def test_set_global_bias_initilizer(self):
"""Test Set Global Bias initilizer with NormalInitializer
"""
main_prog = framework.Program()
startup_prog = framework.Program()
fluid.set_global_initializer(initializer.Uniform(low=-0.5, high=0.5),
bias_init=initializer.Normal(loc=0.0,
scale=2.0))
with fluid.program_guard(main_prog, startup_prog):
x = fluid.data(name="x", shape=[1, 3, 32, 32])
# default initilizer of bias in layers.conv2d is ConstantInitializer
conv = fluid.layers.conv2d(x, 5, 3)
block = startup_prog.global_block()
self.assertEqual(len(block.ops), 2)
# init bias is the first op, and weight is the second
bias_init_op = block.ops[0]
self.assertEqual(bias_init_op.type, 'gaussian_random')
self.assertAlmostEqual(bias_init_op.attr('mean'), 0.0, delta=DELTA)
self.assertAlmostEqual(bias_init_op.attr('std'), 2.0, delta=DELTA)
self.assertEqual(bias_init_op.attr('seed'), 0)
param_init_op = block.ops[1]
self.assertEqual(param_init_op.type, 'uniform_random')
self.assertAlmostEqual(param_init_op.attr('min'), -0.5, delta=DELTA)
self.assertAlmostEqual(param_init_op.attr('max'), 0.5, delta=DELTA)
self.assertEqual(param_init_op.attr('seed'), 0)
fluid.set_global_initializer(None)
net.add_sublayer('linear', nn.Linear(num_inputs, 1))
# Method 3:
# from collections import OrderedDict
# net = nn.Sequential(OrderedDict([
# ('linear', nn.Linear(num_inputs, 1))
# ]))
print(net)
for param in net.parameters():
print(param)
# 3.3.4 初始化模型参数
# 设置全局参数初始化
fluid.set_global_initializer(initializer.Uniform(), initializer.Constant())
# 3.3.5 定义损失函数
loss = nn.MSELoss()
# 3.3.6 定义优化算法
optimizer = optim.SGD(learning_rate=0.03, parameters=net.parameters())
print(optimizer)
# 设置不同自网络的学习率（待修改）
# optimizer = optim.SGD([
# {'params': net._sub_layers1.paramaters()},
# {'params': net._sub_layers2.paramaters(), 'lr': 0.01}
# ], learning_rate=0.03)
# for param_group in optimizer.param_groups: