def make_network(minibatch_size=128, debug=False):
patch_size = 32
inp = DataProvider("data",
shape=(minibatch_size, 3, patch_size, patch_size),
dtype=np.float32)
label = DataProvider("label", shape=(minibatch_size, ), dtype=np.int32)
lay, w = conv_bn(inp, 3, 1, 1, 16, True)
lis_w = [w]
n = 3
lis = [16, 32, 64]
for i in lis:
lay, lis_new = res_block(lay, i, n)
lis_w += lis_new
#global average pooling
#feature = lay.mean(axis = 2).mean(axis = 2)
feature = Pooling2D("pooling",
lay,
window=8,
stride=8,
padding=0,
mode="AVERAGE")
pred = Softmax(
"pred",
FullyConnected(
"fc0",
feature,
output_dim=10,
#W = G(mean = 0, std = (1 / 64)**0.5),
#b = C(0),
nonlinearity=Identity()))
network = Network(outputs=[pred])
network.loss_var = CrossEntropyLoss(pred, label)
lmd = 1
for w in lis_w:
w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0)
w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0)
A = O.MatMul(w.dimshuffle(1, 0), w)
network.loss_var += lmd * (
(A - np.identity(A.partial_shape[0]))**2).mean()
if debug:
visitor = NetworkVisitor(network.loss_var)
for i in visitor.all_oprs:
print(i)
print(i.partial_shape)
print("input = ", i.inputs)
print("output = ", i.outputs)
print()
return network
def make_network(minibatch_size=64):
patch_size = 32
inp = DataProvider("data",
shape=(minibatch_size, 3, patch_size, patch_size))
label = DataProvider("label", shape=(minibatch_size, ))
lay, w = bn_relu_conv(inp, 3, 1, 1, 16, False, False)
lis_w = [w]
k, l = 12, (40 - 4) // 3
for i in range(3):
#lay = transition(dense_block(lay, k, l), i)
lay, lis_new = dense_block(lay, k, l)
lis_w += lis_new
lay, lis_new = transition(lay, i)
lis_w += lis_new
#global average pooling
print(lay.partial_shape)
feature = lay.mean(axis=2).mean(axis=2)
#feature = Pooling2D("glbpoling", lay, window = 8, stride = 8, mode = "AVERAGE")
pred = Softmax(
"pred",
FullyConnected("fc0", feature, output_dim=10, nonlinearity=Identity()))
network = Network(outputs=[pred])
network.loss_var = CrossEntropyLoss(pred, label)
lmd = 0.01
for w in lis_w:
if w is None:
continue
print(w.partial_shape)
w = w.reshape(w.partial_shape[0], -1).dimshuffle(1, 0)
w = w / ((w**2).sum(axis=0)).dimshuffle('x', 0)
A = O.MatMul(w.dimshuffle(1, 0), w)
network.loss_var += lmd * (
(A - np.identity(A.partial_shape[0]))**2).sum()
return network