def main():
#trivial test
X = np.random.randn(100, 5)
A = np.random.randn(10, 5)
bias = np.random.randn(10)
Y = np.dot(A, X.T).T + bias
print(np.dot(A, X.T).T.shape)
model = nn.Sequential()
model.add(nn.Linear(5, 10))
model.add(nn.Tanh(10))
model.add(nn.Linear(10, 10))
model.add(nn.MSE(10))
print("Batch mode")
for i in xrange(10000):
print("Loss", model.forward(X, Y))
model.backward(alpha=0.1)
print("Single mode")
for i in xrange(10000):
for j in xrange(X.shape[0]):
print("Loss", model.forward(X[j], Y[j]))
model.backward(alpha=0.1)
def main():
np.random.seed(1)
model = nn.Sequential()
model.add(nn.Linear(2, 5))
model.add(nn.Sigmoid())
model.add(nn.Linear(5, 1))
#model.add(nn.Sigmoid())
model.set_metric(nn.MSE())
x = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
y = np.array([[0], [1], [1], [0]])
model.fit(x, y, 5000, 1)
import nn network = nn.Container() network.add(nn.Reshape((1, 784))) network.add(nn.Linear(784, 100)) network.add(nn.Sigmoid()) network.add(nn.Linear(100, 10)) network.add(nn.Sigmoid()) network.add(nn.MSE(), cost=True) network.make()
args = parse()
if args.dataset == 'linear':
X_train, Y_train = generate_linear(n=100)
X_test, Y_test = generate_linear(n=100)
elif args.dataset == 'xor':
X_train, Y_train = generate_XOR_easy()
X_test, Y_test = generate_XOR_easy()
else:
raise RuntimeError('Dataset Not Found')
net = Net()
if args.criterion == 'mse':
criterion = nn.MSE()
elif args.criterion == 'crossentropy':
criterion = nn.CrossEntropy()
else:
raise RuntimeError('Criterion Not Found')
if args.optimizer == 'sgd':
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=args.momentum)
elif args.optimizer == 'adagrad':
optimizer = optim.Adagrad(net.parameters(), lr=args.lr)
else:
raise RuntimeError('Optimizer Not Found')
model = Model(net, criterion, optimizer)
train_history = model.train(X_train, Y_train, epochs=args.epochs)
test_history = model.test(X_test, Y_test)