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TensorFlow Hub is a repository of pre-trained TensorFlow models.
This tutorial demonstrates how to:
- Use models from TensorFlow Hub with
tf.keras. - Use an image classification model from TensorFlow Hub.
- Do simple transfer learning to fine-tune a model for your own image classes.
Setup
import numpy as np
import time
import PIL.Image as Image
import matplotlib.pylab as plt
import tensorflow as tf
import tensorflow_hub as hub
import datetime
%load_ext tensorboard
2022-12-14 02:32:09.171157: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory 2022-12-14 02:32:09.171253: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory 2022-12-14 02:32:09.171263: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
An ImageNet classifier
You'll start by using a classifier model pre-trained on the ImageNet benchmark dataset—no initial training required!
Download the classifier
Select a MobileNetV2 pre-trained model from TensorFlow Hub and wrap it as a Keras layer with hub.KerasLayer. Any compatible image classifier model from TensorFlow Hub will work here, including the examples provided in the drop-down below.
mobilenet_v2 ="https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/4"
inception_v3 = "https://tfhub.dev/google/imagenet/inception_v3/classification/5"
classifier_model = mobilenet_v2
IMAGE_SHAPE = (224, 224)
classifier = tf.keras.Sequential([
hub.KerasLayer(classifier_model, input_shape=IMAGE_SHAPE+(3,))
])
WARNING:tensorflow:Please fix your imports. Module tensorflow.python.training.tracking.data_structures has been moved to tensorflow.python.trackable.data_structures. The old module will be deleted in version 2.11. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23. Instructions for updating: Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089 WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23. Instructions for updating: Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089
Run it on a single image
Download a single image to try the model on:
grace_hopper = tf.keras.utils.get_file('image.jpg','https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg')
grace_hopper = Image.open(grace_hopper).resize(IMAGE_SHAPE)
grace_hopper
Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/grace_hopper.jpg 61306/61306 [==============================] - 0s 0us/step
grace_hopper = np.array(grace_hopper)/255.0
grace_hopper.shape
(224, 224, 3)
Add a batch dimension (with np.newaxis) and pass the image to the model:
result = classifier.predict(grace_hopper[np.newaxis, ...])
result.shape
1/1 [==============================] - 2s 2s/step (1, 1001)
The result is a 1001-element vector of logits, rating the probability of each class for the image.
The top class ID can be found with tf.math.argmax:
predicted_class = tf.math.argmax(result[0], axis=-1)
predicted_class
<tf.Tensor: shape=(), dtype=int64, numpy=653>
Decode the predictions
Take the predicted_class ID (such as 653) and fetch the ImageNet dataset labels to decode the predictions:
labels_path = tf.keras.utils.get_file('ImageNetLabels.txt','https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt')
imagenet_labels = np.array(open(labels_path).read().splitlines())
Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt 10484/10484 [==============================] - 0s 0us/step
plt.imshow(grace_hopper)
plt.axis('off')
predicted_class_name = imagenet_labels[predicted_class]
_ = plt.title("Prediction: " + predicted_class_name.title())
Simple transfer learning
But what if you want to create a custom classifier using your own dataset that has classes that aren't included in the original ImageNet dataset (that the pre-trained model was trained on)?
To do that, you can:
- Select a pre-trained model from TensorFlow Hub; and
- Retrain the top (last) layer to recognize the classes from your custom dataset.
Dataset
In this example, you will use the TensorFlow flowers dataset:
data_root = tf.keras.utils.get_file(
'flower_photos',
'https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz',
untar=True)
First, load this data into the model using the image data off disk with tf.keras.utils.image_dataset_from_directory, which will generate a tf.data.Dataset:
batch_size = 32
img_height = 224
img_width = 224
train_ds = tf.keras.utils.image_dataset_from_directory(
str(data_root),
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size
)
val_ds = tf.keras.utils.image_dataset_from_directory(
str(data_root),
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 2936 files for training. Found 3670 files belonging to 5 classes. Using 734 files for validation.
The flowers dataset has five classes:
class_names = np.array(train_ds.class_names)
print(class_names)
['daisy' 'dandelion' 'roses' 'sunflowers' 'tulips']
Second, because TensorFlow Hub's convention for image models is to expect float inputs in the [0, 1] range, use the tf.keras.layers.Rescaling preprocessing layer to achieve this.
normalization_layer = tf.keras.layers.Rescaling(1./255)
train_ds = train_ds.map(lambda x, y: (normalization_layer(x), y)) # Where x—images, y—labels.
val_ds = val_ds.map(lambda x, y: (normalization_layer(x), y)) # Where x—images, y—labels.
