Sonnet is a library built on top of TensorFlow for building complex neural networks.

Usage Example

The following code constructs a Linear module and connects it to multiple inputs. The variables (i.e., the weights and biases of the linear transformation) are automatically shared.

import sonnet as snt

train_data = get_training_data()
test_data = get_test_data()

# Construct the module, providing any configuration necessary.
linear_regression_module = snt.Linear(output_size=FLAGS.output_size)

# Connect the module to some inputs, any number of times.
train_predictions = linear_regression_module(train_data)
test_predictions = linear_regression_module(test_data)

More usage examples:

  • Generative models
  • RNNs

General Principles

The main principle of Sonnet is to first construct Python objects which represent some part of a neural network, and then separately connect these objects into the TensorFlow computation graph. The objects are subclasses of sonnet.AbstractModule and as such are referred to as Modules.

Modules may be connected into the graph multiple times, and any variables declared in that module will be automatically shared on subsequent connection calls. Low level aspects of TensorFlow which control variable sharing, including specifying variable scope names, and using the reuse= flag, are abstracted away from the user.

Separating configuration and connection allows easy construction of higher-order Modules, i.e., modules that wrap other modules. For instance, the BatchApply module merges a number of leading dimensions of a tensor into a single dimension, connects a provided module, and then splits the leading dimension of the result to match the input. At construction time, the inner module is passed in as an argument to the BatchApply constructor. At run time, the module first performs a reshape operation on inputs, then applies the module passed into the constructor, and then inverts the reshape operation.

An additional advantage of representing Modules by Python objects is that it allows additional methods to be defined where necessary. An example of this is a module which, after construction, may be connected in a variety of ways while maintaining weight sharing. For instance, in the case of a generative model, we may want to sample from the model, or calculate the log probability of a given observation. Having both connections simultaneously requires weight sharing, and so these methods depend on the same variables. The variables are conceptually owned by the object, and are used by different methods of the module.

Importing Sonnet

The recommended way to import Sonnet is to alias it to a variable named snt:

import sonnet as snt

Every module is then accessible under the namespace snt, and the rest of this document will use snt for brevity.

The following code constructs a module that is composed of other modules:

import sonnet as snt

# Our data is coming in via multiple inputs, so to apply the same model to each
# we will need to use variable sharing.
train_data = get_training_data()
test_data = get_test_data()

# Make two linear modules, to form a Multi Layer Perceptron. Override the
# default names (which would end up being 'linear', 'linear_1') to provide
# interpretable variable names in TensorBoard / other tools.
lin_to_hidden = snt.Linear(output_size=FLAGS.hidden_size, name='inp_to_hidden')
hidden_to_out = snt.Linear(output_size=FLAGS.output_size, name='hidden_to_out')

# Sequential is a module which applies a number of inner modules or ops in
# sequence to the provided data. Note that raw TF ops such as tanh can be
# used interchangeably with constructed modules, as they contain no variables.
mlp = snt.Sequential([lin_to_hidden, tf.sigmoid, hidden_to_out])

# Connect the sequential into the graph, any number of times.
train_predictions = mlp(train_data)
test_predictions = mlp(test_data)

The following code adds initializers and regularizers to a Linear module:

import sonnet as snt

train_data = get_training_data()
test_data = get_test_data()

# Initializers and regularizers for the weights and the biasses.
initializers={"w": tf.truncated_normal_initializer(stddev=1.0),
              "b": tf.truncated_normal_initializer(stddev=1.0)}
regularizers = {"w": tf.contrib.layers.l1_regularizer(scale=0.1),
                "b": tf.contrib.layers.l2_regularizer(scale=0.1)}

linear_regression_module = snt.Linear(output_size=FLAGS.output_size,

# Connect the module to some inputs, any number of times.
train_predictions = linear_regression_module(train_data)
test_predictions = linear_regression_module(test_data)

# ...

# Get the regularization losses and add them together.
graph_regularizers = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_regularization_loss = tf.reduce_sum(graph_regularizers)

# ...

# When minimizing the loss, minimize also the regularization loss.
train_op = optimizer.minimize(loss + total_regularizer_loss)

Defining your own modules

Inherit from snt.AbstractModule

To define a module, create a new class which inherits from snt.AbstractModule. The constructor of your class should accept any configuration which defines the operation of that module, and store it in a member variable prefixed with an underscore, to indicate that it is private.

