tf.contrib.rnn.IntersectionRNNCell

class tf.contrib.rnn.IntersectionRNNCell

Defined in tensorflow/contrib/rnn/python/ops/rnn_cell.py.

Intersection Recurrent Neural Network (+RNN) cell.

Architecture with coupled recurrent gate as well as coupled depth gate, designed to improve information flow through stacked RNNs. As the architecture uses depth gating, the dimensionality of the depth output (y) also should not change through depth (input size == output size). To achieve this, the first layer of a stacked Intersection RNN projects the inputs to N (num units) dimensions. Therefore when initializing an IntersectionRNNCell, one should set num_in_proj = N for the first layer and use default settings for subsequent layers.

This implements the recurrent cell from the paper:

https://arxiv.org/abs/1611.09913

Jasmine Collins, Jascha Sohl-Dickstein, and David Sussillo. "Capacity and Trainability in Recurrent Neural Networks" Proc. ICLR 2017.

The Intersection RNN is built for use in deeply stacked RNNs so it may not achieve best performance with depth 1.

Properties

graph

losses

non_trainable_variables

non_trainable_weights

output_size

scope_name

state_size

trainable_variables

trainable_weights

updates

variables

Returns the list of all layer variables/weights.

Returns:

A list of variables.

weights

Returns the list of all layer variables/weights.

Returns:

A list of variables.

Methods

__init__

__init__(
    num_units,
    num_in_proj=None,
    initializer=None,
    forget_bias=1.0,
    y_activation=tf.nn.relu,
    reuse=None
)

Initialize the parameters for an +RNN cell.

Args:

  • num_units: int, The number of units in the +RNN cell
  • num_in_proj: (optional) int, The input dimensionality for the RNN. If creating the first layer of an +RNN, this should be set to num_units. Otherwise, this should be set to None (default). If None, dimensionality of inputs should be equal to num_units, otherwise ValueError is thrown.
  • initializer: (optional) The initializer to use for the weight matrices.
  • forget_bias: (optional) float, default 1.0, The initial bias of the forget gates, used to reduce the scale of forgetting at the beginning of the training.
  • y_activation: (optional) Activation function of the states passed through depth. Default is 'tf.nn.relu`.
  • reuse: (optional) Python boolean describing whether to reuse variables in an existing scope. If not True, and the existing scope already has the given variables, an error is raised.

__call__

__call__(
    inputs,
    state,
    scope=None
)

Run this RNN cell on inputs, starting from the given state.

Args:

  • inputs: 2-D tensor with shape [batch_size x input_size].
  • state: if self.state_size is an integer, this should be a 2-D Tensor with shape [batch_size x self.state_size]. Otherwise, if self.state_size is a tuple of integers, this should be a tuple with shapes [batch_size x s] for s in self.state_size.
  • scope: VariableScope for the created subgraph; defaults to class name.

Returns:

A pair containing:

  • Output: A 2-D tensor with shape [batch_size x self.output_size].
  • New state: Either a single 2-D tensor, or a tuple of tensors matching the arity and shapes of state.

__deepcopy__

__deepcopy__(memo)

add_loss

add_loss(
    losses,
    inputs=None
)

Add loss tensor(s), potentially dependent on layer inputs.

Some losses (for instance, activity regularization losses) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.losses may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_losses_for method allows to retrieve the losses relevant to a specific set of inputs.

Arguments:

  • losses: Loss tensor, or list/tuple of tensors.
  • inputs: Optional input tensor(s) that the loss(es) depend on. Must match the inputs argument passed to the __call__ method at the time the losses are created. If None is passed, the losses are assumed to be unconditional, and will apply across all dataflows of the layer (e.g. weight regularization losses).

add_update

add_update(
    updates,
    inputs=None
)

Add update op(s), potentially dependent on layer inputs.

Weight updates (for instance, the updates of the moving mean and variance in a BatchNormalization layer) may be dependent on the inputs passed when calling a layer. Hence, when reusing a same layer on different inputs a and b, some entries in layer.updates may be dependent on a and some on b. This method automatically keeps track of dependencies.

The get_updates_for method allows to retrieve the updates relevant to a specific set of inputs.

Arguments:

  • updates: Update op, or list/tuple of update ops.
  • inputs: Optional input tensor(s) that the update(s) depend on. Must match the inputs argument passed to the __call__ method at the time the updates are created. If None is passed, the updates are assumed to be unconditional, and will apply across all dataflows of the layer.

add_variable

add_variable(
    name,
    shape,
    dtype=None,
    initializer=None,
    regularizer=None,
    trainable=True
)

Adds a new variable to the layer, or gets an existing one; returns it.

Arguments:

  • name: variable name.
  • shape: variable shape.
  • dtype: The type of the variable. Defaults to self.dtype.
  • initializer: initializer instance (callable).
  • regularizer: regularizer instance (callable).
  • trainable: whether the variable should be part of the layer's "trainable_variables" (e.g. variables, biases) or "non_trainable_variables" (e.g. BatchNorm mean, stddev).

Returns:

The created variable.

apply

apply(
    inputs,
    *args,
    **kwargs
)

Apply the layer on a input.

This simply wraps self.__call__.

Arguments:

  • inputs: Input tensor(s). args: additional positional arguments to be passed to self.call.
    *kwargs: additional keyword arguments to be passed to self.call.

Returns:

Output tensor(s).

build

build(_)

call

call(
    inputs,
    state
)

Run one step of the Intersection RNN.

Args:

  • inputs: input Tensor, 2D, batch x input size.
  • state: state Tensor, 2D, batch x num units.

Returns:

  • new_y: batch x num units, Tensor representing the output of the +RNN after reading inputs when previous state was state.
  • new_state: batch x num units, Tensor representing the state of the +RNN after reading inputs when previous state was state.

Raises:

  • ValueError: If input size cannot be inferred from inputs via static shape inference.
  • ValueError: If input size != output size (these must be equal when using the Intersection RNN).

get_losses_for

get_losses_for(inputs)

Retrieves losses relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the losses were created. If you pass inputs=None, unconditional losses are returned, such as weight regularization losses.

Returns:

List of loss tensors of the layer that depend on inputs.

get_updates_for

get_updates_for(inputs)

Retrieves updates relevant to a specific set of inputs.

Arguments:

  • inputs: Input tensor or list/tuple of input tensors. Must match the inputs argument passed to the __call__ method at the time the updates were created. If you pass inputs=None, unconditional updates are returned.

Returns:

List of update ops of the layer that depend on inputs.

zero_state

zero_state(
    batch_size,
    dtype
)

Return zero-filled state tensor(s).

Args:

  • batch_size: int, float, or unit Tensor representing the batch size.
  • dtype: the data type to use for the state.

Returns:

If state_size is an int or TensorShape, then the return value is a N-D tensor of shape [batch_size x state_size] filled with zeros.

If state_size is a nested list or tuple, then the return value is a nested list or tuple (of the same structure) of 2-D tensors with the shapes [batch_size x s] for each s in state_size.