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 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_mse():
N, C, H, W = 2, 3, 4, 5
a = np.random.random((N, C, H, W)) - 0.5
b = np.random.random((N, C, H, W)) - 0.5
l = L.MSE(a, label=b)
y = l.eval()
d = a - b
assert np.allclose(np.mean(np.square(d)), l.Y)
l.dY = np.random.random(l.Y.shape)
l.backward()
assert np.allclose(l.dX, 2 * d * l.dY)
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)
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)
im = imresize(im, target_size) # TO GRAY im = im[:, :, 0] * 0.299 + im[:, :, 1] * 0.587 + im[:, :, 2] * 0.114 h, w = im.shape t = 1 Y = im.reshape((1, h, w, t)).transpose((0, 3, 1, 2)) X = np.random.random((1, t, h, w)) - 0.5 data, label = L.Data([X, Y]) conv = L.Conv(data, dim_out=42, kernel=3, pad=1) relu = L.ReLU(conv) convt = L.ConvT(relu, dim_out=t, kernel=3, pad=1) relu2 = L.ReLU(convt) loss = L.MSE(relu2, label=label) # Net Instance net = mobula.Net() # Set Loss Layer net.set_loss(loss) # Set Solver net.set_solver(S.Momentum()) # Learning Rate net.lr = 2e-6 start_iter = 0 max_iter = 10000 plt.ion() for i in range(start_iter, max_iter + 1):