Call superclass constructor

The first thing the constructor does should be to call the superclass constructor, passing in the name for the module - if you forget to do this, the variable sharing will break. A name kwarg should always be provided as the final one of the list, with the default value being a snake_case version of the class name.

class MyMLP(snt.AbstractModule):
  """Docstring for MyMLP."""
  def __init__(self, hidden_size, output_size,
               nonlinearity=tf.tanh, name="my_mlp"):
    """Docstring explaining __init__ args, including types and defaults."""
    super(MyMLP, self).__init__(name=name)
    self._hidden_size = hidden_size
    self._output_size = output_size
    self._nonlinearity = nonlinearity

Implement _build() method

The only other method implementation which must be provided is _build(). This will be called whenever the module is connected into the tf.Graph. It receives some input, which may be empty, a single Tensor, or some arbitrary structure containing multiple Tensors. Multiple Tensors can be provided with either a tuple or namedtuple, the elements of which in turn can be Tensors or further tuples / namedtuples. Most input Tensors require a batch dimension, and if a Tensor has a color channel then it must be the last dimension. While in many cases the library will not explicitly prevent you, the use of lists and dicts is not supported, as the mutability of these structures can lead to subtle bugs.

# Following on from code snippet above..
def _build(self, inputs):
  """Compute output Tensor from input Tensor."""
  lin_x_to_h = snt.Linear(output_size=self._hidden_size, name="x_to_h")
  lin_h_to_o = snt.Linear(output_size=self._output_size, name="h_to_o")
  return lin_h_to_o(self._nonlinearity(lin_x_to_h(inputs)))

The _build method may include any or all of the following processes:

  • Construct and use internal modules
  • Use modules which already exist, and were passed into the constructor
  • Create variables directly.

If you create variables yourself, it is crucial to create them with tf.get_variable. Calling the tf.Variable constructor directly will only work the first time the module is connected, but on the second call you will receive an error message “Trainable variable created when calling a template after the first time”.

The modules in the above example are created separately, passing in various configurations, and then the final line connects them all into the graph. The return line should be read from right to left - the inputs Tensor is passed into the first Linear, lin_x_to_h, the output of which is passed into whatever nonlinearity was stored in the constructor, the output of which goes through another Linear to produce the result. Note that we give short meaningful names to the internal Linear instances.

Note that the nonlinearity above can be either a raw TF op, eg tf.tanh or tf.sigmoid, or an instance of a Sonnet module. In keeping with Python standards, we may choose to not check this explicitly, and so we may receive an error when _build is called. It is also acceptable to add constraints and sanity checking inside __init__.

Note that in the above code, new instances of snt.Linear are generated each time _build() is called, and you may think this will create different, unshared variables. This is not the case - only 4 variables (2 for each Linear) will be created, no matter how many times the MLP instance is connected into the graph. How this is works is a low level TF detail, and subject to change - see tf.variable_op_scope for details.

Where should the submodules be declared?

Note that modules may use other modules which they receive already externally constructed - eg Sequential etc. The submodules we discuss in this section are any Modules which are constructed inside the code of another Module, which we will refer to as the Parent Module. An example is an LSTM, where most implementations will internally construct one or more Linear modules to contain the weights.

It’s recommended that submodules are created in _build(). Doing it this way means you get the correct nesting of variable scopes, eg:

class ParentModule(snt.AbstractModule):
  def __init__(self, hidden_size, name="parent_module"):
    super(ParentModule, self).__init__(name=name)
    self._hidden_size = hidden_size

  def _build(self, inputs):
    lin_mod = snt.Linear(self._hidden_size)  # Construct submodule...
    return tf.relu(lin_mod(inputs))          # then connect it.

The variables created by the Linear will have a name something like parent_module/linear/w, which is what you probably want in this kind of situation.