Third, finish the input pipeline by using buffered prefetching with Dataset.prefetch, so you can yield the data from disk without I/O blocking issues.
These are some of the most important tf.data methods you should use when loading data. Interested readers can learn more about them, as well as how to cache data to disk and other techniques, in 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)
for image_batch, labels_batch in train_ds:
print(image_batch.shape)
print(labels_batch.shape)
break
(32, 224, 224, 3) (32,) 2022-12-14 02:32:18.340489: W tensorflow/core/kernels/data/cache_dataset_ops.cc:856] 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.
Run the classifier on a batch of images
Now, run the classifier on an image batch:
result_batch = classifier.predict(train_ds)
92/92 [==============================] - 3s 21ms/step
predicted_class_names = imagenet_labels[tf.math.argmax(result_batch, axis=-1)]
predicted_class_names
array(['daisy', 'coral fungus', 'rapeseed', ..., 'daisy', 'daisy',
'birdhouse'], dtype='<U30')
Check how these predictions line up with the images:
plt.figure(figsize=(10,9))
plt.subplots_adjust(hspace=0.5)
for n in range(30):
plt.subplot(6,5,n+1)
plt.imshow(image_batch[n])
plt.title(predicted_class_names[n])
plt.axis('off')
_ = plt.suptitle("ImageNet predictions")
The results are far from perfect, but reasonable considering that these are not the classes the model was trained for (except for "daisy").
Download the headless model
TensorFlow Hub also distributes models without the top classification layer. These can be used to easily perform transfer learning.
Select a MobileNetV2 pre-trained model from TensorFlow Hub. Any compatible image feature vector model from TensorFlow Hub will work here, including the examples from the drop-down menu.
mobilenet_v2 = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
inception_v3 = "https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4"
feature_extractor_model = mobilenet_v2
Create the feature extractor by wrapping the pre-trained model as a Keras layer with hub.KerasLayer. Use the trainable=False argument to freeze the variables, so that the training only modifies the new classifier layer:
feature_extractor_layer = hub.KerasLayer(
feature_extractor_model,
input_shape=(224, 224, 3),
trainable=False)
The feature extractor returns a 1280-long vector for each image (the image batch size remains at 32 in this example):
feature_batch = feature_extractor_layer(image_batch)
print(feature_batch.shape)
(32, 1280)
Attach a classification head
To complete the model, wrap the feature extractor layer in a tf.keras.Sequential model and add a fully-connected layer for classification:
num_classes = len(class_names)
model = tf.keras.Sequential([
feature_extractor_layer,
tf.keras.layers.Dense(num_classes)
])
model.summary()
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
keras_layer_1 (KerasLayer) (None, 1280) 2257984
dense (Dense) (None, 5) 6405
=================================================================
Total params: 2,264,389
Trainable params: 6,405
Non-trainable params: 2,257,984
_________________________________________________________________
predictions = model(image_batch)
predictions.shape
TensorShape([32, 5])
Train the model
Use Model.compile to configure the training process and add a tf.keras.callbacks.TensorBoard callback to create and store logs:
model.compile(
optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['acc'])
log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(
log_dir=log_dir,
histogram_freq=1) # Enable histogram computation for every epoch.
Now use the Model.fit method to train the model.
To keep this example short, you'll be training for just 10 epochs. To visualize the training progress in TensorBoard later, create and store logs an a TensorBoard callback.
