def create_cell_1(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)
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=3, num_filters=16))
n.add_op(MaxPooling1D(pool_size=3, padding='same'))
n.add_op(Dense(10, tf.nn.relu))
n.add_op(Conv1D(filter_size=5, num_filters=16))
n.add_op(MaxPooling1D(pool_size=5, padding='same'))
n.add_op(Dense(100, tf.nn.relu))
n.add_op(Conv1D(filter_size=10, num_filters=16))
n.add_op(MaxPooling1D(pool_size=10, padding='same'))
n.add_op(Dense(1000, tf.nn.relu))
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
block1 = create_conv_block(input_nodes)
block2 = create_conv_block(input_nodes)
block3 = create_conv_block(input_nodes)
cell.add_block(block1)
cell.add_block(block2)
cell.add_block(block3)
addNode = ConstantNode(name='Cell_out')
addNode.set_op(AddByPadding(cell.graph, addNode, cell.get_blocks_output()))
cell.set_outputs(node=addNode)
return cell
def create_cell_1(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)
def create_block(input_node):
def add_mlp_ops_to(vnode):
# REG_L1 = 1.
# REG_L2 = 1.
vnode.add_op(Identity())
vnode.add_op(Dense(100, tf.nn.relu))
vnode.add_op(Dense(100, tf.nn.tanh))
vnode.add_op(Dense(100, tf.nn.sigmoid))
vnode.add_op(Dropout(0.05))
vnode.add_op(Dense(500, tf.nn.relu))
vnode.add_op(Dense(500, tf.nn.tanh))
vnode.add_op(Dense(500, tf.nn.sigmoid))
vnode.add_op(Dropout(0.1))
vnode.add_op(Dense(1000, tf.nn.relu))
vnode.add_op(Dense(1000, tf.nn.tanh))
vnode.add_op(Dense(1000, tf.nn.sigmoid))
vnode.add_op(Dropout(0.2))
# first node of block
n1 = VariableNode('N1')
add_mlp_ops_to(n1)
cell.graph.add_edge(input_node, n1) # fixed input of current block
# second node of block
n2 = VariableNode('N2')
add_mlp_ops_to(n2)
# third node of the block
n3 = VariableNode('N3')
add_mlp_ops_to(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, (n1, n2, n3)
block1, _ = create_block(input_nodes[0])
block2, (vn1, vn2, vn3) = create_block(input_nodes[1])
# first node of block
m_vn1 = MirrorNode(node=vn1)
cell.graph.add_edge(input_nodes[2], m_vn1) # fixed input of current block
# second node of block
m_vn2 = MirrorNode(node=vn2)
# third node of the block
m_vn3 = MirrorNode(node=vn3)
block3 = Block()
block3.add_node(m_vn1)
block3.add_node(m_vn2)
block3.add_node(m_vn3)
block3.add_edge(m_vn1, m_vn2)
block3.add_edge(m_vn2, m_vn3)
cell.add_block(block1)
cell.add_block(block2)
cell.add_block(block3)
addNode = ConstantNode(name='Merging')
addNode.set_op(AddByPadding(cell.graph, addNode, cell.get_blocks_output()))
cell.set_outputs(node=addNode)
return cell