Some users prefer for practical or stylistic reasons to construct everything in the constructor, before anything is used. This is fine, but for proper variable nesting any submodules must be constructed inside a ``self._enter_variable_scope`` call.

class OtherParentModule(snt.AbstractModule):
  def __init__(self, hidden_size, name="other_parent_module"):
    super(OtherParentModule, self).__init__(name=name)
    self._hidden_size = hidden_size
    with self._enter_variable_scope():  # This line is crucial!
      self._lin_mod = snt.Linear(self._hidden_size)  # Construct submodule here.

  def _build(self, inputs):
    return tf.relu(self._lin_mod(inputs))  # Connect previously constructed mod.

The above example is fine, and will have the same variable names etc. Different people prefer different styles and both of the above are considered correct.

The pitfall here is forgetting to call self._enter_variable_scope(). Things will still “work” but the scopes will not be nested as you might expected:

class WrongModule(snt.AbstractModule):
  def __init__(self, hidden_size, name="wrong_module"):
    super(WrongModule, self).__init__(name=name)
    self._hidden_size = hidden_size
    self._lin_mod = snt.Linear(self._hidden_size)  # Construct submodule here.

  def _build(self, inputs):
    return tf.relu(self._lin_mod(inputs))  # Connect previously constructed mod.

The above example works okay in terms of the resulting network’s calculations, but is considered a bug due to the resulting flat (instead of hierarchical) variable namespace. The variables in the linear will be called "linear/w" which is completely disjoint from the "wrong_module" namespace.

Recurrent Modules


Sonnet includes recurrent core modules (also called “cells” in TensorFlow terminology), which perform one time step of computation. These are ready to be unrolled in time using TensorFlow’s unrolling operations.

One example of an LSTM that is unrolled in time is the following:

hidden_size = 5
batch_size = 20
# input_sequence should be a tensor of size
# [time_steps, batch_size, input_features]
input_sequence = ...
lstm = snt.LSTM(hidden_size)
initial_state = lstm.initial_state(batch_size)
output_sequence, final_state = tf.nn.dynamic_rnn(
    lstm, input_sequence, initial_state=initial_state, time_major=True)

The batch_size parameter passed to the initial_state() method can also be an int32 Tensor.

For a more comprehensive demonstration on the usage of recurrent modules, a fully-documented example of a deep LSTM with skip connections trained on the Shakespeare dataset is available.

Defining your own recurrent modules

A recurrent module is any subclass of snt.RNNCore, which inherits from snt.AbstractModule and has an interface compatible with tf.nn.rnn_cell.RNNCell. This allows us to use the variable sharing model from Sonnet whilst also using the cores inside TensorFlow’s RNN Containers.

class Add1RNN(snt.RNNCore):
  """Simple core that adds 1 to its state and produces zero outputs.

  This core computes the following:

  (`input`, (`state1`, `state2`)) -> (`output`, (`next_state1`, `next_state2`))

  where all the elements are tensors, next_statei` = `statei` + 1, and
  `output` = 0. All the outputs (`state` and `output`) are of size
  (`batch_size`, `hidden_size`), where `hidden_size` is a size that is
  specified in the constructor.

  def __init__(self, hidden_size, name="add1_rnn"):
    """Constructor of the module.

      hidden_size: an int, size of the outputs of the module (without batch
      name: the name of the module.
    super(Add1RNN, self).__init__(name=name)
    self._hidden_size = hidden_size

  def _build(self, inputs, state):
    """Builds a TF subgraph that performs one timestep of computation."""
    batch_size = tf.TensorShape([inputs.get_shape()[0]])
    outputs = tf.zeros(shape=batch_size.concatenate(self.output_size))
    state1, state2 = state
    next_state = (state1 + 1, state2 + 1)
    return outputs, next_state

  def state_size(self):
    """Returns a description of the state size, without batch dimension."""
    return (tf.TensorShape([self._hidden_size]),

  def output_size(self):
    """Returns a description of the output size, without batch dimension."""
    return tf.TensorShape([self._hidden_size])

  def initial_state(self, batch_size, dtype):
    """Returns an initial state with zeros, for a batch size and data type.

