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https://github.com/google-deepmind/sonnet

TensorFlow-based neural network library
https://github.com/google-deepmind/sonnet

artificial-intelligence deep-learning machine-learning neural-networks tensorflow

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TensorFlow-based neural network library

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README 

        
![Sonnet](https://sonnet.dev/images/sonnet_logo.png)



# Sonnet



[**Documentation**](https://sonnet.readthedocs.io/) | [**Examples**](#examples)



Sonnet is a library built on top of [TensorFlow 2](https://www.tensorflow.org/)

designed to provide simple, composable abstractions for machine learning

research.



# Introduction



Sonnet has been designed and built by researchers at DeepMind. It can be used to

construct neural networks for many different purposes (un/supervised learning,

reinforcement learning, ...). We find it is a successful abstraction for our

organization, you might too!



More specifically, Sonnet provides a simple but powerful programming model

centered around a single concept: `snt.Module`. Modules can hold references to

parameters, other modules and methods that apply some function on the user

input. Sonnet ships with many predefined modules (e.g. `snt.Linear`,

`snt.Conv2D`, `snt.BatchNorm`) and some predefined networks of modules (e.g.

`snt.nets.MLP`) but users are also encouraged to build their own modules.



Unlike many frameworks Sonnet is extremely unopinionated about **how** you will

use your modules. Modules are designed to be self contained and entirely

decoupled from one another. Sonnet does not ship with a training framework and

users are encouraged to build their own or adopt those built by others.



Sonnet is also designed to be simple to understand, our code is (hopefully!)

clear and focussed. Where we have picked defaults (e.g. defaults for initial

parameter values) we try to point out why.



# Getting Started



## Examples



The easiest way to try Sonnet is to use Google Colab which offers a free Python

notebook attached to a GPU or TPU.



- [Predicting MNIST with an MLP](https://colab.research.google.com/github/deepmind/sonnet/blob/v2/examples/mlp_on_mnist.ipynb)

- [Training a Little GAN on MNIST](https://colab.research.google.com/github/deepmind/sonnet/blob/v2/examples/little_gan_on_mnist.ipynb)

- [Distributed training with `snt.distribute`](https://colab.research.google.com/github/deepmind/sonnet/blob/v2/examples/distributed_cifar10.ipynb)



## Installation



To get started install TensorFlow 2.0 and Sonnet 2:



```shell

$ pip install tensorflow tensorflow-probability

$ pip install dm-sonnet

```



You can run the following to verify things installed correctly:



```python

import tensorflow as tf

import sonnet as snt



print("TensorFlow version {}".format(tf.__version__))

print("Sonnet version {}".format(snt.__version__))

```



### Using existing modules



Sonnet ships with a number of built in modules that you can trivially use. For

example to define an MLP we can use the `snt.Sequential` module to call a

sequence of modules, passing the output of a given module as the input for the

next module. We can use `snt.Linear` and `tf.nn.relu` to actually define our

computation:



```python

mlp = snt.Sequential([

    snt.Linear(1024),

    tf.nn.relu,

    snt.Linear(10),

])

```



To use our module we need to "call" it. The `Sequential` module (and most

modules) define a `__call__` method that means you can call them by name:



```python

logits = mlp(tf.random.normal([batch_size, input_size]))

```



It is also very common to request all the parameters for your module. Most

modules in Sonnet create their parameters the first time they are called with

some input (since in most cases the shape of the parameters is a function of

the input). Sonnet modules provide two properties for accessing parameters.



The `variables` property returns **all** `tf.Variable`s that are referenced by

the given module:



```python

all_variables = mlp.variables

```



It is worth noting that `tf.Variable`s are not just used for parameters of your

model. For example they are used to hold state in metrics used in

`snt.BatchNorm`. In most cases users retrieve the module variables to pass them

to an optimizer to be updated. In this case non-trainable variables should

typically not be in that list as they are updated via a different mechanism.

TensorFlow has a built in mechanism to mark variables as "trainable" (parameters

of your model) vs. non-trainable (other variables). Sonnet provides a mechanism

to gather all trainable variables from your module which is probably what you

want to pass to an optimizer:



```python

model_parameters = mlp.trainable_variables

```



### Building your own module



Sonnet strongly encourages users to subclass `snt.Module` to define their own

modules. Let's start by creating a simple `Linear` layer called `MyLinear`:



```python

class MyLinear(snt.Module):



  def __init__(self, output_size, name=None):

    super(MyLinear, self).__init__(name=name)

    self.output_size = output_size



  @snt.once

  def _initialize(self, x):

    initial_w = tf.random.normal([x.shape[1], self.output_size])

    self.w = tf.Variable(initial_w, name="w")

    self.b = tf.Variable(tf.zeros([self.output_size]), name="b")



  def __call__(self, x):

    self._initialize(x)

    return tf.matmul(x, self.w) + self.b

```



Using this module is trivial:



```python

mod = MyLinear(32)

mod(tf.ones([batch_size, input_size]))

```



By subclassing `snt.Module` you get many nice properties for free. For example

a default implementation of `__repr__` which shows constructor arguments (very

useful for debugging and introspection):



```python

>>> print(repr(mod))

MyLinear(output_size=10)

```



You also get the `variables` and `trainable_variables` properties:



```python

>>> mod.variables

(,

 )

```



You may notice the `my_linear` prefix on the variables above. This is because

Sonnet modules also enter the modules name scope whenever methods are called.

