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Hub with Keras

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TensorFlow Hub is a way to share pretrained model components. See the TensorFlow Module Hub for a searchable listing of pre-trained models.

This tutorial demonstrates:

  1. How to use TensorFlow Hub with tf.keras.
  2. How to do image classification using TensorFlow Hub.
  3. How to do simple transfer learning.

Setup

Imports

from __future__ import absolute_import, division, print_function, unicode_literals

import matplotlib.pylab as plt

!pip install -q tf-nightly-2.0-preview
import tensorflow as tf
!pip install -q -U https://storage.googleapis.com/download.tensorflow.org/data/tensorflow_hub-0.4.0.dev0-py2.py3-none-any.whl
import tensorflow_hub as hub

from tensorflow.keras import layers
WARNING: Logging before flag parsing goes to stderr.
W0418 23:13:35.157299 140467448600320 deprecation_wrapper.py:76] From /usr/local/lib/python3.5/dist-packages/tensorflow_hub/estimator.py:63: The name estimator.Exporter is deprecated. Please use estimator.Exporter instead.

W0418 23:13:35.167857 140467448600320 deprecation_wrapper.py:76] From /usr/local/lib/python3.5/dist-packages/tensorflow_hub/keras_layer.py:38: The name keras.layers.Layer is deprecated. Please use keras.layers.Layer instead.

An ImageNet classifier

Download the classifier

Use hub.module to load a mobilenet, and tf.keras.layers.Lambda to wrap it up as a keras layer.

Any tf2 compatible image classifier url from tfhub.dev will work here.

classifier_url ="https://tfhub.dev/google/tf2-preview/mobilenet_v2/classification/2" #@param {type:"string"}
IMAGE_SHAPE = (224, 224)

classifier = tf.keras.Sequential([
    hub.KerasLayer(classifier_url, input_shape=IMAGE_SHAPE+(3,))
])
W0418 23:13:35.195206 140467448600320 deprecation_wrapper.py:76] From /usr/local/lib/python3.5/dist-packages/tensorflow_hub/tf_utils.py:56: The name compat.as_bytes is deprecated. Please use compat.as_bytes instead.

W0418 23:13:37.539894 140467448600320 deprecation_wrapper.py:76] From /usr/local/lib/python3.5/dist-packages/tensorflow_hub/resolver.py:421: The name errors.NotFoundError is deprecated. Please use errors.NotFoundError instead.

W0418 23:13:40.685408 140467448600320 deprecation_wrapper.py:76] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/autograph/converters/directives.py:116: The name keras.backend.learning_phase is deprecated. Please use keras.backend.learning_phase instead.

Run it on a single image

Download a single image to try the model on.

import numpy as np
import PIL.Image as Image

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
65536/61306 [================================] - 0s 0us/step

png

grace_hopper = np.array(grace_hopper)/255.0
grace_hopper.shape
(224, 224, 3)

Add a batch dimension, and pass the image to the model.

result = classifier.predict(grace_hopper[np.newaxis, ...])
result.shape
(1, 1001)

The result is a 1001 element vector of logits, rating the probability of each class for the image.

So the top class ID can be found with argmax:

predicted_class = np.argmax(result[0], axis=-1)
predicted_class
653

Decode the predictions

We have the predicted class ID, Fetch the ImageNet labels, and 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
16384/10484 [==============================================] - 0s 0us/step
plt.imshow(grace_hopper)
plt.axis('off')
predicted_class_name = imagenet_labels[predicted_class]
_ = plt.title("Prediction: " + predicted_class_name.title())

png

Simple transfer learning

Using TF Hub it is simple to retrain the top layer of the model to recognize the classes in our dataset.

Dataset

For 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)
Downloading data from https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz
228818944/228813984 [==============================] - 4s 0us/step

The simplest way to load this data into our model is using tf.keras.preprocessing.image.ImageDataGenerator,

All of TensorFlow Hub's image modules expect float inputs in the [0, 1] range. Use the ImageDataGenerator's rescale parameter to achieve this.

