import conx as cx
net = cx.Network("XOR2")
net.add(cx.Layer("input1", 2))
net.add(cx.Layer("input2", 2))
net.add(cx.Layer("hidden1", 2, activation="sigmoid"))
net.add(cx.Layer("hidden2", 2, activation="sigmoid"))
net.add(cx.Layer("shared-hidden", 2, activation="sigmoid"))
net.add(cx.Layer("output1", 2, activation="sigmoid"))
net.add(cx.Layer("output2", 2, activation="sigmoid"))
net.connect("input1", "hidden1")
net.connect("input2", "hidden2")
net.connect("hidden1", "shared-hidden")
net.connect("hidden2", "shared-hidden")
net.connect("shared-hidden", "output1")
net.connect("shared-hidden", "output2")
net.compile(loss='mean_squared_error',
optimizer=cx.SGD(lr=0.3, momentum=0.9))
ds = [
([[0, 0],[0, 0]], [[0, 0],[0, 0]], ["one", "one"]),
([[0, 0],[1, 1]], [[1, 1],[1, 1]], ["two", "two"]),
([[1, 1],[0, 0]], [[1, 1],[1, 1]], ["three", "three"]),
([[1, 1],[1, 1]], [[0, 0],[0, 0]], ["four", "four"])
]
net.dataset.load(ds)
net.train(2000, report_rate=10, accuracy=1)
net.test()
import conx as cx
ds = [[[0, 0], [0, 0.5]], [[0, 1], [1, 0.5]], [[1, 0], [1, 0.5]],
[[1, 1], [0, 0.5]]]
net = cx.Network("XOR")
net.add(cx.Layer("input", 2))
net.add(cx.Layer("hidden1", 3, activation="relu"))
net.add(cx.Layer("hidden2", 4, activation="relu"))
net.add(cx.Layer("output", 2, activation="softmax"))
net.connect("input", "hidden1")
net.connect("hidden1", "hidden2")
net.connect("hidden2", "output")
net.compile(error='mean_squared_error', optimizer=cx.SGD(lr=0.3, momentum=0.9))
# NOTE:
# net = Network("XOR", 2, 3, 4, 1, activation="sigmoid")
# is the same as:
# net = Network("XOR")
# net.add(Layer("input", shape=2))
# net.add(Layer("hidden1", shape=3, activation="sigmoid"))
# net.add(Layer("hidden2", shape=4, activation="sigmoid"))
# net.add(Layer("output", shape=1, activation="sigmoid"))
# net.connect("input", "hidden1")
# net.connect("hidden1", "hidden2")
# net.connect("hidden2", "output")
net.dataset.load(ds)
net.train(2000, report_rate=10, accuracy=1)
net.test()