Using a TensorFlow Lite model in your mobile app requires multiple considerations: you must choose a pre-trained or custom model, convert the model to a TensorFLow Lite format, and finally, integrate the model in your app.
1. Choose a model
Depending on the use case, you can choose one of the popular open-sourced models, such as InceptionV3 or MobileNets, and re-train these models with a custom data set or even build your own custom model.
Use a pre-trained model
MobileNets is a family of mobile-first computer vision models for TensorFlow designed to effectively maximize accuracy, while taking into consideration the restricted resources for on-device or embedded applications. MobileNets are small, low-latency, low-power models parameterized to meet the resource constraints for a variety of uses. They can be used for classification, detection, embeddings, and segmentation—similar to other popular large scale models, such as Inception. Google provides 16 pre-trained ImageNet classification checkpoints for MobileNets that can be used in mobile projects of all sizes.
Inception-v3 is an image recognition model that achieves fairly high accuracy recognizing general objects with 1000 classes, for example, "Zebra", "Dalmatian", and "Dishwasher". The model extracts general features from input images using a convolutional neural network and classifies them based on those features with fully-connected and softmax layers.
On Device Smart Reply is an on-device model that provides one-touch replies for incoming text messages by suggesting contextually relevant messages. The model is built specifically for memory constrained devices, such as watches and phones, and has been successfully used in Smart Replies on Android Wear. Currently, this model is Android-specific.
These pre-trained models are available for download
Re-train Inception-V3 or MobileNet for a custom data set
These pre-trained models were trained on the ImageNet data set which contains 1000 predefined classes. If these classes are not sufficient for your use case, the model will need to be re-trained. This technique is called transfer learning and starts with a model that has been already trained on a problem, then retrains the model on a similar problem. Deep learning from scratch can take days, but transfer learning is fairly quick. In order to do this, you need to generate a custom data set labeled with the relevant classes.
The TensorFlow for Poets codelab walks through the re-training process step-by-step. The code supports both floating point and quantized inference.
Train a custom model
A developer may choose to train a custom model using Tensorflow (see the
Tutorials for examples of building and training models). If you have already
written a model, the first step is to export this to a
tf.GraphDef file. This
is required because some formats do not store the model structure outside the
code, and we must communicate with other parts of the framework. See
Exporting the Inference Graph
to create .pb file for the custom model.
TensorFlow Lite currently supports a subset of TensorFlow operators. Refer to the TensorFlow Lite & TensorFlow Compatibility Guide for supported operators and their usage. This set of operators will continue to grow in future Tensorflow Lite releases.
2. Convert the model format
The model generated (or downloaded) in the previous step is a standard
Tensorflow model and you should now have a .pb or .pbtxt
Models generated with transfer learning (re-training) or custom models must be
converted—but, we must first freeze the graph to convert the model to the
Tensorflow Lite format. This process uses several model formats:
tf.GraphDef(.pb) —A protobuf that represents the TensorFlow training or computation graph. It contains operators, tensors, and variables definitions.
- CheckPoint (.ckpt) —Serialized variables from a TensorFlow graph. Since this does not contain a graph structure, it cannot be interpreted by itself.
FrozenGraphDef—A subclass of
GraphDefthat does not contain variables. A
GraphDefcan be converted to a
FrozenGraphDefby taking a CheckPoint and a
GraphDef, and converting each variable into a constant using the value retrieved from the CheckPoint.
GraphDefand CheckPoint with a signature that labels input and output arguments to a model. A
GraphDefand CheckPoint can be extracted from a
- TensorFlow Lite model (.tflite) —A serialized
FlatBuffer that contains TensorFlow
Lite operators and tensors for the TensorFlow Lite interpreter, similar to a
To use the
GraphDef .pb file with TensorFlow Lite, you must have checkpoints
that contain trained weight parameters. The .pb file only contains the structure
of the graph. The process of merging the checkpoint values with the graph
structure is called freezing the graph.
You should have a checkpoints folder or download them for a pre-trained model (for example, MobileNets).
To freeze the graph, use the following command (changing the arguments):
freeze_graph --input_graph=/tmp/mobilenet_v1_224.pb \ --input_checkpoint=/tmp/checkpoints/mobilenet-10202.ckpt \ --input_binary=true \ --output_graph=/tmp/frozen_mobilenet_v1_224.pb \ --output_node_names=MobileNetV1/Predictions/Reshape_1
input_binary flag must be enabled so the protobuf is read and written in
a binary format. Set the
output_node_names may not be obvious outside of the code that built the
model. The easiest way to find them is to visualize the graph, either with
GraphDef is now ready for conversion to the
(.tflite) for use on Android or iOS devices. For Android, the Tensorflow
Optimizing Converter tool supports both float and quantized models. To convert
GraphDef to the .tflite format:
toco --input_file=$(pwd)/mobilenet_v1_1.0_224/frozen_graph.pb \ --input_format=TENSORFLOW_GRAPHDEF \ --output_format=TFLITE \ --output_file=/tmp/mobilenet_v1_1.0_224.tflite \ --inference_type=FLOAT \ --input_type=FLOAT \ --input_arrays=input \ --output_arrays=MobilenetV1/Predictions/Reshape_1 \ --input_shapes=1,224,224,3
input_file argument should reference the frozen
containing the model architecture. The frozen_graph.pb
file used here is available for download.
output_file is where the TensorFlow
Lite model will get generated. The
arguments should be set to
FLOAT, unless converting a
quantized model. Setting the
input_shape arguments are not as straightforward. The
easiest way to find these values is to explore the graph using Tensorboard. Reuse
the arguments for specifying the output nodes for inference in the
It is also possible to use the Tensorflow Optimizing Converter with protobufs from either Python or from the command line (see the toco_from_protos.py example). This allows you to integrate the conversion step into the model design workflow, ensuring the model is easily convertible to a mobile inference graph. For example:
import tensorflow as tf img = tf.placeholder(name="img", dtype=tf.float32, shape=(1, 64, 64, 3)) val = img + tf.constant([1., 2., 3.]) + tf.constant([1., 4., 4.]) out = tf.identity(val, name="out") with tf.Session() as sess: tflite_model = tf.contrib.lite.toco_convert(sess.graph_def, [img], [out]) open("converteds_model.tflite", "wb").write(tflite_model)
For usage, see the Tensorflow Optimizing Converter command-line examples.
bazel run tensorflow/contrib/lite/tools:visualize -- model.tflite model_viz.html
This generates an interactive HTML page listing subgraphs, operations, and a graph visualization.
3. Use the TensorFlow Lite model for inference in a mobile app
After completing the prior steps, you should now have a
.tflite model file.
Since Android apps are written in Java and the core TensorFlow library is in C++, a JNI library is provided as an interface. This is only meant for inference—it provides the ability to load a graph, set up inputs, and run the model to calculate outputs.
The Building TensorFlow on Android guide has instructions for installing TensorFlow on
Android and setting up
bazel and Android Studio.
Core ML support
Core ML is a machine learning framework used in Apple products. In addition to using Tensorflow Lite models directly in your applications, you can convert trained Tensorflow models to the CoreML format for use on Apple devices. To use the converter, refer to the Tensorflow-CoreML converter documentation.
Compile Tensorflow Lite for a Raspberry Pi by following the
RPi build instructions
This compiles a static library file (
.a) used to build your app. There are
plans for Python bindings and a demo app.