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()