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!pip install tf-nightly
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import numpy as np
import os
import PIL
import PIL.Image
import tensorflow as tf
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print(tf.__version__)
Note: all images are licensed CC-BY, creators are listed in the LICENSE.txt file.
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import pathlib
dataset_url = "https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz"
data_dir = tf.keras.utils.get_file(origin=dataset_url,
fname='flower_photos',
untar=True)
data_dir = pathlib.Path(data_dir)
After downloading (218MB), you should now have a copy of the flower photos available. There are 3670 total images:
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image_count = len(list(data_dir.glob('*/*.jpg')))
print(image_count)
Each directory contains images of that type of flower. Here are some roses:
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roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[0]))
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roses = list(data_dir.glob('roses/*'))
PIL.Image.open(str(roses[1]))
Let's load these images off disk using image_dataset_from_directory.
Note: The Keras Preprocesing utilities and layers introduced in this section are currently experimental and may change.
Define some parameters for the loader:
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batch_size = 32
img_height = 180
img_width = 180
It's good practice to use a validation split when developing your model. We will use 80% of the images for training, and 20% for validation.
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train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
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val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
You can find the class names in the class_names attribute on these datasets.
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class_names = train_ds.class_names
print(class_names)
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import matplotlib.pyplot as plt
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")
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:
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for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
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 referes to color channels RGB). The label_batch is a tensor of the shape (32,), these are corresponding labels to the 32 images.
Note: you can call .numpy() on either of these tensors to convert them to a numpy.ndarray.
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, we will standardize values to be in the [0, 1] by using a Rescaling layer.
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from tensorflow.keras import layers
normalization_layer = tf.keras.layers.experimental.preprocessing.Rescaling(1./255)
There are two ways to use this layer. You can apply it to the dataset by calling map:
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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 pixels values are now in `[0,1]`.
print(np.min(first_image), np.max(first_image))
Or, you can include the layer inside your model definition to simplify deployment. We will use the second approach here.
Note: If you would like to scale pixel values to [-1,1] you can instead write Rescaling(1./127.5, offset=-1)
Note: we previously resized images using the image_size argument of image_dataset_from_directory. If you want to include the resizing logic in your model, you can use the Resizing layer instead.
Let's make sure to use buffered prefetching so we can yield data from disk without having I/O become blocking. These are two important methods you should use when loading data.
.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.
.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 data performance guide.
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AUTOTUNE = tf.data.experimental.AUTOTUNE
train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
For completeness, we will show how to train a simple model using the datasets we just prepared. 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 this tutorial.
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num_classes = 5
model = tf.keras.Sequential([
layers.experimental.preprocessing.Rescaling(1./255),
layers.Conv2D(32, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Conv2D(32, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Flatten(),
layers.Dense(128, activation='relu'),
layers.Dense(num_classes)
])
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model.compile(
optimizer='adam',
loss=tf.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
Note: we will only train for a few epochs so this tutorial runs quickly.
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model.fit(
train_ds,
batch_size=batch_size,
validation_data=val_ds,
epochs=3
)
Note: you can also write a custom training loop instead of using model.fit. To learn more, visit this tutorial.
You may notice the validation accuracy is low to the compared to the training accuracy, indicating our model is overfitting. You can learn more about overfitting and how to reduce it in this tutorial.
The above keras.preprocessing utilities are a convenient way to create a tf.data.Dataset from a directory of imgages. 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 zip we downloaded earlier.
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list_ds = tf.data.Dataset.list_files(str(data_dir/'*/*'), shuffle=False)
list_ds = list_ds.shuffle(image_count, reshuffle_each_iteration=False)
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for f in list_ds.take(5):
print(f.numpy())
The tree structure of the files can be used to compile a class_names list.
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class_names = np.array(sorted([item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"]))
print(class_names)
Split the dataset into train and validation:
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val_size = int(image_count * 0.2)
train_ds = list_ds.skip(val_size)
val_ds = list_ds.take(val_size)
You can see the length of each dataset as follows:
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print(tf.data.experimental.cardinality(train_ds).numpy())
print(tf.data.experimental.cardinality(val_ds).numpy())
Write a short function that converts a file path to an (img, label) pair:
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def get_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)
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def decode_img(img):
# convert the compressed string to a 3D uint8 tensor
img = tf.image.decode_jpeg(img, channels=3)
# resize the image to the desired size
return tf.image.resize(img, [img_height, img_width])
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def process_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:
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# 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)
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for image, label in train_ds.take(1):
print("Image shape: ", image.numpy().shape)
print("Label: ", label.numpy())
To train a model with this dataset you will want the data:
These features can be added using the tf.data API. For more details, see the Input Pipeline Performance guide.
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def configure_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)
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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")
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model.fit(
train_ds,
validation_data=val_ds,
epochs=3
)
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. As a next step, you can learn how to add data augmentation by visiting this tutorial. To learn more about tf.data, you can visit this guide.