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)
