def get_config(self): blocks = [blocks_module.serialize(block) for block in self.blocks] nodes = { str(self._node_to_id[node]): nodes_module.serialize(node) for node in self.inputs } override_hps = [ kerastuner.engine.hyperparameters.serialize(hp) for hp in self.override_hps ] block_inputs = { str(block_id): [self._node_to_id[node] for node in block.inputs] for block_id, block in enumerate(self.blocks) } block_outputs = { str(block_id): [self._node_to_id[node] for node in block.outputs] for block_id, block in enumerate(self.blocks) } outputs = [self._node_to_id[node] for node in self.outputs] return { 'override_hps': override_hps, # List [serialized]. 'blocks': blocks, # Dict {id: serialized}. 'nodes': nodes, # Dict {id: serialized}. 'outputs': outputs, # List of node_ids. 'block_inputs': block_inputs, # Dict {id: List of node_ids}. 'block_outputs': block_outputs, # Dict {id: List of node_ids}. }
def test_time_series_input_node_deserialize_build_no_error(): node = nodes.TimeseriesInput(lookback=2, shape=(32, )) node = nodes.deserialize(nodes.serialize(node)) hp = keras_tuner.HyperParameters() input_node = node.build_node(hp) node.build(hp, input_node)
def test_time_series_input_node(): # TODO. Change test once TimeSeriesBlock is added. node = ak.TimeseriesInput(shape=(32, ), lookback=2) output = node.build() assert isinstance(output, tf.Tensor) node = nodes.deserialize(nodes.serialize(node)) output = node.build() assert isinstance(output, tf.Tensor)
def test_time_series_input_node_deserialize_build_to_tensor(): node = nodes.TimeseriesInput(lookback=2, shape=(32, )) node = nodes.deserialize(nodes.serialize(node)) hp = kerastuner.HyperParameters() input_node = node.build_node(hp) output = node.build(hp, input_node) assert isinstance(output, tf.Tensor)
def get_config(self): blocks = [blocks_module.serialize(block) for block in self.blocks] nodes = { str(self._node_to_id[node]): nodes_module.serialize(node) for node in self.inputs } block_inputs = { str(block_id): [self._node_to_id[node] for node in block.inputs] for block_id, block in enumerate(self.blocks) } block_outputs = { str(block_id): [self._node_to_id[node] for node in block.outputs] for block_id, block in enumerate(self.blocks) } outputs = [self._node_to_id[node] for node in self.outputs] return { "blocks": blocks, # Dict {id: serialized}. "nodes": nodes, # Dict {id: serialized}. "outputs": outputs, # List of node_ids. "block_inputs": block_inputs, # Dict {id: List of node_ids}. "block_outputs": block_outputs, # Dict {id: List of node_ids}. }
def test_time_series_input_node_deserialize_build_to_tensor(): node = ak.TimeseriesInput(shape=(32, ), lookback=2) node = nodes.deserialize(nodes.serialize(node)) node.shape = (32, ) output = node.build(kerastuner.HyperParameters()) assert isinstance(output, tf.Tensor)