def test_create_search_space(input_shape=(2, ), output_shape=(1, ), **kwargs):
struct = AutoKSearchSpace(input_shape, output_shape, regression=True)
vnode1 = VariableNode()
for _ in range(1, 11):
vnode1.add_op(Operation(layer=tf.keras.layers.Dense(10)))
struct.connect(struct.input_nodes[0], vnode1)
struct.set_ops([0])
struct.create_model()
def generate_block(ss,
anchor_points,
strides=1,
mime=False,
first=False,
num_filters=8):
# generate block
n1 = generate_conv_node(strides=strides,
mime=mime,
first=first,
num_filters=num_filters)
n2 = generate_conv_node(strides=strides,
mime=mime,
num_filters=num_filters)
add = ConstantNode(op=AddByPadding(ss, [n1, n2], activation=None))
if first:
source = anchor_points[-1]
ss.connect(source, n1)
if mime:
if strides > 1:
if not first:
src_node = next(cycle_reduction_nodes)
skipco1 = MimeNode(src_node, name="SkipCo1")
src_node = next(cycle_reduction_nodes)
skipco2 = MimeNode(src_node, name="SkipCo2")
else:
if not first:
src_node = next(cycle_normal_nodes)
skipco1 = MimeNode(src_node, name="SkipCo1")
src_node = next(cycle_normal_nodes)
skipco2 = MimeNode(src_node, name="SkipCo2")
else:
if not first:
skipco1 = VariableNode(name="SkipCo1")
skipco2 = VariableNode(name="SkipCo2")
if strides > 1:
if not first:
reduction_nodes.append(skipco1)
reduction_nodes.append(skipco2)
else:
if not first:
normal_nodes.append(skipco1)
normal_nodes.append(skipco2)
for anchor in anchor_points:
if not first:
skipco1.add_op(Connect(ss, anchor))
ss.connect(skipco1, n1)
skipco2.add_op(Connect(ss, anchor))
ss.connect(skipco2, n2)
return add
def test_create_more_nodes(self):
from deephyper.nas.space import AutoKSearchSpace
from deephyper.nas.space.node import VariableNode
from deephyper.nas.space.op.op1d import Dense
struct = AutoKSearchSpace((5, ), (1, ), regression=True)
vnode1 = VariableNode()
struct.connect(struct.input_nodes[0], vnode1)
vnode1.add_op(Dense(10))
vnode2 = VariableNode()
vnode2.add_op(Dense(10))
struct.connect(vnode1, vnode2)
struct.set_ops([0, 0])
falias = "test_auto_keras_search_spaceure"
struct.draw_graphviz(f"{falias}.dot")
model = struct.create_model()
from tensorflow.keras.utils import plot_model
plot_model(model, to_file=f"{falias}.png", show_shapes=True)
def build_sub_graph(self, input_, num_layers=3):
source = prev_input = input_
mirror = False
is_input = False
if type(source) is ConstantNode:
if type(source._op) is Tensor:
if "input_" in source._op.tensor.name:
is_input = True
input_name = source._op.tensor.name
input_shape = tuple(source._op.tensor.shape[1:])
if self.shapes_to_vnodes.get(input_shape) is None:
self.shapes_to_vnodes[input_shape] = []
else:
mirror = True
memory = self.shapes_to_vnodes[input_shape][::-1]
# look over skip connections within a range of the 3 previous nodes
anchor_points = collections.deque([source], maxlen=3)
for layer_i in range(num_layers):
if not(mirror):
vnode = VariableNode()
self.add_dense_to_(vnode)
if is_input:
self.shapes_to_vnodes[input_shape].append(vnode)
else:
vnode = MirrorNode(memory.pop())
self.ss.connect(prev_input, vnode)
# * Cell output
prev_node = vnode
if layer_i == num_layers-1:
return prev_node
cmerge = ConstantNode()
cmerge.set_op(Concatenate(self.ss, [prev_node]))
for anchor in anchor_points:
if not(mirror):
skipco = VariableNode()
if is_input:
self.shapes_to_vnodes[input_shape].append(skipco)
else:
