def test_neuron_forward():
a = variable.Variables(1.0)
b = variable.Variables(2.0)
c = variable.Variables(-3.0)
x = variable.Variables(-1.0)
y = variable.Variables(3.0)
ax = Arithmetic.Multiply(a, x)
by = Arithmetic.Multiply(b, y)
axpby = Arithmetic.Add(ax, by)
axpbypc = Arithmetic.Add(axpby, c)
s = sigmoid.Sigmoid(axpbypc)
assert round(s.forward(), 4) == 0.8808
def test_minus_grad():
a = variable.Variables(100)
b = variable.Variables(10)
c = Arithmetic.Minus(a, b)
c.backward(1)
assert a.get_grad() == 1
assert b.get_grad() == -1
def test_neuron_backward():
a = variable.Variables(1.0)
b = variable.Variables(2.0)
c = variable.Variables(-3.0)
x = variable.Variables(-1.0)
y = variable.Variables(3.0)
ax = Arithmetic.Multiply(a, x)
by = Arithmetic.Multiply(b, y)
axpby = Arithmetic.Add(ax, by)
axpbypc = Arithmetic.Add(axpby, c)
s = sigmoid.Sigmoid(axpbypc)
s.backward(1)
assert round(a.get_grad(), 3) == -0.105
assert round(b.get_grad(), 3) == 0.315
assert round(c.get_grad(), 3) == 0.105
assert round(x.get_grad(), 3) == 0.105
assert round(y.get_grad(), 3) == 0.210
def test_accessing_grad_simple_backward2():
test_var_1 = variable.Variables(100)
test_var_2 = constant.Constant(10)
add_both_var = Arithmetic.Multiply(test_var_1, test_var_2)
# Initalize with grad = 1 to start the backpropagation....
add_both_var.backward(1)
var_grad_1 = test_var_1.get_grad()
assert var_grad_1 == 10
def test_accessing_grad_simple_backward():
test_var_1 = variable.Variables(100)
test_var_2 = variable.Variables(10)
add_both_var = Arithmetic.Add(test_var_1, test_var_2)
# Initalize with grad = 1 to start the backpropagation....
add_both_var.backward(1)
var_grad_1 = test_var_1.get_grad()
var_grad_2 = test_var_2.get_grad()
assert var_grad_1 == 1 and var_grad_2 == 1
def test_multi():
test_var = variable.Variables(100)
test_constant = constant.Constant(35)
test_add = Arithmetic.Multiply(test_var, test_constant)
assert test_add.forward() == 100 * 35
def test_minus():
a = variable.Variables(100)
b = variable.Variables(10)
c = Arithmetic.Minus(a, b)
assert c.forward() == 100 - 10
def test_add():
test_var = variable.Variables(100)
test_constant = constant.Constant(35)
test_add = Arithmetic.Add(test_var, test_constant)
assert test_add.forward() == 100 + 35
from AutoDiff.scalarCalculation.visualize import visualize from AutoDiff.scalarCalculation.values import variable, constant from AutoDiff.scalarCalculation.operators import Arithmetic, sigmoid, square a = variable.Variables(1.0, name="Variable1") b = variable.Variables(2.0, name="Variable2") c = variable.Variables(-3.0, name="Variable3") x = variable.Variables(-1.0, name="Variable4") y = variable.Variables(3.0, name="Variable5") ax = Arithmetic.Multiply(a, x, name="Multiply1") by = Arithmetic.Multiply(b, y, name="Multiply2") axpby = Arithmetic.Add(ax, by, name="add1") axpbypc = Arithmetic.Add(axpby, c, name="add2") s = sigmoid.Sigmoid(axpbypc) # test_var_1 = variable.Variables(100, name="test_var_1") # test_var_2 = variable.Variables(10, name="test_var_2") # # add_both_var = Arithmetic.Add(test_var_1, test_var_2) visualize.plot_graph(s)