def main():
# Two sources and single distination
v_in = Vertex("x") # one source
v1 = Edge(name="e0")(v_in)
v2 = Edge(name="e1")(v1)
v3 = Edge(name="e2")(v2, v1) # fork and concate
v4 = Edge(name="e3")(v3)
v5 = Edge(name="e4")(v4)
y = Vertex("y") # second source
y.value = np.random.rand(4, 3)
v_out = SquareLoss(name="square-loss")(v5, y) # single distination
print "----- Vertices and Edges in Graph -----"
print len(cg.vertices)
print len(cg.edges)
print "----- Forward pass (Inference) -----"
inputs = np.random.rand(4, 3)
v_in.forward(inputs)
labels = np.random.rand(4, 3)
y.forward(labels)
print v1.value
print v5.value
print "----- Compute Loss -----"
v_out.backward(1)
print v1.grad
def main():
# Two sources and single distination
v_in = Vertex("x") # one source
v1_w = Vertex("w1", value=np.random.rand(10, 5))
v1 = Affine(name="e1")(v1_w, v_in)
v2_w = Vertex("w2", value=np.random.rand(5, 1))
v2 = Affine(name="e2")(v2_w, v1)
y = Vertex("y") # second source
v_out = SquareLoss(name="square-loss")(v2, y) # single distination
print "----- Vertices and Edges in Graph -----"
print len(cg.vertices)
print len(cg.edges)
print "----- Forward pass (Inference) -----"
inputs = np.random.rand(100, 10)
v_in.forward(inputs)
v1_w.forward()
v2_w.forward()
labels = np.random.rand(100, 1)
y.forward(labels)
print v2.value
print "----- Compute Loss -----"
v_out.backward(1)
v2_w.grad
def main():
# Fork and concate
v_in = Vertex("x") # single source
v1 = Edge(name="e0")(v_in)
v2 = Edge(name="e1")(v1)
v3 = Edge(name="e2")(v2, v1) # fork and concate
v4 = Edge(name="e3")(v3)
v5 = Edge(name="e4")(v4)
print "----- Vertices and Edges in Graph -----"
print len(cg.vertices)
print len(cg.edges)
print "----- Forward pass (Inference) -----"
inputs = np.random.rand(4, 3)
v_in.forward(inputs)
print v1.value
print v5.value
print "----- Backward pass (from the middle) -----"
grad = np.random.rand(4, 3)
v3.backward(grad)
print v1.grad
def main():
# Two sources and one distination
v1_in = Vertex("x1") # one source
v1_1 = Edge(name="e1_0")(v1_in)
v1_2 = Edge(name="e1_1")(v1_1)
v1_3 = Edge(name="e1_2")(v1_2)
v1_4 = Edge(name="e1_3")(v1_3)
v1_5 = Edge(name="e1_4")(v1_4)
y1 = Vertex("y1") # second source
y1.value = np.random.rand(4, 3)
v1_out = SquareLoss(name="square-loss")(v1_5, y1) # one distination
# Two sources and one distination
v2_in = Vertex("x2") # one source
v2_1 = Edge(name="e2_0")(v2_in)
v2_2 = Edge(name="e2_1")(v2_1)
v2_3 = Edge(name="e2_2")(v2_2)
v2_4 = Edge(name="e2_3")(v2_3)
v2_5 = Edge(name="e2_4")(v2_4)
y2 = Vertex("y2") # second source
y2.value = np.random.rand(4, 3)
v2_out = SquareLoss(name="square-loss")(v2_5, y2) # one distination
# Objective function
v_obj = v1_out + v2_out
print "----- Vertices and Edges in Graph -----"
print len(cg.vertices)
print len(cg.edges)
print "----- Forward pass (Inference) -----"
inputs1 = np.random.rand(4, 3)
inputs2 = np.random.rand(4, 3)
v1_in.forward(inputs1), v2_in.forward(inputs2)
labels1 = np.random.rand(4, 3)
labels2 = np.random.rand(4, 3)
y1.forward(labels1), y2.forward(labels2)
print "----- Compute Loss -----"
v_obj.backward()
print v1_1.grad
def main():
# Additon
print "# Additon"
v = np.asarray(np.arange(1, 10)).reshape(3, 3).astype(np.float32)
v0 = Vertex(name="", value=v)
v1 = Vertex(name="", value=v)
v2 = v0 + v1
v0.forward(), v1.forward()
print v2.value
v2.backward()
print v0.grad
print v1.grad
print ""
# Subtraction
print "# Subtraction"
v = np.asarray(np.arange(1, 10)).reshape(3, 3).astype(np.float32)
v0 = Vertex(name="", value=v)
v1 = Vertex(name="", value=v)
v2 = v0 - v1
v0.forward(), v1.forward()
print v2.value
v2.backward()
print v0.grad
print v1.grad
print ""
# Multiplication
print "# Multiplication"
v = np.asarray(np.arange(1, 10)).reshape(3, 3).astype(np.float32)
v0 = Vertex(name="", value=v)
v1 = Vertex(name="", value=v)
v2 = v0 * v1
v0.forward(), v1.forward()
print v2.value
v2.backward()
print v0.grad
print v1.grad
print ""
# Division
print "# Division"
v = np.asarray(np.arange(1, 10)).reshape(3, 3).astype(np.float32)
v0 = Vertex(name="", value=v)
v1 = Vertex(name="", value=v)
v2 = v0 / v1
v0.forward(), v1.forward()
print v2.value
v2.backward()
print v0.grad
print v1.grad
print ""