skipco = MimeNode(memory.pop())
skipco.add_op(Zero())
skipco.add_op(Connect(self.ss, anchor))
self.ss.connect(skipco, cmerge)
prev_input = cmerge
# ! for next iter
anchor_points.append(prev_input)
return prev_input
def create_search_space(
input_shape=(10,), output_shape=(7,), num_layers=10, *args, **kwargs
):
arch = AutoKSearchSpace(input_shape, output_shape, regression=True)
source = prev_input = arch.input_nodes[0]
# look over skip connections within a range of the 3 previous nodes
anchor_points = collections.deque([source], maxlen=3)
for _ in range(num_layers):
vnode = VariableNode()
add_dense_to_(vnode)
arch.connect(prev_input, vnode)
# * Cell output
cell_output = vnode
cmerge = ConstantNode()
cmerge.set_op(AddByProjecting(arch, [cell_output], activation="relu"))
for anchor in anchor_points:
skipco = VariableNode()
skipco.add_op(Tensor([]))
skipco.add_op(Connect(arch, anchor))
arch.connect(skipco, cmerge)
# ! for next iter
prev_input = cmerge
anchor_points.append(prev_input)
return arch
def create_conv_lstm_search_space(
input_shape=(7, 808, 782, 1),
output_shape=(7, 808, 782, 1),
num_layers=10,
*args,
**kwargs,
):
arch = KSearchSpace(input_shape, output_shape)
source = prev_input = arch.input_nodes[0]
# look over skip connections within a range of the 3 previous nodes
anchor_points = collections.deque([source], maxlen=3)
for _ in range(num_layers):
vnode = VariableNode()
add_convlstm_to_(vnode)
arch.connect(prev_input, vnode)
# * Cell output
cell_output = vnode
cmerge = ConstantNode()
cmerge.set_op(
AddByProjecting(arch, [cell_output], activation="relu", axis=-2))
for anchor in anchor_points:
skipco = VariableNode()
skipco.add_op(Tensor([]))
skipco.add_op(Connect(arch, anchor))
arch.connect(skipco, cmerge)
# ! for next iter
prev_input = cmerge
anchor_points.append(prev_input)
# Add layer to enforce consistency
cnode = ConstantNode()
units = output_shape[-1]
add_convlstm_oplayer_(cnode, units)
arch.connect(prev_input, cnode)
return arch
def build_sub_graph(self, input_, num_layers=3):
source = prev_input = input_
# look over skip connections within a range of the 3 previous nodes
anchor_points = collections.deque([source], maxlen=3)
for _ in range(num_layers):
vnode = VariableNode()
self.add_dense_to_(vnode)
self.ss.connect(prev_input, vnode)
# * Cell output
cell_output = vnode
cmerge = ConstantNode()
cmerge.set_op(
AddByProjecting(self.ss, [cell_output], activation="relu"))
for anchor in anchor_points:
skipco = VariableNode()
skipco.add_op(Zero())
skipco.add_op(Connect(self.ss, anchor))
self.ss.connect(skipco, cmerge)
prev_input = cmerge
# ! for next iter
anchor_points.append(prev_input)
return prev_input
def test_mirror_node():
vnode = VariableNode()
vop = Dense(10)
vnode.add_op(vop)
vnode.add_op(Dense(20))
mnode = MirrorNode(vnode)
vnode.set_op(0)
assert vnode.op == vop
assert mnode.op == vop
def test_create_multiple_inputs_with_one_vnode(self):
from deephyper.nas.space import KSearchSpace
from deephyper.nas.space.node import VariableNode, ConstantNode
from deephyper.nas.space.op.op1d import Dense
from deephyper.nas.space.op.merge import Concatenate
struct = KSearchSpace([(5, ), (5, )], (1, ))
merge = ConstantNode()
merge.set_op(Concatenate(struct, struct.input_nodes))
vnode1 = VariableNode()
struct.connect(merge, vnode1)
vnode1.add_op(Dense(1))
struct.set_ops([0])
struct.create_model()
