contrib.rnn.CoupledInputForgetGateLSTMCell

tf.contrib.rnn.CoupledInputForgetGateLSTMCell

class tf.contrib.rnn.CoupledInputForgetGateLSTMCell

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

See the guide: RNN and Cells (contrib) > Core RNN Cell wrappers (RNNCells that wrap other RNNCells)

Long short-term memory unit (LSTM) recurrent network cell.

The default non-peephole implementation is based on:

http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf

S. Hochreiter and J. Schmidhuber. "Long Short-Term Memory". Neural Computation, 9(8):1735-1780, 1997.

The peephole implementation is based on:

https://research.google.com/pubs/archive/43905.pdf

Hasim Sak, Andrew Senior, and Francoise Beaufays. "Long short-term memory recurrent neural network architectures for large scale acoustic modeling." INTERSPEECH, 2014.

The coupling of input and forget gate is based on:

http://arxiv.org/pdf/1503.04069.pdf

Greff et al. "LSTM: A Search Space Odyssey"

The class uses optional peep-hole connections, and an optional projection layer.

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,
    use_peepholes=False,
    initializer=None,
    num_proj=None,
    proj_clip=None,
    num_unit_shards=1,
    num_proj_shards=1,
    forget_bias=1.0,
    state_is_tuple=True,
    activation=tf.tanh,
    reuse=None
)

Initialize the parameters for an LSTM cell.

Args:

• num_units: int, The number of units in the LSTM cell
• use_peepholes: bool, set True to enable diagonal/peephole connections.
• initializer: (optional) The initializer to use for the weight and projection matrices.
• num_proj: (optional) int, The output dimensionality for the projection matrices. If None, no projection is performed.
• proj_clip: (optional) A float value. If num_proj > 0 and proj_clip is provided, then the projected values are clipped elementwise to within [-proj_clip, proj_clip].
• num_unit_shards: How to split the weight matrix. If >1, the weight matrix is stored across num_unit_shards.
• num_proj_shards: How to split the projection matrix. If >1, the projection matrix is stored across num_proj_shards.
• forget_bias: Biases of the forget gate are initialized by default to 1 in order to reduce the scale of forgetting at the beginning of the training.
• state_is_tuple: If True, accepted and returned states are 2-tuples of the c_state and m_state. By default (False), they are concatenated along the column axis. This default behavior will soon be deprecated.
• activation: Activation function of the inner states.
• 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 LSTM.

Args:

• inputs: input Tensor, 2D, batch x num_units.
• state: if state_is_tuple is False, this must be a state Tensor, 2-D, batch x state_size. If state_is_tuple is True, this must be a tuple of state Tensors, both 2-D, with column sizes c_state and m_state.

Returns:

A tuple containing: - A 2-D, [batch x output_dim], Tensor representing the output of the LSTM after reading inputs when previous state was state. Here output_dim is: num_proj if num_proj was set, num_units otherwise. - Tensor(s) representing the new state of LSTM after reading inputs when the previous state was state. Same type and shape(s) as state.

Raises:

• ValueError: If input size cannot be inferred from inputs via static shape inference.

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.