    NOTE: This method is here only for illustrative purposes, the corresponding
    method in its superclass should be already doing this.
    sz1, sz2 = self.state_size
    # Prepend batch size to the state shape, and create zeros.
    return (tf.zeros([batch_size] + sz1.as_list(), dtype=dtype),
            tf.zeros([batch_size] + sz2.as_list(), dtype=dtype))

Apart from the _build method from snt.AbstractModule, a recurrent module must also implement the state_size and output_size properties, which provide the expected size of the recurrent state, and an example of it, respectively. snt.RNNCore defines a initial_state method that can be used to generate a zero initial state or a trainable initial state (based on the aforementioned properties). Optionally, any recurrent module can define its own initial_state method. Note that a zero_state method is also available (as in tf.nn.rnn_cell.RNNCell) to produce a correctly sized state value filled with zeros. In some situations (LSTM, etc) it may be acceptable to begin with a state containing all zeros, but in other situations this is too limiting, and we may want to (eg) fill some part of the state with random noise.

A common option is to make the initial state of an RNN trainable, meaning the state is produced from some tf.Variables which are trained via backpropagation. If a core supports this, it should provide kwargs trainable= and name= for initial_state(). The name= kwarg can be used to provide a prefix for the (potentially multiple) variable name(s) which will be created.

The Transposable interface

Sonnet defines an interface for modules supporting transposition, called snt.Transposable. Transposition is a flexible concept (e.g. not necessarily related to matrix transposition as defined in algebra), and in this context it entails the definition of a new module with attributes which are somehow related to the original module, without strictly implying any form of variable sharing. For example, given a snt.Linear which maps from input size A to output size B, via transposition we will return another snt.Linear module whose weight matrix shape is the transpose of the original one, thus mapping from input size B to output size A; given a snt.Conv2D module we will return a matching snt.Conv2DTranspose module.

The snt.Transposable interface requires that transposable modules implement a method called transpose, returning a module which is the transposed version of the one the method is called on. Whilst not enforced by Sonnet, chaining the method twice should be expected to return a module with the same specifications as the original module.

When implementing a transposable module, special care is required to ensure that parameters needed to instantiate the module are provided as functions whose evaluation is deferred to graph construction time. This mechanism allows for transposed modules to be instantiated before the original module is connected to the graph. An example of this behavior can be found in snt.Linear, where the output_size argument of the transposed module is defined as a lambda returning the input_shape property of the original module; upon evaluation input_shape will raise an error unless the module has not been connected to the graph, but this is not an issue since the lambda is not called until the transposed module is connected to the graph.

Variable reuse with @snt.reuse_variables (experimental)

Some use cases require a tf.VariableScope to be shared across multiple methods, which isn’t possible with snt.AbstractModule. For example, a generative model may define a sample() and log_pdf() method that share parts of the same tf.Graph.

Adding the @snt.reuse_variables decorator to a method will enable variable reuse in much the same manner as _build(). The most notable difference is that a single tf.VariableScope will be used across different decorated methods and each decorated method has its own reuse flag that is used to enter the variable scope.

class Reusable(object):

  def __init__(self, name):
    with tf.variable_scope(name) as vs:
      self.variable_scope = vs

  def reusable_var(self):
    return tf.get_variable("a", shape=[1])

obj = Reusable("reusable")
a1 = obj.reusable_var()
a2 = obj.reusable_var()
# a1 == a2

class NaiveAutoEncoder(snt.AbstractModule):
  def __init__(self, n_latent, n_out, name="naive_auto_encoder"):
    super(NaiveAutoEncoder, self).__init__(name=name)
    self._n_latent = n_latent
    self._n_out = n_out

  def encode(self, input):
    """Builds the front half of AutoEncoder, inputs -> latents."""
    w_enc = tf.get_variable("w_enc", shape=[self._n_out, self._n_latent])
    b_enc = tf.get_variable("b_enc", shape=[self._n_latent])
    return tf.sigmoid(tf.matmul(input, w_enc) + b_enc)

