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(0.05))
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(0.1))
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(0.2))
def add_mlp_op_(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 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))
def create_mlp_node(name):
n = VariableNode(name)
n.add_op(Identity())
n.add_op(Dense(100, tf.nn.relu))
n.add_op(Dense(100, tf.nn.tanh))
n.add_op(Dense(100, tf.nn.sigmoid))
n.add_op(Dropout(0.05))
n.add_op(Dense(500, tf.nn.relu))
n.add_op(Dense(500, tf.nn.tanh))
n.add_op(Dense(500, tf.nn.sigmoid))
n.add_op(Dropout(0.1))
n.add_op(Dense(1000, tf.nn.relu))
n.add_op(Dense(1000, tf.nn.tanh))
n.add_op(Dense(1000, tf.nn.sigmoid))
n.add_op(Dropout(0.2))
return n
def create_structure(input_shape=(2, ), output_shape=(1, ), *args, **kwargs):
struct = AutoOutputStructure(input_shape, output_shape, regression=False)
n1 = ConstantNode(op=Conv1D(filter_size=20, num_filters=128), name='N')
struct.connect(struct.input_nodes[0], n1)
n2 = ConstantNode(op=Activation(activation='relu'), name='N')
struct.connect(n1, n2)
n3 = ConstantNode(op=MaxPooling1D(pool_size=1, padding='same'), name='N')
struct.connect(n2, n3)
n4 = ConstantNode(op=Conv1D(filter_size=10, num_filters=128), name='N')
struct.connect(n3, n4)
n5 = ConstantNode(op=Activation(activation='relu'), name='N')
struct.connect(n4, n5)
n6 = ConstantNode(op=MaxPooling1D(pool_size=10, padding='same'), name='N')
struct.connect(n5, n6)
n7 = ConstantNode(op=Flatten(), name='N')
struct.connect(n6, n7)
n8 = ConstantNode(op=Dense(units=200), name='N')
struct.connect(n7, n8)
n9 = ConstantNode(op=Activation(activation='relu'), name='N')
struct.connect(n8, n9)
n10 = ConstantNode(op=Dropout(rate=0.1), name='N')
struct.connect(n9, n10)
n11 = ConstantNode(op=Dense(units=20), name='N')
struct.connect(n10, n11)
n12 = ConstantNode(op=Activation(activation='relu'), name='N')
struct.connect(n11, n12)
n13 = ConstantNode(op=Dropout(rate=0.1), name='N')
struct.connect(n12, n13)
return struct
def add_dropout_op_(node):
node.add_op(Identity())
node.add_op(Dropout(rate=0.5))
node.add_op(Dropout(rate=0.4))
node.add_op(Dropout(rate=0.3))
node.add_op(Dropout(rate=0.2))
node.add_op(Dropout(rate=0.1))
node.add_op(Dropout(rate=0.05))