def test_merge():
input_shape_1 = (32,)
input_shape_2 = (4, 8)
block = block_module.Merge()
block.set_state(block.get_state())
hp = kerastuner.HyperParameters()
block.build(hp, [ak.Input(shape=input_shape_1).build(),
ak.Input(shape=input_shape_2).build()])
assert common.name_in_hps('merge_type', hp)
def assemble(inputs, outputs, dataset, seed=None):
"""Assemble the HyperBlocks based on the dataset and input output nodes.
# Arguments
inputs: A list of InputNode. The input nodes of the AutoModel.
outputs: A list of HyperHead. The heads of the AutoModel.
dataset: tf.data.Dataset. The training dataset.
seed: Int. Random seed.
# Returns
A list of HyperNode. The output nodes of the AutoModel.
"""
inputs = nest.flatten(inputs)
outputs = nest.flatten(outputs)
assemblers = []
for input_node in inputs:
if isinstance(input_node, node.TextInput):
assemblers.append(TextAssembler())
if isinstance(input_node, node.ImageInput):
assemblers.append(ImageAssembler(seed=seed))
if isinstance(input_node, node.StructuredDataInput):
assemblers.append(
StructuredDataAssembler(column_names=input_node.column_names,
seed=seed))
if isinstance(input_node, node.TimeSeriesInput):
assemblers.append(TimeSeriesAssembler())
# Iterate over the dataset to fit the assemblers.
hps = []
for x, _ in dataset:
for temp_x, assembler in zip(x, assemblers):
assembler.update(temp_x)
hps += assembler.hps
# Assemble the model with assemblers.
middle_nodes = []
for input_node, assembler in zip(inputs, assemblers):
middle_nodes.append(assembler.assemble(input_node))
# Merge the middle nodes.
if len(middle_nodes) > 1:
output_node = block.Merge()(middle_nodes)
else:
output_node = middle_nodes[0]
outputs = nest.flatten(
[output_blocks(output_node) for output_blocks in outputs])
hm = graph.GraphHyperModel(inputs, outputs)
hm.set_hps(hps)
return hm
def assemble(inputs, outputs, dataset):
"""Assemble the HyperBlocks based on the dataset and input output nodes.
# Arguments
inputs: A list of InputNode. The input nodes of the AutoModel.
outputs: A list of HyperHead. The heads of the AutoModel.
dataset: tf.data.Dataset. The training dataset.
# Returns
A list of HyperNode. The output nodes of the AutoModel.
"""
inputs = nest.flatten(inputs)
outputs = nest.flatten(outputs)
assemblers = []
for input_node in inputs:
if isinstance(input_node, node.TextInput):
assemblers.append(TextAssembler())
if isinstance(input_node, node.ImageInput):
assemblers.append(ImageAssembler())
if isinstance(input_node, node.StructuredInput):
assemblers.append(StructuredDataAssembler())
if isinstance(input_node, node.TimeSeriesInput):
assemblers.append(TimeSeriesAssembler())
# Iterate over the dataset to fit the assemblers.
for x, _ in dataset:
for temp_x, assembler in zip(x, assemblers):
assembler.update(temp_x)
# Assemble the model with assemblers.
middle_nodes = []
for input_node, assembler in zip(inputs, assemblers):
middle_nodes.append(assembler.assemble(input_node))
# Merge the middle nodes.
if len(middle_nodes) > 1:
output_node = block.Merge()(middle_nodes)
else:
output_node = middle_nodes[0]
return nest.flatten(
[output_blocks(output_node) for output_blocks in outputs])
