def create_structure(input_shape=(2,), output_shape=(1,), *args, **kwargs):
network = KerasStructure(input_shape, output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
# CELL 1
cell1 = create_cell_conv(input_nodes)
network.add_cell(cell1)
return network
def create_structure(input_shape=[(2, ), (2, ), (2, )],
output_shape=(1, ),
num_cell=8,
*args,
**kwargs):
network = KerasStructure(input_shape,
output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
# CELL 1
cell1 = create_cell_1(input_nodes)
network.add_cell(cell1)
# CELL Middle
inputs_skipco = [
input_nodes, input_nodes[0], input_nodes[1], input_nodes[2],
cell1.output
]
pred_cell = cell1
n = num_cell
for i in range(n):
cell_i = Cell(input_nodes + [cell1.output])
block1 = create_mlp_block(cell_i, pred_cell.output)
cell_i.add_block(block1)
cnode = VariableNode(name='SkipCo')
nullNode = ConstantNode(op=Tensor([]), name='None')
cnode.add_op(Connect(cell_i.graph, nullNode, cnode)) # SAME
for inpt in inputs_skipco:
cnode.add_op(Connect(cell_i.graph, inpt, cnode))
block2 = Block()
block2.add_node(cnode)
cell_i.add_block(block2)
# set_cell_output_add(cell2)
cell_i.set_outputs()
network.add_cell(cell_i)
# prep. for next iter
inputs_skipco.append(cell_i.output)
pred_cell = cell_i
# CELL LAST
cell_last = Cell([pred_cell.output])
block1 = create_mlp_block(cell_last, pred_cell.output)
cell_last.add_block(block1)
# set_cell_output_add(cell3)
cell_last.set_outputs()
network.add_cell(cell_last)
return network
def create_struct_full_skipco(input_shape, output_shape, create_cell,
num_cells):
"""
Create a SequentialStructure object.
Args:
input_shape (tuple): shape of input tensor
output_shape (tuple): shape of output tensor
create_cell (function): function that create a cell, take one argument (inputs: list(None))
num_cells (int): number of cells in the sequential structure
Return:
KerasStructure: the corresponding built structure.
"""
network = KerasStructure(input_shape, output_shape)
input_nodes = network.input_nodes
func = lambda: create_cell(input_nodes)
network.add_cell_f(func)
func = lambda x: create_cell(x)
for i in range(num_cells - 1):
network.add_cell_f(func, num=None)
return network
def create_structure(input_shape=[(2, ) for _ in range(4)],
output_shape=(1, ),
num_cell=8,
*args,
**kwargs):
network = KerasStructure(input_shape,
output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
print('input_nodes: ', input_nodes)
# CELL 1
cell1 = create_cell_1(input_nodes)
network.add_cell(cell1)
# # CELL Middle
# inputs_skipco = [input_nodes, input_nodes[0], input_nodes[1], input_nodes[2], cell1.output]
pred_cell = cell1
# CELL LAST
cell_last = Cell([pred_cell.output])
first_node = create_mlp_block(cell_last, pred_cell.output)
set_cell_output_add(cell_last)
network.add_cell(cell_last)
return network
def create_structure(input_shape=(2, ), output_shape=(1, ), *args, **kwargs):
network = KerasStructure(input_shape,
output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
# CELL 1
cell1 = create_cell_conv(input_nodes)
network.add_cell(cell1)
# CELL 2
cell2 = create_cell_conv([cell1.output])
network.add_cell(cell2)
# CELL 3
cell3 = create_cell_mlp([cell2.output])
network.add_cell(cell3)
# CELL 4
cell4 = create_cell_mlp([cell3.output])
network.add_cell(cell4)
return network
def create_structure(input_shape=[(2, ), (2, ), (2, )],
output_shape=(1, ),
*args,
**kwargs):
network = KerasStructure(input_shape,
output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