The image size will be handled later.

image_generator = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1/255)
image_data = image_generator.flow_from_directory(str(data_root), target_size=IMAGE_SHAPE)
Found 3670 images belonging to 5 classes.

The resulting object is an iterator that returns image_batch, label_batch pairs.

for image_batch, label_batch in image_data:
  print("Image batch shape: ", image_batch.shape)
  print("Labe batch shape: ", label_batch.shape)
  break
Image batch shape:  (32, 224, 224, 3)
Labe batch shape:  (32, 5)

Run the classifier on a batch of images

Now run the classifier on the image batch.

result_batch = classifier.predict(image_batch)
result_batch.shape
(32, 1001)
predicted_class_names = imagenet_labels[np.argmax(result_batch, axis=-1)]
predicted_class_names
array(['daisy', 'sea urchin', 'ant', 'hamper', 'daisy', 'ringlet',
       'daisy', 'daisy', 'daisy', 'cardoon', 'lycaenid', 'sleeping bag',
       'Bedlington terrier', 'daisy', 'daisy', 'picket fence',
       'coral fungus', 'daisy', 'zucchini', 'daisy', 'daisy', 'bee',
       'daisy', 'daisy', 'bee', 'daisy', 'picket fence', 'bell pepper',
       'daisy', 'pot', 'wolf spider', 'greenhouse'], dtype='<U30')

Now 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")

png

See the LICENSE.txt file for image attributions.

The results are far from perfect, but reasonable considering that these are not the classes the model was trained for (except "daisy").

Download the headless model

TensorFlow Hub also distributes models without the top classification layer. These can be used to easily do transfer learning.

Any image feature vector url from tfhub.dev will work here.

feature_extractor_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/2" #@param {type:"string"}

Create the feature extractor.

feature_extractor_layer = hub.KerasLayer(feature_extractor_url,
                                         input_shape=(224,224,3))

It returns a 1280-length vector for each image:

feature_batch = feature_extractor_layer(image_batch)
print(feature_batch.shape)
(32, 1280)

Freeze the variables in the feature extractor layer, so that the training only modifies the new classifier layer.

feature_extractor_layer.trainable = False

Attach a classification head

Now wrap the hub layer in a tf.keras.Sequential model, and add a new classification layer.

model = tf.keras.Sequential([
  feature_extractor_layer,
  layers.Dense(image_data.num_classes, activation='softmax')
])

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 compile to configure the training process:

model.compile(
  optimizer=tf.keras.optimizers.Adam(),
  loss='categorical_crossentropy',
  metrics=['acc'])

Now use the .fit method to train the model.

To keep this example short train just 2 epochs. To visualize the training progress, use a custom callback to log the loss and accuracy of each batch individually, instead of the epoch average.

class CollectBatchStats(tf.keras.callbacks.Callback):
  def __init__(self):
    self.batch_losses = []
    self.batch_acc = []

  def on_train_batch_end(self, batch, logs=None):
    self.batch_losses.append(logs['loss'])
    self.batch_acc.append(logs['acc'])
    self.model.reset_metrics()
steps_per_epoch = np.ceil(image_data.samples/image_data.batch_size)

batch_stats_callback = CollectBatchStats()

history = model.fit(image_data, epochs=2,
                    steps_per_epoch=steps_per_epoch,
                    callbacks = [batch_stats_callback])
Epoch 1/2
115/115 [==============================] - 62s 540ms/step - loss: 0.8808 - acc: 0.9062
Epoch 2/2
115/115 [==============================] - 59s 510ms/step - loss: 0.5046 - acc: 0.9062

Now after, even just a few training iterations, we can already see that the model is making progress on the task.

