def forward(x, net):
activ = F.relu
t = jhML.Variable(x, requires_grad=False)
activated_layers = net[:-1]
for l in activated_layers:
t = l(t)
t = activ(t)
return net[-1](t)
activ = F.relu
W1, b1, W2, b2, W3, b3, W4, b4 = parameters
t = x
t = activ((F.linear(t, W1, b1)))
t = activ((F.linear(t, W2, b2)))
# t = activ((F.linear(t, W3, b3)))
return F.linear(t, W4, b4)
if __name__ == "__main__":
x = [[0, 0], [0, 1], [1, 0], [1, 1]]
gt = [[0], [1], [1], [0]]
W1 = jhML.Variable(np.random.randn(2, 4))
b1 = jhML.Variable(np.random.randn(4))
W2 = jhML.Variable(np.random.randn(4, 12))
b2 = jhML.Variable(np.random.randn(12))
W3 = jhML.Variable(np.random.randn(12, 12))
b3 = jhML.Variable(np.random.randn(12))
W4 = jhML.Variable(np.random.randn(12, 1))
b4 = jhML.Variable(np.random.randn(1))
parameters = [W1, b1, W2, b2, W3, b3, W4, b4]
num_epoch = int(1e+5)
for epoch in range(num_epoch):
[2],
[7],
]
num_class = 8
net = nn.Sequential(nn.Linear(3, 4), nn.ReLU(), nn.Linear(4, 1000),
nn.ReLU(), nn.Linear(1000, 800), nn.ReLU(),
nn.Linear(800, 100), nn.ReLU(), nn.Linear(100, 8),
nn.ReLU(), nn.Linear(8, num_class)).to_gpu()
#### learning
optim = optim.RMSprop(net.params(), lr=1e-4)
num_epoch = int(1e+4)
for epoch in range(num_epoch):
data, label = jhML.Variable(x).to_gpu(), jhML.Variable(gt).to_gpu()
optim.zero_grad()
pred = net(data)
loss = F.softmax_cross_entropy(pred, label)
loss.backward()
optim.step()
if epoch % (num_epoch / 100) == 0:
print("%d/%d" % (epoch, num_epoch))
print(F.argmax(pred))
for num in x:
data = jhML.Variable(num).to_gpu()
pred = net(data).to_cpu()
print("0b%d%d%d = %d" %
(num[0], num[1], num[2], F.argmax(pred, axis=0)))
import jhML.layers as nn
import jhML.optimizer as optim
if __name__ == "__main__":
x = [[0, 0],
[0, 1],
[1, 0],
[1, 1]]
gt = [[0],
[1],
[1],
[0]]
data = jhML.Variable(x)
net = nn.Sequential(
nn.Linear(2, 4),
nn.ReLU(),
nn.Linear(4, 8),
nn.ReLU(),
nn.Linear(8, 8),
nn.ReLU(),
nn.Linear(8, 1)
)
#### learning
optim = optim.RMSprop(net.params(), lr=1e-4)
num_epoch = int(1e+5)
for epoch in range(num_epoch):