  def decode(self, latents):
    """Builds the back half of AutoEncoder, latents -> reconstruction."""
    w_rec = tf.get_variable("w_dec", shape=[self._n_latent, self._n_out])
    b_rec = tf.get_variable("b_dec", shape=[self._n_out])
    return tf.sigmoid(tf.matmul(latents, w_rec) + b_rec)

  def _build(self, input):
    """Builds the 'full' AutoEncoder, ie input -> latents -> reconstruction."""
    latents = self.encode(input)
    return self.decode(latents)

batch_size = 5
n_in = 10
n_out = n_in
n_latent = 2
nae = NaiveAutoEncoder(n_latent=n_latent, n_out=n_out)
inputs = tf.placeholder(tf.float32, shape=[batch_size, n_in])
latents = tf.placeholder(tf.float32, shape=[batch_size, n_latent])

# Connecting the default way calls build(), producing 'full' AutoEncoder.
reconstructed_from_input = nae(inputs)

# Connecting with one of the other methods might only require some subset of the
# variables, but sharing will still work.
reconstructed_from_latent = nae.decode(latents)

In the above example, any variables created by nae.encode(), nae.decode() or nae() exist in the same tf.VariableScope. In addition, since each decorated method has its own reuse flag we don’t need to worry about having to create all variables on the first method call, or about the calling order at all. We can even nest (decorated) methods within other (decorated) methods - the reuse flag is always set correctly since the variable scope is re-entered for every method call.

However every decorated method must be the sole owner of its variables. For example, if we use tf.get_variable("w_dec", ...) inside NaiveAutoEncoder.encode, this will not do variable sharing. Instead, TensorFlow will treat tf.get_variable("w_dec", ...) in NaiveAutoEncoder.enodec and NaiveAutoEncoder.decode() as separate variables and an error will occur in eitherobj.a() or (whichever is called second).

See below for an example of bad variable reuse:

class BadReusable(object):

  def __init__(self, name):
    with tf.variable_scope(name) as vs:
      self.variable_scope = vs

  def reusable_var(self):
    return tf.get_variable("a", shape=[1])

  def another_reusable_var(self):
    return tf.get_variable("a", shape=[1])

obj = BadReusable("bad_reusable")
obj.another_reusable_var()  # Raises a ValueError because `reuse=False`

Wrapping functions into Sonnet modules using snt.Module

Whilst the recommended way of defining new Sonnet modules is to inherit from snt.AbstractModule, the library also offers an alternative route to succinctly instantiate modules wrapping user-provided functions.

The snt.Module class constructor takes a callable and returns a Sonnet module. The provided function is invoked when the module is called, thus specifying how new nodes are added to the computational graph and how to compute output Tensors from input Tensors. Please refer to the module documentation for more details and examples.


Q: Why another TF library?

A: The existing libraries were judged insufficiently flexible for the DeepMind use case where extensive use is made of weight sharing. Making everything use tf.make_template, and therefore support weight sharing from the start, seemed to have sufficient benefits to outweight the development cost. The paradigm of separating configuration from connection also allows easy composability of modules.

Q: Can I access different variables on subsequent calls to the same build()?

A: No. This is enforced by tf.make_template, which considers it an error to access different / extra variables on subsequent calls.

Q: What if I mistakenly give two modules the same name?

A: Modules which appear to be constructed with the same name will have distinct names, and variable scopes. Under the hood, Sonnet uses tf.make_template which essentially wraps a python function together with some tf.VariableScope, ensuring that every call to the function happens in the same scope, and that all calls after the first are set to reuse variables. One feature of the templating is that it will uniquify any provided names, if they have already been entered in the same scope. For example:

lin_1 = snt.Linear(output_size=42, name="linear")
lin_2 = snt.Linear(output_size=84, name="linear")  # this name is already taken.

print(  # prints "linear"
print(  # prints "linear_1" - automatically uniquified.

Note that the .name property is available to see the “post-uniquification” name.

Q: Do I have to name my modules?

