):
 for i in range(9):
 ax = plt.subplot(3, 3, i + 1)
 plt.imshow(images[i].numpy().astype("uint8"))
 plt.title(class_names[labels[i]])
 plt.axis("off")

png

You can train a model using these datasets by passing them to model.fit (shown later in this tutorial). If you like, you can also manually iterate over the dataset and retrieve batches of images:

for image_batch, labels_batch in train_ds:
 print(image_batch.shape)
 print(labels_batch.shape)
 break

(32, 180, 180, 3)
(32,)

The image_batch is a tensor of the shape (32, 180, 180, 3). This is a batch of 32 images of shape 180x180x3 (the last dimension refers to color channels RGB). The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images.

You can call .numpy() on either of these tensors to convert them to a numpy.ndarray.

Standardize the data

The RGB channel values are in the [0, 255] range. This is not ideal for a neural network; in general you should seek to make your input values small.

Here, you will standardize values to be in the [0, 1] range by using tf.keras.layers.Rescaling:

normalization_layer = tf.keras.layers.Rescaling(1./255)

There are two ways to use this layer. You can apply it to the dataset by calling Dataset.map:

normalized_ds = train_ds.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(normalized_ds))
first_image = image_batch[0]
# Notice the pixel values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image))

0.0 0.96902645

Or, you can include the layer inside your model definition to simplify deployment. You will use the second approach here.

Configure the dataset for performance

Let's make sure to use buffered prefetching so you can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data:

  • Dataset.cache keeps the images in memory after they're loaded off disk during the first epoch. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache.
  • Dataset.prefetch overlaps data preprocessing and model execution while training.

Interested readers can learn more about both methods, as well as how to cache data to disk in the Prefetching section of the Better performance with the tf.data API guide.

AUTOTUNE = tf.data.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)

Train a model

For completeness, you will show how to train a simple model using the datasets you have just prepared.

The Sequential model consists of three convolution blocks (tf.keras.layers.Conv2D) with a max pooling layer (tf.keras.layers.MaxPooling2D) in each of them. There's a fully-connected layer (tf.keras.layers.Dense) with 128 units on top of it that is activated by a ReLU activation function ('relu'). This model has not been tuned in any way—the goal is to show you the mechanics using the datasets you just created. To learn more about image classification, visit the Image classification tutorial.

num_classes = 5
model = tf.keras.Sequential([
 tf.keras.layers.Rescaling(1./255),
 tf.keras.layers.Conv2D(32, 3, activation='relu'),
 tf.keras.layers.MaxPooling2D(),
 tf.keras.layers.Conv2D(32, 3, activation='relu'),
 tf.keras.layers.MaxPooling2D(),
 tf.keras.layers.Conv2D(32, 3, activation='relu'),
 tf.keras.layers.MaxPooling2D(),
 tf.keras.layers.Flatten(),
 tf.keras.layers.Dense(128, activation='relu'),
 tf.keras.layers.Dense(num_classes)
])

Choose the tf.keras.optimizers.Adam optimizer and tf.keras.losses.SparseCategoricalCrossentropy loss function. To view training and validation accuracy for each training epoch, pass the metrics argument to Model.compile.

model.compile(
 optimizer='adam',
 loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
 metrics=['accuracy'])

model.fit(
 train_ds,
 validation_data=val_ds,
 epochs=3
)