def test_create_one_vnode_with_wrong_output_shape(self):
from deephyper.nas.space import KSearchSpace
struct = KSearchSpace((5, ), (1, ))
from deephyper.nas.space.node import VariableNode
vnode = VariableNode()
struct.connect(struct.input_nodes[0], vnode)
from deephyper.nas.space.op.op1d import Dense
vnode.add_op(Dense(10))
struct.set_ops([0])
with pytest.raises(WrongOutputShape):
struct.create_model()
def create_structure(input_shape=[(1, ), (942, ), (5270, ), (2048, )], output_shape=(1,), num_cells=2, *args, **kwargs):
struct = AutoKSearchSpace(input_shape, output_shape, regression=True)
input_nodes = struct.input_nodes
output_submodels = [input_nodes[0]]
for i in range(1, 4):
vnode1 = VariableNode('N1')
add_mlp_op_(vnode1)
struct.connect(input_nodes[i], vnode1)
vnode2 = VariableNode('N2')
add_mlp_op_(vnode2)
struct.connect(vnode1, vnode2)
vnode3 = VariableNode('N3')
add_mlp_op_(vnode3)
struct.connect(vnode2, vnode3)
output_submodels.append(vnode3)
merge1 = ConstantNode(name='Merge', op=Concatenate(struct, output_submodels))
# merge1.set_op(Concatenate(struct, merge1, output_submodels))
vnode4 = VariableNode('N4')
add_mlp_op_(vnode4)
struct.connect(merge1, vnode4)
prev = vnode4
for i in range(num_cells):
vnode = VariableNode(f'N{i+1}')
add_mlp_op_(vnode)
struct.connect(prev, vnode)
merge = ConstantNode(name='Merge', op=AddByPadding(struct, [vnode, prev]))
# merge.set_op()
prev = merge
return struct
def generate_conv_node(strides, mime=False):
if mime:
if strides > 1:
node = MimeNode(next(cycle_reduction_nodes), name="Conv")
else:
node = MimeNode(next(cycle_normal_nodes), name="Conv")
else:
node = VariableNode(name="Conv")
if strides > 1:
reduction_nodes.append(node)
else:
normal_nodes.append(node)
padding = "valid" if strides > 1 else "same"
node.add_op(Identity())
node.add_op(
Conv2D(filters=8, kernel_size=(1, 1), strides=strides,
padding=padding))
node.add_op(
Conv2D(filters=8, kernel_size=(3, 3), strides=strides,
padding=padding))
node.add_op(
Conv2D(filters=8, kernel_size=(5, 5), strides=strides,
padding=padding))
node.add_op(AvgPool2D(pool_size=(3, 3), strides=strides, padding=padding))
node.add_op(MaxPool2D(pool_size=(3, 3), strides=strides, padding=padding))
node.add_op(MaxPool2D(pool_size=(5, 5), strides=strides, padding=padding))
node.add_op(MaxPool2D(pool_size=(7, 7), strides=strides, padding=padding))
node.add_op(
SeparableConv2D(kernel_size=(3, 3),
filters=8,
strides=strides,
padding=padding))
node.add_op(
SeparableConv2D(kernel_size=(5, 5),
filters=8,
strides=strides,
padding=padding))
node.add_op(
SeparableConv2D(kernel_size=(7, 7),
filters=8,
strides=strides,
padding=padding))
if strides == 1:
node.add_op(
Conv2D(
filters=8,
kernel_size=(3, 3),
strides=strides,
padding=padding,
dilation_rate=2,
))
return node
def build(
self,
input_shape,
output_shape,
regression=True,
num_units=(1, 11),
num_layers=10,
**kwargs
):
"""
Args:
input_shape (tuple, optional): True shape of inputs (no batch size dimension). Defaults to (2,).
output_shape (tuple, optional): True shape of outputs (no batch size dimension).. Defaults to (1,).
num_layers (int, optional): Maximum number of layers to have. Defaults to 10.
num_units (tuple, optional): Range of number of units such as range(start, end, step_size). Defaults to (1, 11).
regression (bool, optional): A boolean defining if the model is a regressor or a classifier. Defaults to True.
