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_conv_block(input_nodes):
# first node of block
n1 = VariableNode('N1')
for inpt in input_nodes:
n1.add_op(Connect(cell.graph, inpt, n1))
def create_conv_node(name):
n = VariableNode(name)
n.add_op(Identity())
n.add_op(Conv1D(filter_size=5, num_filters=2))
n.add_op(Conv1D(filter_size=5, num_filters=3))
n.add_op(MaxPooling1D(pool_size=3, padding='same'))
n.add_op(MaxPooling1D(pool_size=5, padding='same'))
return n
# second node of block
n2 = create_conv_node('N2')
n3 = create_conv_node('N3')
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
return block
def create_cell_conv(input_nodes):
"""Create a cell with convolution.
Args:
input_nodes (list(Node)): a list of input_nodes for this cell.
Returns:
Cell: the corresponding cell.
"""
cell = Cell(input_nodes)
n1 = ConstantNode(op=Conv1D(filter_size=20, num_filters=128), name='N1')
cell.graph.add_edge(input_nodes[0], n1) # fixed input connection
n2 = ConstantNode(op=Activation(activation='relu'), name='N2')
n3 = ConstantNode(op=MaxPooling1D(pool_size=1, padding='same'), name='N3')
n4 = ConstantNode(op=Conv1D(filter_size=10, num_filters=128),name='N4')
n5 = ConstantNode(op=Activation(activation='relu'), name='N5')
n6 = ConstantNode(op=MaxPooling1D(pool_size=10, padding='same'), name='N6')
n7 = ConstantNode(op=Flatten(), name='N7')
n8 = ConstantNode(op=Dense(units=200), name='N8')
n9 = ConstantNode(op=Activation(activation='relu'), name='N9')
n10 = ConstantNode(op=Dropout(rate=0.1), name='N10')
n11 = ConstantNode(op=Dense(units=20), name='N11')
n12 = ConstantNode(op=Activation(activation='relu'), name='N12')
n13 = ConstantNode(op=Dropout(rate=0.1), name='N13')
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_node(n4)
block.add_node(n5)
block.add_node(n6)
block.add_node(n7)
block.add_node(n8)
block.add_node(n9)
block.add_node(n10)
block.add_node(n11)
block.add_node(n12)
block.add_node(n13)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
block.add_edge(n3, n4)
block.add_edge(n4, n5)
block.add_edge(n5, n6)
block.add_edge(n6, n7)
block.add_edge(n7, n8)
block.add_edge(n8, n9)
block.add_edge(n9, n10)
block.add_edge(n10, n11)
block.add_edge(n11, n12)
block.add_edge(n12, n13)
cell.add_block(block)
cell.set_outputs()
return cell
