# W3cubDocs

/TensorFlow Python

# tf.keras.layers.SimpleRNN

## Class `SimpleRNN`

Inherits From: `RNN`

Fully-connected RNN where the output is to be fed back to input.

#### Arguments:

• `units`: Positive integer, dimensionality of the output space.
• `activation`: Activation function to use. Default: hyperbolic tangent (`tanh`). If you pass None, no activation is applied (ie. "linear" activation: `a(x) = x`).
• `use_bias`: Boolean, whether the layer uses a bias vector.
• `kernel_initializer`: Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs.
• `recurrent_initializer`: Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state.
• `bias_initializer`: Initializer for the bias vector.
• `kernel_regularizer`: Regularizer function applied to the `kernel` weights matrix.
• `recurrent_regularizer`: Regularizer function applied to the `recurrent_kernel` weights matrix.
• `bias_regularizer`: Regularizer function applied to the bias vector.
• `activity_regularizer`: Regularizer function applied to the output of the layer (its "activation")..
• `kernel_constraint`: Constraint function applied to the `kernel` weights matrix.
• `recurrent_constraint`: Constraint function applied to the `recurrent_kernel` weights matrix.
• `bias_constraint`: Constraint function applied to the bias vector.
• `dropout`: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs.
• `recurrent_dropout`: Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state.
• `return_sequences`: Boolean. Whether to return the last output in the output sequence, or the full sequence.
• `return_state`: Boolean. Whether to return the last state in addition to the output.
• `go_backwards`: Boolean (default False). If True, process the input sequence backwards and return the reversed sequence.
• `stateful`: Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.
• `unroll`: Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN, although it tends to be more memory-intensive. Unrolling is only suitable for short sequences.

## Properties

### `activity_regularizer`

Optional regularizer function for the output of this layer.

### `input`

Retrieves the input tensor(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.

#### Returns:

Input tensor or list of input tensors.

#### Raises:

• `AttributeError`: if the layer is connected to more than one incoming layers.

#### Raises:

• `RuntimeError`: If called in Eager mode.
• `AttributeError`: If no inbound nodes are found.

### `input_mask`

Retrieves the input mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

#### Raises:

• `AttributeError`: if the layer is connected to more than one incoming layers.

### `input_shape`

Retrieves the input shape(s) of a layer.

Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.

#### Returns:

Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).

#### Raises:

• `AttributeError`: if the layer has no defined input_shape.
• `RuntimeError`: if called in Eager mode.

### `losses`

Losses which are associated with this `Layer`.

Note that when executing eagerly, getting this property evaluates regularizers. When using graph execution, variable regularization ops have already been created and are simply returned here.

#### Returns:

A list of tensors.

### `output`

Retrieves the output tensor(s) of a layer.

Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.

#### Returns:

Output tensor or list of output tensors.

#### Raises:

• `AttributeError`: if the layer is connected to more than one incoming layers.
• `RuntimeError`: if called in Eager mode.

### `output_mask`

Retrieves the output mask tensor(s) of a layer.

Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.

#### Raises:

• `AttributeError`: if the layer is connected to more than one incoming layers.

### `output_shape`

Retrieves the output shape(s) of a layer.

Only applicable if the layer has one output, or if all outputs have the same shape.

#### Returns:

Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).

#### Raises:

• `AttributeError`: if the layer has no defined output shape.
• `RuntimeError`: if called in Eager mode.

### `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__(
units,
activation='tanh',
use_bias=True,
kernel_initializer='glorot_uniform',
recurrent_initializer='orthogonal',
bias_initializer='zeros',
kernel_regularizer=None,
recurrent_regularizer=None,
bias_regularizer=None,
activity_regularizer=None,
kernel_constraint=None,
recurrent_constraint=None,
bias_constraint=None,
dropout=0.0,
recurrent_dropout=0.0,
return_sequences=False,
return_state=False,
go_backwards=False,
stateful=False,
unroll=False,
**kwargs
)
```

Initialize self. See help(type(self)) for accurate signature.

### `__call__`

```__call__(
inputs,
initial_state=None,
constants=None,
**kwargs
)
```

Wrapper around self.call(), for handling internal references.

If a Keras tensor is passed: - We call self._add_inbound_node(). - If necessary, we `build` the layer to match the shape of the input(s). - We update the _keras_history of the output tensor(s) with the current layer. This is done as part of _add_inbound_node().

#### Arguments:

• `inputs`: Can be a tensor or list/tuple of tensors.
• `*args`: Additional positional arguments to be passed to `call()`. Only allowed in subclassed Models with custom call() signatures. In other cases, `Layer` inputs must be passed using the `inputs` argument and non-inputs must be keyword arguments.
• `**kwargs`: Additional keyword arguments to be passed to `call()`.

#### Returns:

Output of the layer's `call` method.

#### Raises:

• `ValueError`: in case the layer is missing shape information for its `build` call.
• `TypeError`: If positional arguments are passed and this `Layer` is not a subclassed `Model`.

### `__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 the 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.

Note that `add_loss` is not supported when executing eagerly. Instead, variable regularizers may be added through `add_variable`. Activity regularization is not supported directly (but such losses may be returned from `Layer.call()`).

#### Arguments:

• `losses`: Loss tensor, or list/tuple of tensors.
• `inputs`: If anything other than None is passed, it signals the losses are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for activity regularization losses, for instance. 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).

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `add_update`

```add_update(
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 the 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.

This call is ignored in Eager mode.