NUM_EPOCHS = 10
history = model.fit(train_ds,
validation_data=val_ds,
epochs=NUM_EPOCHS,
callbacks=tensorboard_callback)
Epoch 1/10 92/92 [==============================] - 9s 58ms/step - loss: 0.8000 - acc: 0.7037 - val_loss: 0.4569 - val_acc: 0.8501 Epoch 2/10 92/92 [==============================] - 4s 49ms/step - loss: 0.3769 - acc: 0.8740 - val_loss: 0.3615 - val_acc: 0.8733 Epoch 3/10 92/92 [==============================] - 4s 49ms/step - loss: 0.2954 - acc: 0.9091 - val_loss: 0.3252 - val_acc: 0.8965 Epoch 4/10 92/92 [==============================] - 4s 49ms/step - loss: 0.2459 - acc: 0.9271 - val_loss: 0.3075 - val_acc: 0.9005 Epoch 5/10 92/92 [==============================] - 4s 49ms/step - loss: 0.2104 - acc: 0.9401 - val_loss: 0.2978 - val_acc: 0.9074 Epoch 6/10 92/92 [==============================] - 4s 49ms/step - loss: 0.1832 - acc: 0.9499 - val_loss: 0.2921 - val_acc: 0.9114 Epoch 7/10 92/92 [==============================] - 4s 49ms/step - loss: 0.1616 - acc: 0.9561 - val_loss: 0.2885 - val_acc: 0.9155 Epoch 8/10 92/92 [==============================] - 4s 49ms/step - loss: 0.1439 - acc: 0.9612 - val_loss: 0.2861 - val_acc: 0.9169 Epoch 9/10 92/92 [==============================] - 4s 49ms/step - loss: 0.1290 - acc: 0.9680 - val_loss: 0.2845 - val_acc: 0.9223 Epoch 10/10 92/92 [==============================] - 5s 49ms/step - loss: 0.1162 - acc: 0.9731 - val_loss: 0.2835 - val_acc: 0.9196
Start the TensorBoard to view how the metrics change with each epoch and to track other scalar values:
%tensorboard --logdir logs/fit
Check the predictions
Obtain the ordered list of class names from the model predictions:
predicted_batch = model.predict(image_batch)
predicted_id = tf.math.argmax(predicted_batch, axis=-1)
predicted_label_batch = class_names[predicted_id]
print(predicted_label_batch)
1/1 [==============================] - 0s 430ms/step ['roses' 'dandelion' 'tulips' 'sunflowers' 'dandelion' 'roses' 'dandelion' 'roses' 'tulips' 'dandelion' 'tulips' 'tulips' 'sunflowers' 'tulips' 'dandelion' 'roses' 'daisy' 'tulips' 'dandelion' 'dandelion' 'dandelion' 'tulips' 'sunflowers' 'roses' 'sunflowers' 'dandelion' 'tulips' 'roses' 'roses' 'sunflowers' 'tulips' 'sunflowers']
Plot the model predictions:
plt.figure(figsize=(10,9))
plt.subplots_adjust(hspace=0.5)
for n in range(30):
plt.subplot(6,5,n+1)
plt.imshow(image_batch[n])
plt.title(predicted_label_batch[n].title())
plt.axis('off')
_ = plt.suptitle("Model predictions")
Export and reload your model
Now that you've trained the model, export it as a SavedModel for reusing it later.
t = time.time()
export_path = "/tmp/saved_models/{}".format(int(t))
model.save(export_path)
export_path
INFO:tensorflow:Assets written to: /tmp/saved_models/1670985195/assets INFO:tensorflow:Assets written to: /tmp/saved_models/1670985195/assets '/tmp/saved_models/1670985195'
Confirm that you can reload the SavedModel and that the model is able to output the same results:
reloaded = tf.keras.models.load_model(export_path)
result_batch = model.predict(image_batch)
reloaded_result_batch = reloaded.predict(image_batch)
1/1 [==============================] - 0s 45ms/step 1/1 [==============================] - 1s 531ms/step
abs(reloaded_result_batch - result_batch).max()
0.0
reloaded_predicted_id = tf.math.argmax(reloaded_result_batch, axis=-1)
reloaded_predicted_label_batch = class_names[reloaded_predicted_id]
print(reloaded_predicted_label_batch)
['roses' 'dandelion' 'tulips' 'sunflowers' 'dandelion' 'roses' 'dandelion' 'roses' 'tulips' 'dandelion' 'tulips' 'tulips' 'sunflowers' 'tulips' 'dandelion' 'roses' 'daisy' 'tulips' 'dandelion' 'dandelion' 'dandelion' 'tulips' 'sunflowers' 'roses' 'sunflowers' 'dandelion' 'tulips' 'roses' 'roses' 'sunflowers' 'tulips' 'sunflowers']
plt.figure(figsize=(10,9))
plt.subplots_adjust(hspace=0.5)
for n in range(30):
plt.subplot(6,5,n+1)
plt.imshow(image_batch[n])
plt.title(reloaded_predicted_label_batch[n].title())
plt.axis('off')
_ = plt.suptitle("Model predictions")
Next steps
You can use the SavedModel to load for inference or convert it to a TensorFlow Lite model (for on-device machine learning) or a TensorFlow.js model (for machine learning in JavaScript).
Discover more tutorials to learn how to use pre-trained models from TensorFlow Hub on image, text, audio, and video tasks.