Returns:
AutoKSearchSpace: A search space object based on tf.keras implementations.
"""
ss = AutoKSearchSpace(input_shape, output_shape, regression=regression)
prev_node = ss.input_nodes[0]
for _ in range(num_layers):
vnode = VariableNode()
vnode.add_op(Identity())
for i in range(*num_units):
vnode.add_op(Dense(i, tf.nn.relu))
ss.connect(prev_node, vnode)
prev_node = vnode
return ss
def create_search_space(
input_shape=(20, ), output_shape=(20, ), num_layers=5, *args,
**kwargs):
vocab_size = 10000
ss = KSearchSpace(input_shape, (*output_shape, vocab_size))
source = ss.input_nodes[0]
emb = VariableNode()
add_embedding_(emb, vocab_size)
ss.connect(source, emb)
timestep_dropout = prev_input = ConstantNode(op=TimestepDropout(rate=0.1))
ss.connect(emb, timestep_dropout)
# look over skip connections within a range of the 2 previous nodes
anchor_points = collections.deque([timestep_dropout], maxlen=3)
for _ in range(num_layers):
vnode = VariableNode()
add_lstm_seq_(vnode)
ss.connect(prev_input, vnode)
# * Cell output
cell_output = vnode
cmerge = ConstantNode()
cmerge.set_op(AddByProjecting(ss, [cell_output], activation="relu"))
for anchor in anchor_points:
skipco = VariableNode()
skipco.add_op(Zero())
skipco.add_op(Connect(ss, anchor))
ss.connect(skipco, cmerge)
# ! for next iter
prev_input = cmerge
anchor_points.append(prev_input)
# out = ConstantNode(
# op=tf.keras.layers.TimeDistributed(
# tf.keras.layers.Dense(units=vocab_size, activation="softmax")
# )
# )
out = ConstantNode(
op=tf.keras.layers.Dense(units=vocab_size, activation="softmax"))
ss.connect(prev_input, out)
return ss
def test_mime_node():
vnode = VariableNode()
vop = Dense(10)
vnode.add_op(vop)
vnode.add_op(Dense(20))
mnode = MimeNode(vnode)
mop = Dense(30)
mnode.add_op(mop)
mnode.add_op(Dense(40))
vnode.set_op(0)
assert vnode.op == vop
assert mnode.op == mop
def test_create_one_vnode(self):
from deephyper.nas.space import KSearchSpace
struct = KSearchSpace((5, ), (1, ))
from deephyper.nas.space.node import VariableNode
vnode = VariableNode()
struct.connect(struct.input_nodes[0], vnode)
from deephyper.nas.space.op.op1d import Dense
vnode.add_op(Dense(1))
struct.set_ops([0])
falias = "test_keras_search_spaceure"
struct.draw_graphviz(f"{falias}.dot")
model = struct.create_model()
from tensorflow.keras.utils import plot_model
plot_model(model, to_file=f"{falias}.png", show_shapes=True)
def create_structure(input_shape=[(1, ), (942, ), (5270, ), (2048, )],
output_shape=(1, ),
num_cells=2,
*args,
**kwargs):
struct = AutoKSearchSpace(input_shape, output_shape, regression=True)
input_nodes = struct.input_nodes
output_submodels = [input_nodes[0]]
for i in range(1, 4):
cnode1 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(input_nodes[i], cnode1)
cnode2 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(cnode1, cnode2)
vnode1 = VariableNode(name='N3')
add_mlp_op_(vnode1)
struct.connect(cnode2, vnode1)
output_submodels.append(vnode1)
merge1 = ConstantNode(name='Merge')
# merge1.set_op(Concatenate(struct, merge1, output_submodels))
merge1.set_op(Concatenate(struct, output_submodels))
