# tf.contrib.eager.Sequential

## Class Sequential

Inherits From: Network

Represents a linear sequence of Layers or functions.

The output of each layer/function is provided as the input to the next. The inputs passed to __call__ are passed to the inputs of the first Layer, and it returns the outputs of the last Layer.

#### Args:

• layers_funcs: An optional sequence where each element is either a tf.layers.Layer object or a callable.
• name: An optional string name to use for this Network.

## 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_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

Gather losses from Layers in the Network.

Note that when executing eagerly, Layer.losses 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_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.

## Methods

### __init__

__init__(
layers_funcs=None,
name=None
)


### __call__

__call__(
inputs,
*args,
**kwargs
)


Wraps call, applying pre- and post-processing steps.

#### 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. Note: kwarg scope is reserved for use by the layer.

#### Returns:

Output tensor(s).

#### Raises:

• ValueError: if the layer's call method returns None (an invalid value).

### __deepcopy__

__deepcopy__(memo)


### add

add(layer_func)


### add_loss

add_loss(
losses,
inputs=None
)


### 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
)


### 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(_)


Creates the variables of the layer.

### call

call(
inputs,
training=None
)


Call each Layer in the order they were added.

### compute_output_shape

compute_output_shape(input_shape)


Computes the output shape of the layer given the input shape.

#### Args:

• input_shape: A (possibly nested tuple of) TensorShape. It need not be fully defined (e.g. the batch size may be unknown).

#### Returns:

A (possibly nested tuple of) TensorShape.

#### Raises:

• TypeError: if input_shape is not a (possibly nested tuple of) TensorShape.
• ValueError: if input_shape is incomplete or is incompatible with the the layer.

### 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).

### 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_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_layer

get_layer(
name=None,
index=None
)


Get a contained tf.layers.Layer either by name or index.

#### Args:

• name: String matching one of the names of a contained Layer. Note that the names of Layers added to Networks may not be unique when doing layer sharing (i.e. adding a Layer to this Network which was already added to another Network). The lowest index Layer with a matching name will be returned.
• index: Integer in [0, number of layers). Layers are assigned an index by the order they are added.

#### Returns:

A tf.layers.Layer object.

#### Raises:

• ValueError: If neither or both of 'index' or 'name' is specified, or the lookup failed.

### 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_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.

### track_layer

track_layer(layer)


Track a Layer in this Network.

Network requires that all Layers used in call() be tracked so that the Network can export a complete list of variables.

#### Returns:

The passed in layer.

#### Raises:

• RuntimeError: If init has not been called.
• TypeError: If layer is the wrong type.
• ValueError: If a Layer with the same name has already been added.