#### Arguments:

• `updates`: Update op, or list/tuple of update ops.
• `inputs`: If anything other than None is passed, it signals the updates are conditional on some of the layer's inputs, and thus they should only be run where these inputs are available. This is the case for BatchNormalization updates, for instance. If None, the updates will be taken into account unconditionally, and you are responsible for making sure that any dependency they might have is available at runtime. A step counter might fall into this category.

### `add_variable`

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

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` or `float32`.
• `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). Note, if the current variable scope is marked as non-trainable then this parameter is ignored and any added variables are also marked as non-trainable.
• `constraint`: constraint instance (callable).
• `partitioner`: (optional) partitioner instance (callable). If provided, when the requested variable is created it will be split into multiple partitions according to `partitioner`. In this case, an instance of `PartitionedVariable` is returned. Available partitioners include `tf.fixed_size_partitioner` and `tf.variable_axis_size_partitioner`. For more details, see the documentation of `tf.get_variable` and the "Variable Partitioners and Sharding" section of the API guide.

#### Returns:

The created variable. Usually either a `Variable` or `ResourceVariable` instance. If `partitioner` is not `None`, a `PartitionedVariable` instance is returned.

#### Raises:

• `RuntimeError`: If called with partioned variable regularization and eager execution is enabled.

### `add_weight`

```add_weight(
name,
shape,
dtype=None,
initializer=None,
regularizer=None,
trainable=True,
constraint=None
)
```

Adds a weight variable to the layer.

#### Arguments:

• `name`: String, the name for the weight variable.
• `shape`: The shape tuple of the weight.
• `dtype`: The dtype of the weight.
• `initializer`: An Initializer instance (callable).
• `regularizer`: An optional Regularizer instance.
• `trainable`: A boolean, whether the weight should be trained via backprop or not (assuming that the layer itself is also trainable).
• `constraint`: An optional Constraint instance.

#### Returns:

The created weight 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(
instance,
input_shape
)
```

### `call`

```call(
inputs,
training=None,
initial_state=None
)
```

This is where the layer's logic lives.

#### Arguments:

• `inputs`: Input tensor, or list/tuple of input tensors.
• `**kwargs`: Additional keyword arguments.

#### Returns:

A tensor or list/tuple of tensors.

### `compute_mask`

```compute_mask(
inputs,
)
```

#### Arguments:

• `inputs`: Tensor or list of tensors.
• `mask`: Tensor or list of tensors.

#### Returns:

None or a tensor (or list of tensors, one per output tensor of the layer).

### `compute_output_shape`

```compute_output_shape(
instance,
input_shape
)
```

### `count_params`

```count_params()
```

Count the total number of scalars composing the weights.

#### Returns:

An integer count.

#### Raises:

• `ValueError`: if the layer isn't yet built (in which case its weights aren't yet defined).

### `from_config`

```@classmethod
from_config(
cls,
config
)
```

Creates a layer from its config.

This method is the reverse of `get_config`, capable of instantiating the same layer from the config dictionary. It does not handle layer connectivity (handled by Network), nor weights (handled by `set_weights`).

#### Arguments:

• `config`: A Python dictionary, typically the output of get_config.

#### Returns:

A layer instance.

### `get_config`

```get_config()
```

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by `Network` (one layer of abstraction above).

#### Returns:

Python dictionary.

### `get_initial_state`

```get_initial_state(inputs)
```

### `get_input_at`

```get_input_at(node_index)
```

Retrieves the input tensor(s) of a layer at a given node.

#### Arguments:

• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.

#### Returns:

A tensor (or list of tensors if the layer has multiple inputs).

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `get_input_mask_at`

```get_input_mask_at(node_index)
```

Retrieves the input mask tensor(s) of a layer at a given node.

#### Arguments:

• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.

#### Returns:

A mask tensor (or list of tensors if the layer has multiple inputs).

### `get_input_shape_at`

```get_input_shape_at(node_index)
```

Retrieves the input shape(s) of a layer at a given node.

#### Arguments:

• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.

#### Returns:

A shape tuple (or list of shape tuples if the layer has multiple inputs).

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `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.

#### Returns:

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

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `get_output_at`

```get_output_at(node_index)
```

Retrieves the output tensor(s) of a layer at a given node.

#### Arguments:

• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.

#### Returns:

A tensor (or list of tensors if the layer has multiple outputs).

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `get_output_mask_at`

```get_output_mask_at(node_index)
```

Retrieves the output mask tensor(s) of a layer at a given node.

#### Arguments:

• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.

#### Returns:

A mask tensor (or list of tensors if the layer has multiple outputs).

### `get_output_shape_at`

```get_output_shape_at(node_index)
```

Retrieves the output shape(s) of a layer at a given node.

#### Arguments:

• `node_index`: Integer, index of the node from which to retrieve the attribute. E.g. `node_index=0` will correspond to the first time the layer was called.

#### Returns:

A shape tuple (or list of shape tuples if the layer has multiple outputs).

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `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.

#### Returns:

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

#### Raises:

• `RuntimeError`: If called in Eager mode.

### `get_weights`

```get_weights()
```

Returns the current weights of the layer.

#### Returns:

Weights values as a list of numpy arrays.

### `reset_states`

```reset_states(states=None)
```

### `set_weights`

```set_weights(weights)
```

Sets the weights of the layer, from Numpy arrays.

#### Arguments:

• `weights`: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output of `get_weights`).

#### Raises:

• `ValueError`: If the provided weights list does not match the layer's specifications.