Epoch 1/3
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1723793742.229752 228538 service.cc:146] XLA service 0x7f8424005180 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
I0000 00:00:1723793742.229788 228538 service.cc:154] StreamExecutor device (0): Tesla T4, Compute Capability 7.5
I0000 00:00:1723793742.229793 228538 service.cc:154] StreamExecutor device (1): Tesla T4, Compute Capability 7.5
I0000 00:00:1723793742.229796 228538 service.cc:154] StreamExecutor device (2): Tesla T4, Compute Capability 7.5
I0000 00:00:1723793742.229799 228538 service.cc:154] StreamExecutor device (3): Tesla T4, Compute Capability 7.5
E0000 00:00:1723793743.747934 228538 gpu_timer.cc:183] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.
E0000 00:00:1723793743.889619 228538 gpu_timer.cc:183] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.
10/92 ━━━━━━━━━━━━━━━━━━━━ 1s 19ms/step - accuracy: 0.2397 - loss: 1.6561
I0000 00:00:1723793745.306928 228538 device_compiler.h:188] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
91/92 ━━━━━━━━━━━━━━━━━━━━ 0s 31ms/step - accuracy: 0.3728 - loss: 1.4181
E0000 00:00:1723793748.938167 228536 gpu_timer.cc:183] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.
E0000 00:00:1723793749.078774 228536 gpu_timer.cc:183] Delay kernel timed out: measured time has sub-optimal accuracy. There may be a missing warmup execution, please investigate in Nsight Systems.
92/92 ━━━━━━━━━━━━━━━━━━━━ 12s 79ms/step - accuracy: 0.3744 - loss: 1.4152 - val_accuracy: 0.5463 - val_loss: 1.1106
Epoch 2/3
92/92 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.5915 - loss: 1.0141 - val_accuracy: 0.5763 - val_loss: 1.0378
Epoch 3/3
92/92 ━━━━━━━━━━━━━━━━━━━━ 2s 20ms/step - accuracy: 0.6581 - loss: 0.8870 - val_accuracy: 0.6090 - val_loss: 0.9559
<keras.src.callbacks.history.History at 0x7f8584228c40>

You may notice the validation accuracy is low compared to the training accuracy, indicating your model is overfitting. You can learn more about overfitting and how to reduce it in this tutorial.

Using tf.data for finer control

The above Keras preprocessing utility—tf.keras.utils.image_dataset_from_directory—is a convenient way to create a tf.data.Dataset from a directory of images.

For finer grain control, you can write your own input pipeline using tf.data. This section shows how to do just that, beginning with the file paths from the TGZ file you downloaded earlier.

list_ds = tf.data.Dataset.list_files(str(data_dir/'*/*'), shuffle=False)
list_ds = list_ds.shuffle(image_count, reshuffle_each_iteration=False)

for f in list_ds.take(5):
 print(f.numpy())

b'/home/kbuilder/.keras/datasets/flower_photos/dandelion/16237158409_01913cf918_n.jpg'
b'/home/kbuilder/.keras/datasets/flower_photos/dandelion/4654848357_9549351e0b_n.jpg'
b'/home/kbuilder/.keras/datasets/flower_photos/dandelion/19443726008_8c9c68efa7_m.jpg'
b'/home/kbuilder/.keras/datasets/flower_photos/tulips/16751015081_af2ef77c9a_n.jpg'
b'/home/kbuilder/.keras/datasets/flower_photos/dandelion/2330343016_23acc484ee.jpg'

The tree structure of the files can be used to compile a class_names list.

class_names = np.array(sorted([item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"]))
print(class_names)

['daisy' 'dandelion' 'roses' 'sunflowers' 'tulips']

Split the dataset into training and validation sets:

val_size = int(image_count * 0.2)
train_ds = list_ds.skip(val_size)
val_ds = list_ds.take(val_size)

You can print the length of each dataset as follows:

print(tf.data.experimental.cardinality(train_ds).numpy())
print(tf.data.experimental.cardinality(val_ds).numpy())

2936
734

Write a short function that converts a file path to an (img, label) pair:

defget_label(file_path):
 # Convert the path to a list of path components
 parts = tf.strings.split(file_path, os.path.sep)
 # The second to last is the class-directory
 one_hot = parts[-2] == class_names
 # Integer encode the label
 return tf.argmax(one_hot)

defdecode_img(img):
 # Convert the compressed string to a 3D uint8 tensor
 img = tf.io.decode_jpeg(img, channels=3)
 # Resize the image to the desired size
 return tf.image.resize(img, [img_height, img_width])

defprocess_path(file_path):
 label = get_label(file_path)
 # Load the raw data from the file as a string
 img = tf.io.read_file(file_path)
 img = decode_img(img)
 return img, label

Use Dataset.map to create a dataset of image, label pairs:

# Set `num_parallel_calls` so multiple images are loaded/processed in parallel.
train_ds = train_ds.map(process_path, num_parallel_calls=AUTOTUNE)
val_ds = val_ds.map(process_path, num_parallel_calls=AUTOTUNE)

for image, label in train_ds.take(1):
 print("Image shape: ", image.numpy().shape)
 print("Label: ", label.numpy())

Image shape: (180, 180, 3)
Label: 4

Configure dataset for performance

To train a model with this dataset you will want the data:

  • To be well shuffled.
  • To be batched.
  • Batches to be available as soon as possible.

