def net1():
l0 = plonny.Input((32, 32, 1))
la1 = plonny.Conv2D([l0], (8, 8, 40), (3, 3))
la2 = plonny.Custom((8, 8, 40), "Text\nembeds", [la1])
l1 = plonny.Conv2D([l0], (32, 32, 40), (3, 3))
l2 = plonny.Pool((16, 16, 40), [l1])
l3 = plonny.Conv2D([l2], (16, 16, 40), (3, 3))
l4 = plonny.Pool((8, 8, 40), [l3])
l5 = plonny.Conv2D([l4, la2], (8, 8, 40), (3, 3))
l6 = plonny.Upsample((32, 32, 40), [l5])
lg = layergrid.LayerGrid()
lg.set(0, 0, l0)
lg.set(1, 0, l0)
lg.set(0, 1, l1)
lg.set(0, 2, l2)
lg.set(0, 3, l3)
lg.set(0, 4, l4)
lg.set(0, 5, l5)
lg.set(1, 5, l5)
lg.set(1, 2, la1)
lg.set(1, 3, la2)
lg.set(0, 6, l6)
lg.set(1, 6, l6)
# print(lg.get_row(0))
print(lg)
graph.Graph(lg).graphshow()
def net3():
l0 = plonny.Input(
(64, 64, 3),
img="./tmp/rois/h15m34s15_class=BaseTech_p=1_used=False.jpg")
l1 = plonny.Conv2D([l0], (64, 64, 64), (5, 5))
l2 = plonny.Pool((32, 32, 64), [l1])
l3 = plonny.Conv2D([l2], (32, 32, 128), (5, 5))
l4 = plonny.Pool((16, 16, 64), [l3])
# l5 = plonny.Reshape((1, 32768), [l3])
l5 = plonny.Fluid((1, 92), "Reshape", 32768,
[l4]) #shape, name, actual_size=5, layers=None
l6 = plonny.FullyConnected([l5], 2048)
# l6 = plonny.Dropout((1, 2048), [l5])
l7 = plonny.Dropout([l6], 2048)
l8 = plonny.FullyConnected([l7], 5)
lg = layergrid.LayerGrid()
lg.set(0, 0, l0)
lg.set(0, 1, l1)
lg.set(0, 2, l2)
lg.set(0, 3, l3)
lg.set(0, 4, l4)
# lg.set(1,1,l0)
lg.set(0, 5, l5)
lg.set(0, 6, l6)
lg.set(0, 7, l7)
# lg.set(0,6,l8)
lg.set(0, 8, l8)
print(lg)
graph.Graph(lg).graphshow("Box classifier")
def net2():
l0 = plonny.Input((400, 600, 1))
l1 = plonny.Conv2D([l0], (400, 600, 32), (3, 3))
l2 = plonny.Pool((200, 300, 32), [l1])
l3 = plonny.Conv2D([l2], (200, 300, 64), (3, 3))
l4 = plonny.Pool((100, 150, 64), [l3])
l41 = plonny.Custom((100, 150, 1), "Feature\n Map", [l4])
l42 = plonny.Custom((100, 150, 1), "Feature\n Map", [l4])
l43 = plonny.Custom((100, 150, 1), "Feature\n Map", [l4])
l44 = plonny.Custom((100, 150, 1), "Feature\n Map", [l4])
l5 = plonny.FullyConnected([l41, l42, l43, l44], 64)
l6 = plonny.Custom((1, 1), "LSTM", [l5])
lg = layergrid.LayerGrid()
lg.set(0, 0, l0)
lg.set(0, 1, l1)
lg.set(0, 2, l2)
lg.set(0, 3, l3)
lg.set(0, 4, l4)
lg.set(0, 5, l41)
lg.set(1, 5, l42)
lg.set(2, 5, l43)
lg.set(3, 5, l44)
lg.set(0, 6, l5)
lg.set(0, 7, l6)
print(lg)
graph.Graph(lg).graphshow()
size=16)
GraphParam.txt_height = maxheight - 2 * GraphParam.txt_margin
# Iterate layers, plotting their output shapes
for row in grid.rows():
for layer in row:
layer.show(ax, layer.inbound)
plt.show()
if __name__ == "__main__":
l0 = plonny.Input((32, 32, 1))
l02 = plonny.Input((32, 32, 1))
l1 = plonny.Conv2D([l0, l02], (16, 16, 1), (3, 3))
l2 = plonny.Conv2D([l1], (32, 32, 1), (3, 3))
l3 = plonny.Conv2D([l2], (32, 32, 1), (3, 3))
l4 = plonny.Conv2D([l3], (32, 32, 1), (3, 3))
l5 = plonny.Conv2D([l4], (32, 32, 1), (3, 3))
l22 = plonny.Conv2D([l1], (32, 32, 1), (3, 3))
lg = LayerGrid()
lg.set(0, 0, l0)
lg.set(1, 0, l02)
lg.set(0, 1, l1)
lg.set(0, 2, l2)
lg.set(1, 2, l22)
# lg.set(0, 2, l2)
# lg.set(0, 3, l3)
import plonny
if __name__ == "__main__":
# Define model by calling layers
image_0 = plonny.Input((32, 128, 1))
conv2_1 = plonny.Conv2D(image_0, (32, 128, 16), (3, 3))
Pool_2 = plonny.Pool(conv2_1, (16, 64, 16))
conv2_3 = plonny.Conv2D(Pool_2, (16, 64, 64), (3, 3))
Pool_4 = plonny.Pool(conv2_3, (8, 32, 64))
conv2_5 = plonny.Conv2D(Pool_4, (8, 32, 64), (9, 9))
Pool_6 = plonny.Pool(conv2_5, (4, 16, 64))
conv2_7 = plonny.Conv2D(Pool_6, (4, 16, 64), (3, 3))
conv2_8 = plonny.Conv2D(conv2_7, (4, 16, 16), (3, 3))
conv2_9 = plonny.Conv2D(conv2_8, (4, 16, 7), (3, 3))
resh_10 = plonny.Reshape(conv2_9, (8, 56))
conv_11 = plonny.Conv2D(resh_10, (8, 28), (3, 3))
fccc_12 = plonny.FullyConnected(conv_11, 8)
fccc_13 = plonny.FullyConnected(fccc_12, 50)
fccc_13.graphshow("Neural Network")