def create_mlp_block(cell, input_node):
block = Block()
# first node of block
n1 = ConstantNode(op=Dense(1000, tf.nn.relu), name='N1')
cell.graph.add_edge(input_node, n1) # fixed input of current block
block.add_node(n1)
# second node of block
n2 = ConstantNode(op=Dense(1000, tf.nn.relu), name='N2')
block.add_node(n2)
addNode1 = ConstantNode(name='Merging')
addNode1.set_op(AddByPadding()) # edge created here
block.add_node(addNode1)
block.add_edge(n1, n2)
block.add_edge(n2, addNode1)
block.add_edge(n1, addNode1) # residual connection
n3 = ConstantNode(op=Dense(1000, tf.nn.relu), name='N3')
block.add_node(n3)
block.add_edge(addNode1, n3)
addNode2 = ConstantNode(name='Merging')
addNode2.set_op(AddByPadding()) # edge created here
block.add_node(addNode2)
block.add_edge(n3, addNode2)
block.add_edge(addNode1, addNode2)
cell.add_block(block)
return n1
def create_mlp_node(node):
node.add_op(Identity())
node.add_op(Dense(100, tf.nn.relu))
node.add_op(Dense(100, tf.nn.tanh))
node.add_op(Dense(100, tf.nn.sigmoid))
node.add_op(Dropout(0.3))
node.add_op(Dense(500, tf.nn.relu))
node.add_op(Dense(500, tf.nn.tanh))
node.add_op(Dense(500, tf.nn.sigmoid))
node.add_op(Dropout(0.4))
node.add_op(Dense(1000, tf.nn.relu))
node.add_op(Dense(1000, tf.nn.tanh))
node.add_op(Dense(1000, tf.nn.sigmoid))
node.add_op(Dropout(0.5))
def create_cell_mlp(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(name='N1')
cell.graph.add_edge(input_nodes[0], n1) # fixed input connection
n1.set_op(op=Flatten())
n2 = VariableNode('N2')
n2.add_op(Identity())
n2.add_op(Dense(units=10))
n2.add_op(Dense(units=50))
n2.add_op(Dense(units=100))
n2.add_op(Dense(units=200))
n2.add_op(Dense(units=250))
n2.add_op(Dense(units=500))
n2.add_op(Dense(units=750))
n2.add_op(Dense(units=1000))
n3 = VariableNode('N3')
n3.add_op(Identity())
n3.add_op(Activation(activation='relu'))
n3.add_op(Activation(activation='tanh'))
n3.add_op(Activation(activation='sigmoid'))
n4 = VariableNode('N4')
n4.add_op(Identity())
n4.add_op(Dropout(rate=0.5))
n4.add_op(Dropout(rate=0.4))
n4.add_op(Dropout(rate=0.3))
n4.add_op(Dropout(rate=0.2))
n4.add_op(Dropout(rate=0.1))
n4.add_op(Dropout(rate=0.05))
block = Block()
block.add_node(n1)
block.add_node(n2)
block.add_node(n3)
block.add_node(n4)
block.add_edge(n1, n2)
block.add_edge(n2, n3)
block.add_edge(n3, n4)
cell.add_block(block)
cell.set_outputs()
return cell
def create_mlp_block(cell, input_node):
# first node of block
n1 = ConstantNode(op=Dense(1000, tf.nn.relu), name='N1')
cell.graph.add_edge(input_node, n1) # fixed input of current block
# second node of block
n2 = ConstantNode(op=Dense(1000, tf.nn.relu), name='N2')
n3 = ConstantNode(op=Dense(1000, tf.nn.relu), name='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_block():
# first node of block
n1 = VariableNode('N1')
for inpt in input_nodes:
n1.add_op(Connect(cell.graph, inpt, n1))
# second node of block
mlp_op_list = list()
mlp_op_list.append(Identity())
mlp_op_list.append(Dense(5, tf.nn.relu))
mlp_op_list.append(Dense(5, tf.nn.tanh))
mlp_op_list.append(Dense(10, tf.nn.relu))
mlp_op_list.append(Dense(10, tf.nn.tanh))
mlp_op_list.append(Dense(20, tf.nn.relu))
mlp_op_list.append(Dense(20, tf.nn.tanh))
n2 = VariableNode('N2')
for op in mlp_op_list:
n2.add_op(op)
# third node of block
n3 = VariableNode('N3')
for op in dropout_ops:
n3.add_op(op)
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_dense_cell_type2(input_nodes):
"""MLP type 2
Args:
input_nodes (list(Node)): possible inputs of the current cell.
Returns:
Cell: a Cell instance.
"""
cell = Cell(input_nodes)
# first node of block
n1 = VariableNode('N_0')
for inpt in input_nodes:
n1.add_op(Connect(cell.graph, inpt, n1))
# second node of block
mlp_op_list = list()
mlp_op_list.append(Identity())
mlp_op_list.append(Dense(5, tf.nn.relu))
mlp_op_list.append(Dense(10, tf.nn.relu))
mlp_op_list.append(Dense(20, tf.nn.relu))
mlp_op_list.append(Dense(40, tf.nn.relu))
mlp_op_list.append(Dense(80, tf.nn.relu))
mlp_op_list.append(Dense(160, tf.nn.relu))
mlp_op_list.append(Dense(320, tf.nn.relu))
n2 = VariableNode('N_1')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = VariableNode('N_2')
drop_ops = []
drop_ops.extend(dropout_ops)
for op in drop_ops:
n3.add_op(op)
# 1 Blocks
block1 = Block()
block1.add_node(n1)
block1.add_node(n2)
block1.add_node(n3)
block1.add_edge(n1, n2)
block1.add_edge(n2, n3)
cell.add_block(block1)
cell.set_outputs()
return cell
def create_mlp_node(node):
node.add_op(Dense(1000, tf.nn.relu))
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