A: No. Modules have a default name, which should be the class name in snake_case, and that will be used as the name with uniquification (see above) if necessary. However, we recommend providing a name for modules which contain variables, as the name provided becomes the name of the internal scope, and thus defines the variable names. Most modules are written to declare internal weights with names like "w" and "b" for weights and bias - it’s vastly preferable to do have a list of weights like:


rather than:


The names you choose will appear in TensorBoard.

Q: How do I find out what variables are declared by a module?

A: You can query a module to find out all the variables in its scope using the get_variables() method. Note that this will throw an error if the module is not connected into the graph, as the variables do not exist at that point so the relevant scope will be empty.

Q: Should I be putting calls to variable_scope in my code?

A: Every module implicitly creates an internal variable_scope, which it re-enters each time it connects to the graph. Assuming all the variables in your model are inside Sonnet modules, it is not necessary to use scopes yourself.

Q: Can I mix this with raw TF ops?

A: Yes. An op which doesn’t declare variables internally, and so is effectively a pure function, can be used inside module _build implementations, and also to plumb together values between modules.

Q: Should everything in Sonnet be implemented as a Module?

No, computations which do not create tf.Variables and do not store internal configurations can be implemented in the regular TF Op style, ie a python function that receives input tensors, keyword arguments, and returns tensor outputs.

If an op is going to create variables (ie call tf.get_variable anywhere internally, including indirectly) it must be implemented as a subclass of snt.AbstractModule so that variable sharing is correctly handled.

Note that if a computation doesn’t create any Variables, it may still be desirable to implement it with a Module instead of an Op.

Aside from variable sharing, it may be convenient to use Sonnet Modules in cases where we wish to attach configuration parameters to an op. An example of this is the content addressing modules in the Differentiable Neural Computer. These modules receive a number of configuration parameters (size of each word in memory, number of read heads) and some function of these inputs defines what the valid input size is. We use a snt.Linear of the correct output size before this module, in order to provide the correct dimensionality. As a module this is easy - provide the configuration at construction time, then a method .param_size() which gives the required input dimensionality. We can then make the correct size of input tensor and perform the connection.

class CosineWeights(snt.AbstractModule):
  def __init__(self, word_size, num_heads, name="cosine_weights"):
    super(CosineWeights, self).__init__(name=name)
    self._word_size = word_size
    self._num_heads = num_heads
  def param_size(self):
    """Returns the size the 2nd dimension of `cos_params` is required to be."""
    return self._num_heads * (1 + self._word_size)
  def _build(self, memory, cos_params):
    """cos_params must be `[batch_size, self.param_size()]` shape"""
    # ...

# Construct the module, then work out the right input size
cos_weights_mod = CosineWeights(word_size=32, num_heads=3)
cosine_params_mod = snt.Linear(output_size=cos_weights_mod.param_size())

cos_params = cosine_params_mod(inputs)  # We know this is now the right shape.
weights = cos_weights_mode(memory, cos_params)

If the above was implemented as an op cosine_weights(memory, cos_params, word_size, num_heads) then the logic to indicate the desired size of cos_params would have to be stored in a separate function. Encapsulating the related functions into one module results in cleaner code.

Another example of where this flexibility is useful is when an Op has a large number of arguments which are conceptually configuration, along with some which are conceptually inputs. We often want to use the same configuration in multiple places, for different inputs, and so writing a Module which can be constructed with the configuration and then passed around may be useful.

import functools
# 1. Define our computation as some op
def useful_op(input_a, input_b,
              use_clipping=True, remove_nans=False, solve_agi='maybe'):
  # ...

# 2a). Set the configuration parameters with functools, then pass around.
useful_op_configured = functools.partial(
do_something_a(... , inner_op=useful_op_configured)
do_something_else_a(..., inner_op=useful_op_configured)

# 2b). OR, set the configuration by creating kwargs and pass around both.
op_kwargs = {
    'use_clipping': False,
    'remove_nans': True,
    'solve_agi': 'definitely',
do_something_b(..., inner_op=useful_op, inner_op_kwargs=op_kwargs)
do_something_else_b(..., inner_op=useful_op, inner_op_kwargs=op_kwargs)

