def test_get_node(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
fc1.element().set_name("fc1")
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
res = model.node("fc1")
assert res.element().name() == "fc1"
def test_get_node(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
fc1.element().set_name("fc1")
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
res = model.node("fc1")
assert res.element().name() == "fc1"
def test_create_node(self):
import numpy as np
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)), np.ones((2,))])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
assert_allclose(output,
np.array([2.2, 2.2]))
def test_create_node(self):
import numpy as np
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
model = Model([fc1, fc2], [output1])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)), np.ones((2,))])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
assert_allclose(output,
np.array([2.2, 2.2]))
def test_graph_backward(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
output2 = Threshold(10.0)(cadd)
model = Model([fc1, fc2], [output1, output2])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)) * 2, np.ones((2,)) * 2])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
gradInput = model.backward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])],
[np.array([1.0, 2.0]),
np.array([3.0, 4.0])])
assert_allclose(gradInput[0],
np.array([3.0, 3.0, 3.0, 3.0]))
assert_allclose(gradInput[1],
np.array([6.0, 6.0, 6.0, 6.0]))
def test_graph_backward(self):
fc1 = Linear(4, 2)()
fc2 = Linear(4, 2)()
cadd = CAddTable()([fc1, fc2])
output1 = ReLU()(cadd)
output2 = Threshold(10.0)(cadd)
model = Model([fc1, fc2], [output1, output2])
fc1.element().set_weights([np.ones((4, 2)), np.ones((2,))])
fc2.element().set_weights([np.ones((4, 2)) * 2, np.ones((2,)) * 2])
output = model.forward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])])
gradInput = model.backward([np.array([0.1, 0.2, -0.3, -0.4]),
np.array([0.5, 0.4, -0.2, -0.1])],
[np.array([1.0, 2.0]),
np.array([3.0, 4.0])])
assert_allclose(gradInput[0],
np.array([3.0, 3.0, 3.0, 3.0]))
assert_allclose(gradInput[1],
np.array([6.0, 6.0, 6.0, 6.0]))
