deephyper.nas.KSearchSpace#

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

Bases: deephyper.nas._nx_search_space.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()[source]#

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)[source]#

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))