Either of the above approaches is valid, but many users dislike the style of using functools or needing to pass both the Op and a dictionary around. In which case, rewriting the Op as a Module can be a nice solution - in particular, the difference between configuration parameters and inputs from the Graph are now made explicit:

class UsefulModule(snt.AbstractModule):
  def __init__(self, use_clipping=True, remove_nans=False,
               solve_agi='maybe', name='useful_module')
    super(UsefulModule, self).__init__(name=name)
    self._use_clipping = use_clipping
    self._remove_nans = remove_nans
    self._solve_agi = solve_agi
  def _build(self, input_a, input_b):

Q: Can I mix this with other high level TF APIs, eg TF Slim?

A: Sonnet modules, once constructed, follow the Tensor-In-Tensor-Out principle, so can be mixed with functions from TF-Slim, etc. Note that this may lead to unexpected behaviour - TF-Slim controls sharing by passing explicit scope= and reuse= kwargs into the layer functions - if you use a TF-Slim layer inside the _build() method of a Sonnet module, then calling it multiple times is not likely to work correctly.

Q: Shouldn’t I be overriding __call__ in modules?

A: No. AbstractModule.__init__ provides an implementation of __call__, which calls an internal function wrapped in a Template, which in turn wraps the _build method. Overriding __call__ yourself will likely break variable sharing.

Q: What is the overhead of using Sonnet vs other libraries vs raw TF?

A: None. Sonnet is only involved when constructing the computation graph. Once you are at the stage of using you are simply executing Ops, without regard for what library was used to put that graph together.

Q: How do I list all the variables which are used in any way in a Module?

A: Currently, not easily possible. Although there is a get_variables() method, it only searches the VariableScope defined inside a module, which will contain any internally constructed variables or modules. However, the actual computation done by a module could use other modules - for example, the snt.Sequential module in the example section above. The modules passed into the constructor have by definition been constructed before the Sequential, and so they have different variable scopes. Currently, once the Sequential is connected into the graph, querying it with get_variables() will return an empty tuple.

The DeepMind Research Engineering team is considering future additions to the Module API which remedy this, without requiring extra effort from module implementors.

Q: How do I serialize Sonnet module instances?

A: We do not support serializing module instances, via pickle or any other method. Modules contain inherently non-serializable artifacts, such as references to Variables and Graphs. To save a model, you should instead serialize the configuration information which will allow you to regenerate the Graph, such as the name and constructor parameters of some top level module. This avoids any complications with adding new members to modules, and then having to deal with old serialized instances where those members don’t exist.

Installing from source

To install Sonnet from source, you will need to compile the library using bazel. You should have installed TensorFlow by following the TensorFlow installation instructions.

Install bazel

Ensure you have a recent version of bazel (>= 0.4.5) and JDK (>= 1.8). If not, follow these directions.

(virtualenv TensorFlow installation) Activate virtualenv

If using virtualenv, activate your virtualenv for the rest of the installation, otherwise skip this step:

$ source $VIRTUALENV_PATH/bin/activate # bash, sh, ksh, or zsh
$ source $VIRTUALENV_PATH/bin/activate.csh  # csh or tcsh

Build and run the installer

First clone the Sonnet source code:

$ git clone

Then run the install script to create a wheel file in a temporary directory:

$ mkdir /tmp/sonnet
$ bazel build :install
$ ./bazel-bin/install /tmp/sonnet

To build the GPU version instead, add a flag to the bazel build :install command above: bazel build :install --define gpu=true.

By default, the wheel file is built using python. You can optionally specify another python binary in the previous command to build the wheel file, such as python3:

$ ./bazel-bin/install /tmp/sonnet python3

pip install the generated wheel file:

$ pip install /tmp/sonnet/*.whl

If Sonnet was already installed, uninstall prior to calling pip install on the wheel file:

$ pip uninstall dm-sonnet  # or dm-sonnet-gpu

You can verify that Sonnet has been successfully installed by, for example, trying to instantiate and connect a Linear module:

$ cd ~/
$ python
>>> import sonnet as snt
>>> input_ = tf.zeros(3, 5)
>>> output = snt.Linear(10)(input_)

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