cnode4 = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(merge1, cnode4)
prev = cnode4
for i in range(num_cells):
cnode = ConstantNode(name='N', op=Dense(1000, tf.nn.relu))
struct.connect(prev, cnode)
merge = ConstantNode(name='Merge')
# merge.set_op(AddByPadding(struct, merge, [cnode, prev]))
merge.set_op(AddByPadding(struct, [cnode, prev]))
prev = merge
return struct
def build(
self,
input_shape,
output_shape,
regression=True,
num_layers=10,
dropout=0.0,
**kwargs,
):
ss = AutoKSearchSpace(input_shape, output_shape, regression=regression)
source = prev_input = ss.input_nodes[0]
# look over skip connections within a range of the 3 previous nodes
anchor_points = collections.deque([source], maxlen=3)
for _ in range(num_layers):
vnode = VariableNode()
self.add_dense_to_(vnode)
ss.connect(prev_input, vnode)
# * Cell output
cell_output = vnode
cmerge = ConstantNode()
cmerge.set_op(AddByProjecting(ss, [cell_output],
activation="relu"))
for anchor in anchor_points:
skipco = VariableNode()
skipco.add_op(Zero())
skipco.add_op(Connect(ss, anchor))
ss.connect(skipco, cmerge)
prev_input = cmerge
# ! for next iter
anchor_points.append(prev_input)
if dropout >= 0.0:
dropout_node = ConstantNode(op=Dropout(rate=dropout))
ss.connect(prev_input, dropout_node)
return ss
def create_search_space(
input_shape=(32, 32, 3),
output_shape=(10, ),
num_filters=8,
num_blocks=4,
normal_cells=2,
reduction_cells=1,
repetitions=3,
*args,
**kwargs,
):
ss = AutoKSearchSpace(input_shape, output_shape, regression=False)
source = prev_input = ss.input_nodes[0]
# look over skip connections within a range of the 3 previous nodes
hidden_states = collections.deque([source, source], maxlen=2)
for ri in range(repetitions):
for nci in range(normal_cells):
# generate a normal cell
cout = generate_cell(
ss,
hidden_states,
num_blocks,
strides=1,
mime=ri + nci > 0,
num_filters=num_filters,
)
hidden_states.append(cout)
if ri < repetitions - 1: # we don't want the last cell to be a reduction cell
for rci in range(reduction_cells):
# generate a reduction cell
cout = generate_cell(
ss,
hidden_states,
num_blocks,
strides=2,
mime=ri + rci > 0,
num_filters=num_filters,
)
hidden_states.append(cout)
# out_node = ConstantNode(op=Dense(100, activation=tf.nn.relu))
out_dense = VariableNode()
out_dense.add_op(Identity())
for units in [10, 20, 50, 100, 200, 500, 1000]:
out_dense.add_op(Dense(units, activation=tf.nn.relu))
ss.connect(cout, out_dense)
out_dropout = VariableNode()
out_dropout.add_op(Identity())
for drop_rate in [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8]:
out_dropout.add_op(Dropout(rate=drop_rate))
ss.connect(out_dense, out_dropout)
return ss
def create_structure(input_shape=(2, ), output_shape=(1, ), *args, **kwargs):
struct = AutoKSearchSpace(input_shape, output_shape, regression=False)
n1 = VariableNode('N')
add_conv_op_(n1)
struct.connect(struct.input_nodes[0], n1)
n2 = VariableNode('N')
add_activation_op_(n2)
struct.connect(n1, n2)
n3 = VariableNode('N')
add_pooling_op_(n3)
struct.connect(n2, n3)
n4 = VariableNode('N')
add_conv_op_(n4)
struct.connect(n3, n4)
n5 = VariableNode('N')
add_activation_op_(n5)