By entering the module name scope we provide a much more useful graph for tools

like TensorBoard to consume (e.g. all operations that occur inside my_linear

will be in a group called my_linear).



Additionally your module will now support TensorFlow checkpointing and saved

model which are advanced features covered later.



# Serialization



Sonnet supports multiple serialization formats. The simplest format we support

is Python's `pickle`, and all built in modules are tested to make sure they can

be saved/loaded via pickle in the same Python process. In general we discourage

the use of pickle, it is not well supported by many parts of TensorFlow and in

our experience can be quite brittle.



## TensorFlow Checkpointing



**Reference:** https://www.tensorflow.org/alpha/guide/checkpoints



TensorFlow checkpointing can be used to save the value of parameters

periodically during training. This can be useful to save the progress of

training in case your program crashes or is stopped. Sonnet is designed to work

cleanly with TensorFlow checkpointing:



```python

checkpoint_root = "/tmp/checkpoints"

checkpoint_name = "example"

save_prefix = os.path.join(checkpoint_root, checkpoint_name)



my_module = create_my_sonnet_module()  # Can be anything extending snt.Module.



# A `Checkpoint` object manages checkpointing of the TensorFlow state associated

# with the objects passed to it's constructor. Note that Checkpoint supports

# restore on create, meaning that the variables of `my_module` do **not** need

# to be created before you restore from a checkpoint (their value will be

# restored when they are created).

checkpoint = tf.train.Checkpoint(module=my_module)



# Most training scripts will want to restore from a checkpoint if one exists. This

# would be the case if you interrupted your training (e.g. to use your GPU for

# something else, or in a cloud environment if your instance is preempted).

latest = tf.train.latest_checkpoint(checkpoint_root)

if latest is not None:

  checkpoint.restore(latest)



for step_num in range(num_steps):

  train(my_module)



  # During training we will occasionally save the values of weights. Note that

  # this is a blocking call and can be slow (typically we are writing to the

  # slowest storage on the machine). If you have a more reliable setup it might be

  # appropriate to save less frequently.

  if step_num and not step_num % 1000:

    checkpoint.save(save_prefix)



# Make sure to save your final values!!

checkpoint.save(save_prefix)

```



## TensorFlow Saved Model



**Reference:** https://www.tensorflow.org/alpha/guide/saved_model



TensorFlow saved models can be used to save a copy of your network that is

decoupled from the Python source for it. This is enabled by saving a TensorFlow

graph describing the computation and a checkpoint containing the value of

weights.



The first thing to do in order to create a saved model is to create a

`snt.Module` that you want to save:



```python

my_module = snt.nets.MLP([1024, 1024, 10])

my_module(tf.ones([1, input_size]))

```



Next, we need to create another module describing the specific parts of our

model that we want to export. We advise doing this (rather than modifying the

original model in-place) so you have fine grained control over what is actually

exported. This is typically important to avoid creating very large saved models,

and such that you only share the parts of your model you want to (e.g. you only

want to share the generator for a GAN but keep the discriminator private).



```python

@tf.function(input_signature=[tf.TensorSpec([None, input_size])])

def inference(x):

  return my_module(x)



to_save = snt.Module()

to_save.inference = inference

to_save.all_variables = list(my_module.variables)

tf.saved_model.save(to_save, "/tmp/example_saved_model")

```



We now have a saved model in the `/tmp/example_saved_model` folder:



```shell

$ ls -lh /tmp/example_saved_model

total 24K

drwxrwsr-t 2 tomhennigan 154432098 4.0K Apr 28 00:14 assets

-rw-rw-r-- 1 tomhennigan 154432098  14K Apr 28 00:15 saved_model.pb

drwxrwsr-t 2 tomhennigan 154432098 4.0K Apr 28 00:15 variables

```



Loading this model is simple and can be done on a different machine without any

of the Python code that built the saved model:



```python

loaded = tf.saved_model.load("/tmp/example_saved_model")



# Use the inference method. Note this doesn't run the Python code from `to_save`

# but instead uses the TensorFlow Graph that is part of the saved model.

loaded.inference(tf.ones([1, input_size]))



# The all_variables property can be used to retrieve the restored variables.

assert len(loaded.all_variables) > 0

```



Note that the loaded object is not a Sonnet module, it is a container object

that has the specific methods (e.g. `inference`) and properties (e.g.

`all_variables`) that we added in the previous block.



## Distributed training



**Example:** https://github.com/deepmind/sonnet/blob/v2/examples/distributed_cifar10.ipynb



Sonnet has support for distributed training using

[custom TensorFlow distribution strategies](https://sonnet.readthedocs.io/en/latest/api.html#module-sonnet.distribute).



A key difference between Sonnet and distributed training using `tf.keras` is

that Sonnet modules and optimizers do not behave differently when run under

distribution strategies (e.g. we do not average your gradients or sync your

batch norm stats). We believe that users should be in full control of these

aspects of their training and they should not be baked into the library. The

trade off here is that you need to implement these features in your training

script (typically this is just 2 lines of code to all reduce your gradients

before applying your optimizer) or swap in modules that are explicitly

distribution aware (e.g. `snt.distribute.CrossReplicaBatchNorm`).



Our [distributed Cifar-10](https://github.com/deepmind/sonnet/blob/v2/examples/distributed_cifar10.ipynb)

example walks through doing multi-GPU training with Sonnet.