def test_add_layer(self):
config = {
'input_shape': [3, 5, 5],
'step_size': 1,
'mu': 0.9,
'step_decay': 0.9
}
n = Net(config)
l = FC(10)
n.add(l)
def test_fit(self):
config = {
'input_shape': [3, 5, 5],
'step_size': 1,
'mu': 0.9,
'step_decay': 0.9
}
n = Net(config)
l = FC(10)
n.add(l)
x = np.random.random([1, 3, 5, 5]).reshape(1, -1)
y = np.array([0])
n.fit(x, y, 10)
def test_train_iteration(self):
config = {
'input_shape': [3, 5, 5],
'step_size': 1,
'mu': 0.9,
'step_decay': 0.9
}
n = Net(config)
l = FC(10)
n.add(l)
x = np.random.random([1, 3, 5, 5]).reshape(1, -1)
y = np.array([0])
loss = n.train_one_iteration(x, y)
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(2)
z = net.variable(3)
x2 = net.mul(x, x)
y2 = net.mul(y, y)
z2 = net.mul(z, z)
o1 = net.add(x2, y2)
o = net.add(o1, z2)
net.gradient_descendent()
print('x=', x.v, 'y=', y.v, 'z=', z.v)
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(2)
z = net.variable(3)
x2 = net.mul(x, x) # x*x=x2
y2 = net.mul(y, y) # y*y=y2
z2 = net.mul(z, z) # z*z=z2
o1 = net.add(x2, y2) # x2+y2=o1
o = net.add(o1, z2) # o1+z2=o
net.gradient_descendent()
print('x=', x.v, 'y=', y.v, 'z=', z.v)
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(3)
x2 = net.mul(x, x)
y2 = net.mul(y, y)
o = net.add(x2, y2)
'''
print('net.forward()=', net.forward())
print('net.backward()')
net.backward()
print('x=', x, 'y=', y, 'o=', o)
print('gfx = x.g/o.g = ', x.g/o.g, 'gfy = y.g/o.g=', y.g/o.g)
print('x2=', x2, 'y2=', y2)
'''
net.gradient_descendent()
print('x=', x.v, 'y=', y.v)
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(2)
z = net.variable(3)
x2 = net.mul(x, x)
y2 = net.mul(y, y)
z2 = net.mul(z, z)
o = net.add(x2, y2)
o = net.add(o, z2)
net.gradient_descendent()
print('x=', x.v, 'y=', y.v, 'z=', z.v)
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(2)
z = net.variable(3)
x2 = net.mul(x, x)
y2 = net.mul(y, y)
z2 = net.mul(z, z)
A = net.add(x2, y2)
B = net.add(A, z2)
net.gradient_descendent()
print('x=', x.v, 'y=', y.v, 'z=', z.v)
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(2)
z = net.variable(3)
x2 = net.mul(x, x)
y2 = net.mul(y, y)
z2 = net.mul(z, z)
t = net.add(x2, y2)
o = net.add(t, z2)
net.gradient_descendent()
print('x = ', x.v, 'y = ', y.v, 'z = ', z.v)
from net import Net
n = Net()
x = n.variable(1)
y = n.variable(2)
z = n.variable(3)
x2 = n.mul(x, x)
y2 = n.mul(y, y)
z2 = n.mul(z, z)
o = n.add(x2, y2)
out = n.add(o, z2)
n.gradient_descendent()
print('x={0:.5f} y={1:.5f} z={2:.5f}'.format(x.v, y.v, z.v))
from net import Net
net = Net()
x = net.variable(1)
y = net.variable(2)
z = net.variable(3)
x2 = net.mul(x, x)
y2 = net.mul(y, y)
z2 = net.mul(z, z)
q = net.add(x2, y2)
o = net.add(q, z2)
'''
print('net.forward()=', net.forward())
print('net.backward()')
net.backward()
print('x=', x, 'y=', y, 'o=', o)
print('gfx = x.g/o.g = ', x.g/o.g, 'gfy = y.g/o.g=', y.g/o.g)
print('x2=', x2, 'y2=', y2)
'''
net.gradient_descendent()
print('x=', x.v, 'y=', y.v, 'z=', z.v)