struct.connect(n4, n5)
n6 = VariableNode('N')
add_pooling_op_(n6)
struct.connect(n5, n6)
n7 = ConstantNode(op=Flatten(), name='N')
struct.connect(n6, n7)
n8 = VariableNode('N')
add_dense_op_(n8)
struct.connect(n7, n8)
n9 = VariableNode('N')
add_activation_op_(n9)
struct.connect(n8, n9)
n10 = VariableNode('N')
add_dropout_op_(n10)
struct.connect(n9, n10)
n11 = VariableNode('N')
add_dense_op_(n11)
struct.connect(n10, n11)
n12 = VariableNode('N')
add_activation_op_(n12)
struct.connect(n11, n12)
n13 = VariableNode('N')
add_dropout_op_(n13)
struct.connect(n12, n13)
return struct
def gen_vnode(self) -> VariableNode:
vnode = VariableNode()
for i in range(1, 11):
vnode.add_op(Dense(i, tf.nn.relu))
return vnode
def generate_conv_node(strides, mime=False, first=False, num_filters=8):
if mime:
if strides > 1:
node = MimeNode(next(cycle_reduction_nodes), name="Conv")
else:
node = MimeNode(next(cycle_normal_nodes), name="Conv")
else:
node = VariableNode(name="Conv")
if strides > 1:
reduction_nodes.append(node)
else:
normal_nodes.append(node)
padding = "same"
if first:
node.add_op(Identity())
else:
node.add_op(Zero())
node.add_op(Identity())
node.add_op(
Conv2D(
filters=num_filters,
kernel_size=(3, 3),
strides=strides,
padding=padding,
activation=tf.nn.relu,
))
node.add_op(
Conv2D(
filters=num_filters,
kernel_size=(5, 5),
strides=strides,
padding=padding,
activation=tf.nn.relu,
))
node.add_op(AvgPool2D(pool_size=(3, 3), strides=strides, padding=padding))
node.add_op(MaxPool2D(pool_size=(3, 3), strides=strides, padding=padding))
node.add_op(
SeparableConv2D(kernel_size=(3, 3),
filters=num_filters,
strides=strides,
padding=padding))
node.add_op(
SeparableConv2D(kernel_size=(5, 5),
filters=num_filters,
strides=strides,
padding=padding))
if strides == 1:
node.add_op(
Conv2D(
filters=num_filters,
kernel_size=(3, 3),
strides=strides,
padding=padding,
dilation_rate=2,
))
node.add_op(
Conv2D(
filters=num_filters,
kernel_size=(5, 5),
strides=strides,
padding=padding,
dilation_rate=2,
))
return node
def build(self,
input_shape,
output_shape,
units=[128, 64, 32, 16, 8, 16, 32, 64, 128],
num_layers=5,
**kwargs):
ss = KSearchSpace(input_shape, output_shape)
inp = ss.input_nodes[0]
# auto-encoder
units = [128, 64, 32, 16, 8, 16, 32, 64, 128]
prev_node = inp
d = 1
for i in range(len(units)):
vnode = VariableNode()
vnode.add_op(Identity())
if d == 1 and units[i] < units[i + 1]:
d = -1
for u in range(min(2, units[i]), max(2, units[i]) + 1, 2):
vnode.add_op(Dense(u, tf.nn.relu))
latente_space = vnode
else:
for u in range(min(units[i], units[i + d]),
max(units[i], units[i + d]) + 1, 2):
vnode.add_op(Dense(u, tf.nn.relu))
ss.connect(prev_node, vnode)
prev_node = vnode
out2 = ConstantNode(op=Dense(output_shape[0][0], name="output_0"))
ss.connect(prev_node, out2)
# regressor
prev_node = latente_space
# prev_node = inp
for _ in range(num_layers):
vnode = VariableNode()
for i in range(16, 129, 16):
vnode.add_op(Dense(i, tf.nn.relu))
ss.connect(prev_node, vnode)
prev_node = vnode
out1 = ConstantNode(op=Dense(output_shape[1][0], name="output_1"))
ss.connect(prev_node, out1)
return ss