def test_fc():
X = np.zeros((4, 2, 1, 1))
X[0, :, 0, 0] = [0., 0.]
X[1, :, 0, 0] = [0., 1.]
X[2, :, 0, 0] = [1., 0.]
X[3, :, 0, 0] = [1., 1.]
Y = np.array([8., 10., 12., 14.]).reshape((-1, 1))
data, label = L.Data([X, Y], "Data")()
fc1 = L.FC(data, "fc1", dim_out=1)
loss = L.MSE(fc1, "MSE", label=label)
fc1.reshape()
fc1.W = np.array([1.0, 3.0]).reshape(fc1.W.shape)
fc1.b = np.array([0.0]).reshape(fc1.b.shape)
net = mobula.Net()
net.set_loss(loss)
net.lr = 0.5
for i in range(30):
net.forward()
net.backward()
print("Iter: %d, Cost: %f" % (i, loss.Y))
# forward one more time, because of the change of weights last backward
net.forward()
target = np.dot(X.reshape((4, 2)), fc1.W.T) + fc1.b
print(target, fc1.Y)
assert np.allclose(fc1.Y, target)
def test_fc2():
X = np.random.random((4, 2, 1, 1))
Y1 = np.random.random((4, 10))
Y2 = np.random.random((4, 10))
[x, y1, y2] = L.Data([X, Y1, Y2])
fc1 = L.FC(x, dim_out=10)
fc2 = L.FC(x, dim_out=10)
loss1 = L.MSE(fc1, label=Y1)
loss2 = L.MSE(fc2, label=Y2)
net = mobula.Net()
loss = L.L1Loss(loss1 + loss2)
net.set_loss(loss)
L1Loss = mobula.get_layer("L1Loss")
Add = mobula.get_layer(L1Loss.model.name)
net.lr = 0.5
for i in range(30):
net.forward()
net.backward()
print("Iter: %d, Cost: %f" % (i, loss.Y))
# check forward
t1 = np.dot(X.reshape((4, 2)), fc1.W.T) + fc1.b.T
t2 = np.dot(X.reshape((4, 2)), fc2.W.T) + fc2.b.T
# forward one more time, because of the change of weights last backward
net.forward()
assert np.allclose(fc1.Y, t1)
assert np.allclose(fc2.Y, t2)
def __init__(self, X, labels):
data, label = L.Data([X, labels], "data", batch_size=100)()
conv1 = L.Conv(data, "conv1", dim_out=20, kernel=5)
pool1 = L.Pool(conv1, "pool1", pool=L.Pool.MAX, kernel=2, stride=2)
conv2 = L.Conv(pool1, "conv2", dim_out=50, kernel=5)
pool2 = L.Pool(conv2, "pool2", pool=L.Pool.MAX, kernel=2, stride=2)
fc3 = L.FC(pool2, "fc3", dim_out=500)
relu3 = L.ReLU(fc3, "relu3")
pred = L.FC(relu3, "pred", dim_out=10)
loss = L.SoftmaxWithLoss(pred, "loss", label=label)
# Net Instance
self.net = mobula.Net()
# Set Loss Layer
self.net.set_loss(loss)
def test_saver():
filename = "tmp.net"
X = np.random.random((4,2,1,1))
Y = np.random.random((4, 10))
x, y = L.Data([X, Y])
fc = L.FC(x, dim_out = 10)
with M.name_scope("mobula"):
prelu = L.PReLU(fc)
loss = L.MSE(prelu, label = y)
net = M.Net()
net.set_loss(loss)
init_params(fc)
init_params(prelu)
# save mobula
M.save_scope(filename, "mobula")
params_f = clear_params(fc)
params_p = clear_params(prelu)
for p in fc.params + prelu.params:
assert np.isnan(p).all()
M.load_scope(filename)
for p in fc.params:
assert np.isnan(p).all()
for i, p in enumerate(prelu.params):
assert np.allclose(p, params_p[i])
init_params(fc)
init_params(prelu)
# save all
M.save_scope(filename)
params_f = clear_params(fc)
params_p = clear_params(prelu)
for p in fc.params + prelu.params:
assert np.isnan(p).all()
M.load_scope(filename)
for i, p in enumerate(fc.params):
assert np.allclose(p, params_f[i])
for i, p in enumerate(prelu.params):
assert np.allclose(p, params_p[i])
os.remove(filename)
def test_net_saver():
filename = "tmp.net"
X = np.random.random((4,2,1,1))
Y = np.random.random((4, 10))
x, y = L.Data([X, Y])
x = L.FC(x, dim_out = 10)
with M.name_scope("mobula"):
x = L.PReLU(x)
loss = L.MSE(x, label = y)
net = M.Net()
net.set_loss(loss)
net.lr = 0.01
for i in range(10):
net.forward()
net.backward()
net.save(filename)
# random init layers
lst = M.get_layers("/")
assert len(lst) == 4 # Data, FC, PReLU, MSE
k = 0
rec = []
for l in lst:
for i in range(len(l.params)):
rec.append(l.params[i])
l.params[i][...] = None
k += 1
assert k == 3 # FC.W, FC.b, PReLU.a
for l in lst:
for i in range(len(l.params)):
assert np.isnan(l.params[i]).all()
net.load(filename)
h = 0
for l in lst:
for i in range(len(l.params)):
assert np.allclose(rec[h], l.params[i])
h += 1
os.remove(filename)
def test_net():
X = np.random.random((4, 2, 1, 1))
Y1 = np.random.random((4, 5))
Y2 = np.random.random((4, 5))
[x, y1, y2] = L.Data([X, Y1, Y2])
fc0 = L.FC(x, dim_out=10)
fc1 = L.FC(fc0, dim_out=5)
fc2 = L.FC(fc0, dim_out=5)
loss1 = L.MSE(fc1, label=y1)
loss2 = L.MSE(fc2, label=y2)
net = mobula.Net()
net.set_loss(loss1 + loss2)
net.lr = 0.01
for i in range(10):
net.forward()
net.backward()
net.time()
print("Iter: %d, Cost: %f" % (i, loss1.Y + loss2.Y))
assert np.allclose(fc0.dY, fc1.dX + fc2.dX)
# LeNet-5 data, label = L.Data([X, labels], "data", batch_size = 100) conv1 = L.Conv(data, "conv1", dim_out = 20, kernel = 5) pool1 = L.Pool(conv1, "pool1", pool = L.Pool.MAX, kernel = 2, stride = 2) relu1 = L.ReLU(pool1, "relu1") conv2 = L.Conv(relu1, "conv2", dim_out = 50, kernel = 5) pool2 = L.Pool(conv2, "pool2", pool = L.Pool.MAX, kernel = 2, stride = 2) relu2 = L.ReLU(pool2, "relu2") fc3 = L.FC(relu2, "fc3", dim_out = 500) relu3 = L.ReLU(fc3, "relu3") pred = L.FC(relu3, "pred", dim_out = 10) loss = L.SoftmaxWithLoss(pred, "loss", label = label) # Net Instance net = mobula.Net() # Set Loss Layer net.set_loss(loss) # Set Solver solver = S.Momentum(gamma = 0.1, stepsize = 1000) solver.lr_policy = S.LR_POLICY.STEP net.set_solver(S.Momentum()) # Learning Rate net.lr = 0.005 ''' If start_iter > 0, load the existed model and continue to train. Otherwise, initialize weights and start to train.