def two_conv_graph():
G = NNGraph(name='two_conv_graph')
ti = G.add_input(Dim.unnamed([10, 10, 2]))
c1filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c1filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n1 = Conv2DParameters("node1",
filt=c1filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n1)
w1 = [[0.25, 0.25], [0.25, 0.25], [0.25, 0.25]]
w1 = [w1, w1, w1]
w2 = [[0.75, 0.75], [0.75, 0.75], [0.75, 0.75]]
w2 = [w2, w2, w2]
n1.weights = np.array([w1, w2])
c2filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c2filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n2 = Conv2DParameters("node2",
filt=c2filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n2)
w3 = [[0.75, 0.25], [0.75, 0.25], [0.75, 0.25]]
w3 = [w3, w3, w3]
n2.weights = np.array([w3, w3])
to = G.add_output()
G.add_edge(NNEdge(ti, n1))
G.add_edge(NNEdge(n1, n2))
G.add_edge(NNEdge(n2, to))
G.add_dimensions()
yield G
def actfusion_graph():
G = NNGraph(name='actfusion_graph')
ti1 = G.add_input(Dim.unnamed([10, 10, 2])).name
ti2 = G.add_input(Dim.unnamed([10, 10, 2])).name
c1filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c1filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n1 = Conv2DParameters("node1",
filt=c1filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n1)
w1 = [[0.25, 0.25], [0.25, 0.25], [0.25, 0.25]]
w1 = [w1, w1, w1]
w2 = [[0.75, 0.75], [0.75, 0.75], [0.75, 0.75]]
w2 = [w2, w2, w2]
n1.weights = np.array([w1, w2])
n1a = ReluActivationParameters("node1a")
G.add_node(n1a)
c2filt = Conv2DFilterDim(3, 3, 2, in_c=2)
c2filt.impose_order(['out_c', 'h', 'w', 'in_c'])
n2 = Conv2DParameters("node2",
filt=c2filt,
stride=StrideDim(1, 1),
padding=PadDim(0),
in_dims_hint=SparseList([['h', 'w', 'c']]),
out_dims_hint=SparseList([['h', 'w', 'c']]))
G.add_node(n2)
w3 = [[0.75, 0.25], [0.75, 0.25], [0.75, 0.25]]
w3 = [w3, w3, w3]
n2.weights = np.array([w3, w3])
n3 = MatrixAddParameters("node3")
G.add_node(n3)
n4 = ReluActivationParameters("node4")
G.add_node(n4)
to = G.add_output()
G.add_edge(NNEdge(ti1, n1))
G.add_edge(NNEdge(n1, n1a))
G.add_edge(NNEdge(ti2, n2))
G.add_edge(NNEdge(n1a, n3, to_idx=0))
G.add_edge(NNEdge(n2, n3, to_idx=1))
G.add_edge(NNEdge(n3, n4))
G.add_edge(NNEdge(n4, to))
G.add_dimensions()
yield G