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# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from tensorflow import nest
from autokeras.engine import named_hypermodel
from autokeras.engine import node as node_module
class Block(named_hypermodel.NamedHyperModel):
"""The base class for different Block.
The Block can be connected together to build the search space
for an AutoModel. Notably, many args in the __init__ function are defaults to
be a tunable variable when not specified by the user.
"""
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.inputs = None
self.outputs = None
self._num_output_node = 1
def _build_wrapper(self, hp, *args, **kwargs):
with hp.name_scope(self.name):
return super()._build_wrapper(hp, *args, **kwargs)
def __call__(self, inputs):
"""Functional API.
# Arguments
inputs: A list of input node(s) or a single input node for the block.
# Returns
list: A list of output node(s) of the Block.
"""
self.inputs = nest.flatten(inputs)
for input_node in self.inputs:
if not isinstance(input_node, node_module.Node):
raise TypeError(
"Expect the inputs to block {name} to be "
"a Node, but got {type}.".format(
name=self.name, type=type(input_node)
)
)
input_node.add_out_block(self)
self.outputs = []
for _ in range(self._num_output_node):
output_node = node_module.Node()
output_node.add_in_block(self)
self.outputs.append(output_node)
return self.outputs
def build(self, hp, inputs=None):
"""Build the Block into a real Keras Model.
The subclasses should override this function and return the output node.
# Arguments
hp: HyperParameters. The hyperparameters for building the model.
inputs: A list of input node(s).
"""
raise NotImplementedError