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, ))
k, l = 20, (40 - 4) // 3
lay = bn_relu_conv(inp, 3, 1, 1, k, False, False)
for i in range(3):
lay = transition(dense_block(lay, k, l), i)
#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)
info = CInfo()
info.get_complexity(network.outputs).as_table().show()
return network
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 = conv_bn(inp, 3, 1, 1, 16 * 4 * 2, True)
n = 4 * 3
group = 8
lis = [16 * 4, 32 * 4, 64 * 4]
for i in range(len(lis)):
lay = res_block(lay, lis[i], i, n, group)
#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)
info = CInfo()
info.get_complexity(network.outputs).as_table().show()
"""
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