def from_config(cls, config):
blocks = [
blocks_module.deserialize(block) for block in config['blocks']
]
nodes = {
int(node_id): nodes_module.deserialize(node)
for node_id, node in config['nodes'].items()
}
override_hps = [
kerastuner.engine.hyperparameters.deserialize(config)
for config in config['override_hps']
]
inputs = [nodes[node_id] for node_id in nodes]
for block_id, block in enumerate(blocks):
input_nodes = [
nodes[node_id]
for node_id in config['block_inputs'][str(block_id)]
]
output_nodes = nest.flatten(block(input_nodes))
for output_node, node_id in zip(
output_nodes, config['block_outputs'][str(block_id)]):
nodes[node_id] = output_node
outputs = [nodes[node_id] for node_id in config['outputs']]
return cls(inputs=inputs, outputs=outputs, override_hps=override_hps)
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 from_config(cls, config):
blocks = [
blocks_module.deserialize(block) for block in config["blocks"]
]
nodes = {
int(node_id): nodes_module.deserialize(node)
for node_id, node in config["nodes"].items()
}
inputs = [nodes[node_id] for node_id in nodes]
for block_id, block in enumerate(blocks):
input_nodes = [
nodes[node_id]
for node_id in config["block_inputs"][str(block_id)]
]
output_nodes = nest.flatten(block(input_nodes))
for output_node, node_id in zip(
output_nodes, config["block_outputs"][str(block_id)]):
nodes[node_id] = output_node
outputs = [nodes[node_id] for node_id in config["outputs"]]
return cls(inputs=inputs, outputs=outputs)
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)