# CELL 1
cell1 = create_cell_1(input_nodes)
network.add_cell(cell1)
# CELL 2
cell2 = Cell(input_nodes + [cell1.output])
block1 = create_mlp_block(cell2, cell1.output)
cell2.add_block(block1)
cnode = VariableNode(name='SkipCo')
nullNode = ConstantNode(op=Tensor([]), name='None')
cnode.add_op(Connect(cell2.graph, nullNode, cnode)) # SAME
cnode.add_op(Connect(cell2.graph, input_nodes[0], cnode))
cnode.add_op(Connect(cell2.graph, input_nodes[1], cnode))
cnode.add_op(Connect(cell2.graph, input_nodes[2], cnode))
cnode.add_op(Connect(cell2.graph, cell1.output, cnode))
cnode.add_op(Connect(cell2.graph, input_nodes, cnode))
cnode.add_op(Connect(cell2.graph, [input_nodes[0], input_nodes[1]], cnode))
cnode.add_op(Connect(cell2.graph, [input_nodes[1], input_nodes[2]], cnode))
cnode.add_op(Connect(cell2.graph, [input_nodes[0], input_nodes[2]], cnode))
block2 = Block()
block2.add_node(cnode)
cell2.add_block(block2)
# set_cell_output_add(cell2)
cell2.set_outputs()
network.add_cell(cell2)
# CELL 3
cell3 = Cell([cell2.output])
block1 = create_mlp_block(cell3, cell2.output)
cell3.add_block(block1)
# set_cell_output_add(cell3)
cell3.set_outputs()
network.add_cell(cell3)
return network
def create_structure(input_shape=[(2,) for _ in range(4)], output_shape=(1,), num_cell=8, *args, **kwargs):
network = KerasStructure(input_shape, output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
print('input_nodes: ', input_nodes)
# CELL 1
cell1 = create_cell_1(input_nodes)
network.add_cell(cell1)
# # CELL Middle
pred_cell = cell1
skipco_inputs = [
input_nodes,
[input_nodes[0], input_nodes[1], input_nodes[2]],
[input_nodes[0], input_nodes[1], input_nodes[3]],
[input_nodes[0], input_nodes[2], input_nodes[3]],
[input_nodes[1], input_nodes[2], input_nodes[3]],
[input_nodes[0], input_nodes[1]],
[input_nodes[0], input_nodes[2]],
[input_nodes[0], input_nodes[3]],
[input_nodes[1], input_nodes[2]],
[input_nodes[1], input_nodes[3]],
[input_nodes[2], input_nodes[3]],
input_nodes[0],
input_nodes[1],
input_nodes[2],
input_nodes[3],
]
for _ in range(8):
curr_cell = Cell([pred_cell.output])# + skipco_inputs) # current cell
dense_node = create_mlp_block(curr_cell, pred_cell.output, skipco_inputs)
set_cell_output_add(curr_cell)
network.add_cell(curr_cell)
# preparing next iter
skipco_inputs.append(pred_cell.output)
skipco_inputs.append(dense_node)
pred_cell = curr_cell
return network
def create_structure(input_shape=[(2, ), (2, ), (2, )],
output_shape=(1, ),
*args,
**kwargs):
network = KerasStructure(input_shape,
output_shape) #, output_op=AddByPadding)
input_nodes = network.input_nodes
# CELL 1
cell1 = create_cell_1(input_nodes)
network.add_cell(cell1)
# CELL 2
cell2 = Cell([cell1.output])
block1 = create_mlp_block(cell2, cell1.output)
cell2.add_block(block1)
cell2.set_outputs()
network.add_cell(cell2)
return network
def create_seq_struct(input_shape, output_shape, create_cell, num_cells):
"""
Create a KerasStructure object.
Args:
input_tensor (tensor): a tensorflow tensor object
create_cell (function): function that create a cell, take one argument (inputs: list(None))
num_cells (int): number of cells in the sequential structure
Return:
KerasStructure: the corresponding built structure.
"""
network = KerasStructure(input_shape, output_shape)
input_nodes = network.input_nodes
func = lambda: create_cell(input_nodes)
network.add_cell_f(func)
func = lambda x: create_cell(x)
for _ in range(num_cells - 1):
network.add_cell_f(func, num=1)
return network