plt.figure()
plt.ylabel("Loss")
plt.xlabel("Training Steps")
plt.ylim([0,2])
plt.plot(batch_stats_callback.batch_losses)
[<matplotlib.lines.Line2D at 0x7fbfe17b7eb8>]

png

plt.figure()
plt.ylabel("Accuracy")
plt.xlabel("Training Steps")
plt.ylim([0,1])
plt.plot(batch_stats_callback.batch_acc)
[<matplotlib.lines.Line2D at 0x7fbfe0b7f748>]

png

Check the predictions

To redo the plot from before, first get the ordered list of class names:

class_names = sorted(image_data.class_indices.items(), key=lambda pair:pair[1])
class_names = np.array([key.title() for key, value in class_names])
class_names
array(['Daisy', 'Dandelion', 'Roses', 'Sunflowers', 'Tulips'],
      dtype='<U10')

Run the image batch through the model and comvert the indices to class names.

predicted_batch = model.predict(image_batch)
predicted_id = np.argmax(predicted_batch, axis=-1)
predicted_label_batch = class_names[predicted_id]

Plot the result

label_id = np.argmax(label_batch, axis=-1)
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])
  color = "green" if predicted_id[n] == label_id[n] else "red"
  plt.title(predicted_label_batch[n].title(), color=color)
  plt.axis('off')
_ = plt.suptitle("Model predictions (green: correct, red: incorrect)")

png

Export your model

Now that you've trained the model, export it as a saved model:

import time
t = time.time()

export_path = "/tmp/saved_models/{}".format(int(t))
tf.keras.experimental.export_saved_model(model, export_path)

export_path
W0418 23:16:06.195625 140467448600320 deprecation.py:506] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/saved_model/load.py:264: calling Zeros.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0418 23:16:09.411318 140467448600320 deprecation.py:506] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/init_ops.py:1257: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.
Instructions for updating:
Call initializer instance with the dtype argument instead of passing it to the constructor
W0418 23:16:14.253237 140467448600320 deprecation.py:323] From /usr/local/lib/python3.5/dist-packages/tensorflow/python/saved_model/signature_def_utils_impl.py:253: build_tensor_info (from tensorflow.python.saved_model.utils_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.utils.build_tensor_info or tf.compat.v1.saved_model.build_tensor_info.
W0418 23:16:14.255191 140467448600320 tf_logging.py:161] Export includes no default signature!
W0418 23:16:15.383613 140467448600320 meta_graph.py:447] Issue encountered when serializing variables.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'list' object has no attribute 'name'
W0418 23:16:15.384918 140467448600320 meta_graph.py:447] Issue encountered when serializing trainable_variables.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'list' object has no attribute 'name'
W0418 23:16:23.887842 140467448600320 tf_logging.py:161] Export includes no default signature!
W0418 23:16:24.675942 140467448600320 meta_graph.py:447] Issue encountered when serializing variables.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'list' object has no attribute 'name'
W0418 23:16:24.677327 140467448600320 meta_graph.py:447] Issue encountered when serializing trainable_variables.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'list' object has no attribute 'name'
W0418 23:16:33.620975 140467448600320 meta_graph.py:447] Issue encountered when serializing variables.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'list' object has no attribute 'name'
W0418 23:16:33.622552 140467448600320 meta_graph.py:447] Issue encountered when serializing trainable_variables.
Type is unsupported, or the types of the items don't match field type in CollectionDef. Note this is a warning and probably safe to ignore.
'list' object has no attribute 'name'

'/tmp/saved_models/1555629364'

Now confirm that we can reload it, and it still gives the same results:

reloaded = tf.keras.experimental.load_from_saved_model(export_path, custom_objects={'KerasLayer':hub.KerasLayer})
result_batch = model.predict(image_batch)
reloaded_result_batch = reloaded.predict(image_batch)
abs(reloaded_result_batch - result_batch).max()
0.0

This saved model can loaded for inference later, or converted to TFLite or TFjs.