These features can be added using the tf.data API. For more details, visit the Input Pipeline Performance guide.

defconfigure_for_performance(ds):
 ds = ds.cache()
 ds = ds.shuffle(buffer_size=1000)
 ds = ds.batch(batch_size)
 ds = ds.prefetch(buffer_size=AUTOTUNE)
 return ds
train_ds = configure_for_performance(train_ds)
val_ds = configure_for_performance(val_ds)

Visualize the data

You can visualize this dataset similarly to the one you created previously:

image_batch, label_batch = next(iter(train_ds))
plt.figure(figsize=(10, 10))
for i in range(9):
 ax = plt.subplot(3, 3, i + 1)
 plt.imshow(image_batch[i].numpy().astype("uint8"))
 label = label_batch[i]
 plt.title(class_names[label])
 plt.axis("off")

2024年08月16日 07:35:56.791290: W tensorflow/core/kernels/data/cache_dataset_ops.cc:913] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

png

Continue training the model

You have now manually built a similar tf.data.Dataset to the one created by tf.keras.utils.image_dataset_from_directory above. You can continue training the model with it. As before, you will train for just a few epochs to keep the running time short.

model.fit(
 train_ds,
 validation_data=val_ds,
 epochs=3
)

Epoch 1/3
92/92 ━━━━━━━━━━━━━━━━━━━━ 6s 48ms/step - accuracy: 0.7026 - loss: 0.7613 - val_accuracy: 0.7057 - val_loss: 0.7631
Epoch 2/3
92/92 ━━━━━━━━━━━━━━━━━━━━ 2s 24ms/step - accuracy: 0.7659 - loss: 0.6259 - val_accuracy: 0.7139 - val_loss: 0.7349
Epoch 3/3
92/92 ━━━━━━━━━━━━━━━━━━━━ 2s 23ms/step - accuracy: 0.8536 - loss: 0.4182 - val_accuracy: 0.7330 - val_loss: 0.7181
<keras.src.callbacks.history.History at 0x7f85641dbf40>

Using TensorFlow Datasets

So far, this tutorial has focused on loading data off disk. You can also find a dataset to use by exploring the large catalog of easy-to-download datasets at TensorFlow Datasets.

As you have previously loaded the Flowers dataset off disk, let's now import it with TensorFlow Datasets.

Download the Flowers dataset using TensorFlow Datasets:

(train_ds, val_ds, test_ds), metadata = tfds.load(
 'tf_flowers',
 split=['train[:80%]', 'train[80%:90%]', 'train[90%:]'],
 with_info=True,
 as_supervised=True,
)

The flowers dataset has five classes:

num_classes = metadata.features['label'].num_classes
print(num_classes)

5

Retrieve an image from the dataset:

get_label_name = metadata.features['label'].int2str
image, label = next(iter(train_ds))
_ = plt.imshow(image)
_ = plt.title(get_label_name(label))

2024年08月16日 07:36:09.400520: W tensorflow/core/kernels/data/cache_dataset_ops.cc:913] The calling iterator did not fully read the dataset being cached. In order to avoid unexpected truncation of the dataset, the partially cached contents of the dataset will be discarded. This can happen if you have an input pipeline similar to `dataset.cache().take(k).repeat()`. You should use `dataset.take(k).cache().repeat()` instead.

png

As before, remember to batch, shuffle, and configure the training, validation, and test sets for performance:

train_ds = configure_for_performance(train_ds)
val_ds = configure_for_performance(val_ds)
test_ds = configure_for_performance(test_ds)

You can find a complete example of working with the Flowers dataset and TensorFlow Datasets by visiting the Data augmentation tutorial.

Next steps

This tutorial showed two ways of loading images off disk. First, you learned how to load and preprocess an image dataset using Keras preprocessing layers and utilities. Next, you learned how to write an input pipeline from scratch using tf.data. Finally, you learned how to download a dataset from TensorFlow Datasets.

For your next steps:

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Last updated 2024年08月16日 UTC.