def test_updateWeights(self):
loss = C.LossMSE()
m = N.Network(loss)
try:
m.updateWeights(0.01)
except:
return 0
return 1
def test_forwardNotImplemented(self):
loss = C.LossMSE()
m = N.Sequential(loss)
try:
m.forward()
except:
return 0
return 1
def test_backwardCall(self):
loss = C.LossMSE()
m = N.Network(loss)
try:
m.backwardCall()
except:
return 0
return 1
def test_resetGradients(self):
loss = C.LossMSE()
m = N.Network(loss)
try:
m.resetGradients(x, y)
except:
return 0
return 1
def test_iter(self):
loss = C.LossMSE()
m = N.Network(loss)
try:
m.__iter__()
except:
return 0
return 1
def test_forward(self):
loss = C.LossMSE()
m = N.Network(loss)
try:
m.forward()
except:
return 0
return 1
def test_forward(self):
loss = C.LossMSE()
m = SequentialChild(loss)
x = FloatTensor([[1, 2, 3], [4, 3, 2]])
output = m.forward(x)
if output.size() == (2, 3):
return 0
return 1
def test_backward(self):
x = FloatTensor([[1, 2, 3], [-4, 2, 3]])
y = FloatTensor([[3, 2, 0], [-1, 2, 3]])
loss = C.LossMSE()
m = N.Network(loss)
try:
m.backward(x, y)
except:
return 0
return 1
def test_grad(self):
loss = C.LossMSE()
x = FloatTensor([[1, 2, 3], [-4, 2, 3]])
y = FloatTensor([[3, 2, 0], [-1, 2, 3]])
output = loss.grad(x, y)
expected_output = FloatTensor([[-4, 0, 6], [-6, 0, 0]])
if not areEqual(output, expected_output):
return 1
return 0
def test_function(self):
loss = C.LossMSE()
x = FloatTensor([[1, 2, 3], [-4, 2, 3]])
y = FloatTensor([[3, 2, 0], [-1, 2, 3]])
res = loss.function(x, y)
expected_res = 22 / 6
if abs(res - expected_res) > 1e-10:
return 1
return 0
def test_backward(self):
loss = C.LossMSE()
m = SequentialChild(loss)
x = FloatTensor([[2, 1, 3], [4, 3, 2]])
y = FloatTensor([[1, 2, 3], [4, -2, 2]])
output = m.forward(x)
x = output
output = m.backward(x, y)
l_value = loss.function(x, y)
if abs(l_value - output) < 1e-10:
return 0
return 1
