def __init__(self):
super(cnn_mnist_model, self).__init__()
self.conv1 = nn.conv2d(1, 32, 3, padding=1)
self.pool = nn.maxpool2d(2, 2)
self.conv2 = nn.conv2d(32, 48, 3)
self.fc1 = nn.linear(48 * 2 * 2, 120) # (599, 192)
self.fc2 = nn.linear(120, 84)
self.fc3 = nn.linear(84, 10)
self.relu1 = nn.relu()
self.relu2 = nn.relu()
self.relu3 = nn.relu()
self.relu4 = nn.relu()
def __init__(self):
super(spiral_model, self).__init__()
self.fc1 = nn.linear(2, 16)
self.fc2 = nn.linear(16, 16)
self.fc3 = nn.linear(16, 2)
self.tanh1 = nn.tanh()
self.tanh2 = nn.tanh()
self.sig = nn.relu()
def test_relu(self):
import madml
import madml.nn as nn
x = np.random.uniform(-2, 2, size=81).reshape([9, 9])
t1 = madml.tensor(x)
module = nn.relu()
logit = module.forward(t1)
y = logit.host_data
logit.gradient.host_data = x
dlogit = module.backward()
dx = dlogit.host_data
self.assertTrue((np.sum(y) == np.sum(dx)).all())
def test_relu():
x = np.random.uniform(-2, 2, size=81).reshape([9, 9])
t1 = madml.tensor(x)
module = nn.relu()
t3 = module._forward_gpu(t1)
y_hat = t3.download()
print(y_hat)
print()
t2 = module._forward_cpu(t1)
y = t2.host_data
print(y)
input()
def __init__(self):
super(dnn_mnist_model, self).__init__()
self.fc1 = nn.linear(8 * 8, 256)
self.fc2 = nn.linear(256, 10)
self.relu1 = nn.relu()
self.relu2 = nn.relu()