deephyper.nas.KSearchSpace

class deephyper.nas.KSearchSpace(input_shape, output_shape, batch_size=None, seed=None, *args, **kwargs)

Bases: NxSearchSpace

A KSearchSpace represents a search space of neural networks.

>>> import tensorflow as tf
>>> from deephyper.nas import KSearchSpace
>>> from deephyper.nas.node import ConstantNode, VariableNode
>>> from deephyper.nas.operation import operation, Identity
>>> Dense = operation(tf.keras.layers.Dense)
>>> Dropout = operation(tf.keras.layers.Dropout)

>>> class ExampleSpace(KSearchSpace):
...     def build(self):
...         # input nodes are automatically built based on `input_shape`
...         input_node = self.input_nodes[0]
...         # we want 4 layers maximum (Identity corresponds to not adding a layer)
...         for i in range(4):
...             node = VariableNode()
...             self.connect(input_node, node)
...             # we add 3 possible operations for each node
...             node.add_op(Identity())
...             node.add_op(Dense(100, "relu"))
...             node.add_op(Dropout(0.2))
...             input_node = node
...         output = ConstantNode(op=Dense(self.output_shape[0]))
...         self.connect(input_node, output)
...         return self
...
>>>

>>> space = ExampleSpace(input_shape=(1,), output_shape=(1,)).build()
>>> space.sample().summary()

Parameters:

• input_shape (list(tuple(int))) – list of shapes of all inputs.

• output_shape (tuple(int)) – shape of output.

• batch_size (list(tuple(int))) – batch size of the input layer. If input_shape is defining a list of inputs, batch_size should also define a list of inputs.

Raises:

InputShapeOfWrongType – [description]

Methods

add_node	Add a new node to the search_space.
build	Build the current graph search space.
choices	Gives the possible choices for each decision variable of the search space.
connect	Create a new connection in the KSearchSpace graph.
create_model	Create the tensors corresponding to the search_space.
create_tensor_aux	Recursive function to create the tensors from the graph.
denormalize	Denormalize a sequence of normalized indexes to get a sequence of absolute indexes.
plot
sample	Sample a tf.keras.Model from the search space.
set_ops	Set the operations for each node of each cell of the search_space.
set_output_node	Set the output node of the search_space.

Attributes

depth
input
longest_path
max_num_ops	Returns the maximum number of operations accross all VariableNodes of the struct.
mime_nodes	Iterator of MimeNodes of the search_space.
nodes	Nodes of the current KSearchSpace.
num_nodes	Returns the number of VariableNodes in the current Structure.
output
size	Size of the search space define by the search_space
variable_nodes	Iterator of VariableNodes of the search_space.

add_node(node)

Add a new node to the search_space.

Parameters:

node (Node) – node to add to the search_space.

Raises:

• TypeError – if ‘node’ is not an instance of Node.

• NodeAlreadyAdded – if ‘node’ has already been added to the search_space.

abstract build()

Build the current graph search space.

choices()

Gives the possible choices for each decision variable of the search space.

Returns:

A list of tuple where each element corresponds to a discrete variable represented by (low, high).

Return type:

list

connect(node1, node2)

Create a new connection in the KSearchSpace graph.

The edge created corresponds to : node1 -> node2.

Parameters:

• node1 (Node) –

• node2 (Node) –

Raises:

StructureHasACycle – if the new edge is creating a cycle.

denormalize(indexes)

Denormalize a sequence of normalized indexes to get a sequence of absolute indexes. Useful when you want to compare the number of different search_spaces.

Parameters:

indexes (Iterable) – a sequence of normalized indexes.

Returns:

A list of absolute indexes corresponding to operations choosen with relative indexes of indexes.

Return type:

list

property max_num_ops

Returns the maximum number of operations accross all VariableNodes of the struct.

Returns:

maximum number of Operations for a VariableNode in the current Structure.

Return type:

int

property mime_nodes

Iterator of MimeNodes of the search_space.

Returns:

iterator of MimeNodes of the search_space.

Return type:

(Iterator(MimeNode))

property nodes

Nodes of the current KSearchSpace.

Returns:

nodes of the current KSearchSpace.

Return type:

iterator

property num_nodes

Returns the number of VariableNodes in the current Structure.

Returns:

number of VariableNodes in the current Structure.

Return type:

int

sample(choice=None)

Sample a tf.keras.Model from the search space.

Parameters:

choice (list, optional) – A list of decision for the operations of this search space. Defaults to None, will generate a random sample.

Returns:

A Tensorflow Keras model.

Return type:

tf.keras.Model

property size

Size of the search space define by the search_space

property variable_nodes

Iterator of VariableNodes of the search_space.

Returns:

generator of VariablesNodes of the search_space.

Return type:

(Iterator(VariableNode))