How to use the deephyper.search.nas.operation.mlp.MLP function in deephyper
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deephyper / deephyper / deephyper / search / nas / model / baseline / mlp.py View on Github 
def create_block():
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 5, tf.nn.tanh))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.tanh))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.tanh))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third node of block
n3 = Node('N3')
for op in mlp_op_list:
n3.add_op(op)
# fourth node of block
n4 = Node('N4')
for op in mlp_op_list:
n4.add_op(op)
# fifthcell = Cell(input_nodes)
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = Node('N3')
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)def create_dense_cell_toy(input_nodes):
cell = Cell(input_nodes)
n1 = Node('N1')
n1.add_op(Connect(cell.graph, input_nodes[-1], n1))
# first node of block
mlp_op_list = list()
mlp_op_list.append(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# 1 Blocks
block1 = Block()
block1.add_node(n1)
block1.add_node(n2)
block1.add_edge(n1, n2)
cell.add_block(block1)def create_block():
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 5, tf.nn.tanh))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.tanh))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.tanh))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third node of block
n3 = Node('N3')
for op in mlp_op_list:
n3.add_op(op)
# fourth node of block
n4 = Node('N4')
for op in mlp_op_list:
n4.add_op(op)Returns:
Cell: a Cell instance.
"""
cell = Cell(input_nodes)
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = Node('N3')
drop_ops = []
drop_ops.extend(dropout_ops)
for op in drop_ops:
n3.add_op(op)
def create_block():
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 5, tf.nn.tanh))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.tanh))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.tanh))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third node of block
n3 = Node('N3')
for op in mlp_op_list:
n3.add_op(op)
# fourth node of block
n4 = Node('N4')
for op in mlp_op_list:
n4.add_op(op)Cell: a Cell instance.
"""
cell = Cell(input_nodes)
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = Node('N3')
drop_ops = []
drop_ops.extend(dropout_ops)
for op in drop_ops:
n3.add_op(op)
# 1 Blocks
block1 = Block()def create_dense_cell_toy(input_nodes):
cell = Cell(input_nodes)
n1 = Node('N1')
n1.add_op(Connect(cell.graph, input_nodes[-1], n1))
# first node of block
mlp_op_list = list()
mlp_op_list.append(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# 1 Blocks
block1 = Block()
block1.add_node(n1)
block1.add_node(n2)
block1.add_edge(n1, n2)
cell.add_block(block1)
cell.set_outputs('stack', axis=1)
return cell"""
cell = Cell(input_nodes)
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = Node('N3')
drop_ops = []
drop_ops.extend(dropout_ops)
for op in drop_ops:
n3.add_op(op)
# 1 Blocks
block1 = Block()
block1.add_node(n1)input_nodes (list(Node)): possible inputs of the current cell.
Returns:
Cell: a Cell instance.
"""
cell = Cell(input_nodes)
# first node of block
n1 = Node('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(MLP(1, 5, tf.nn.relu))
mlp_op_list.append(MLP(1, 10, tf.nn.relu))
mlp_op_list.append(MLP(1, 20, tf.nn.relu))
mlp_op_list.append(MLP(1, 40, tf.nn.relu))
mlp_op_list.append(MLP(1, 80, tf.nn.relu))
mlp_op_list.append(MLP(1, 160, tf.nn.relu))
mlp_op_list.append(MLP(1, 320, tf.nn.relu))
n2 = Node('N2')
for op in mlp_op_list:
n2.add_op(op)
# third
n3 = Node('N3')
drop_ops = []
drop_ops.extend(dropout_ops)
for op in drop_ops:
n3.add_op(op)deephyper
Scalable asynchronous neural architecture and hyperparameter search for deep neural networks.
Package Health Score
70 / 100Popular deephyper functions
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