def mlp02(mark,
memory_depth,
branch_num,
hidden_dim,
learning_rate,
activation,
identity_init=False):
# Initiate a neural net
if identity_init:
model = NeuralNet(memory_depth,
mark=mark,
bamboo_braod=True,
identity_initial=True)
else:
model = NeuralNet(memory_depth,
mark=mark,
bamboo_broad=True,
identity_initial=False)
nn = model.nn
assert isinstance(nn, Bamboo_Broad)
# Add layers
nn.add(Input([memory_depth]))
branch = nn.add_branch()
branch.add(Linear(output_dim=hidden_dim))
branch.add(Activation(activation))
branch.add(Linear(output_dim=1))
for _ in range(branch_num - 1):
branch = nn.add_branch()
branch.add(
Linear(output_dim=hidden_dim,
weight_initializer=tf.zeros_initializer(),
bias_initializer=tf.zeros_initializer()))
branch.add(Activation(activation))
branch.add(
Linear(output_dim=1,
weight_initializer=tf.zeros_initializer(),
bias_initializer=tf.zeros_initializer()))
# Build model
model.default_build(learning_rate)
# Return model
return model
def mlp_res00(mark,
memory_depth,
branch_num,
hidden_dim,
learning_rate,
activation,
identity_init=True):
# Initiate a neural net
if identity_init:
model = NeuralNet(memory_depth,
mark=mark,
bamboo=True,
identity_initial=True)
else:
model = NeuralNet(memory_depth,
mark=mark,
bamboo=True,
identity_initial=False)
nn = model.nn
assert isinstance(nn, Bamboo)
# Add layers
nn.add(Input([memory_depth]))
for _ in range(branch_num):
nn.add(Linear(output_dim=hidden_dim))
nn.add(Activation(activation))
branch = nn.add_branch()
branch.add(Linear(output_dim=1))
resnet = nn.add(ResidualNet())
resnet.add(Linear(output_dim=hidden_dim))
resnet.add(Activation(activation))
resnet.add(Linear(output_dim=hidden_dim))
resnet.add_shortcut()
resnet.add(Activation(activation))
nn.add(Linear(output_dim=hidden_dim))
nn.add(Activation(activation))
nn.add(Linear(output_dim=1))
# Build model
model.default_build(learning_rate)
# Return model
return model
def mlp00(mark, memory_depth, hidden_dim, learning_rate, activation):
# Initiate a neural net
model = NeuralNet(memory_depth, mark=mark)
nn = model.nn
# Add layers
nn.add(Input([memory_depth]))
nn.add(Linear(output_dim=hidden_dim))
nn.add(Activation(activation))
nn.add(Linear(output_dim=hidden_dim))
nn.add(Activation(activation))
nn.add(Linear(output_dim=hidden_dim))
nn.add(Activation(activation))
nn.add(Linear(output_dim=1))
# Build model
model.default_build(learning_rate=learning_rate)
# Return model
return model