def check_l2decay(self, place, model):
main_prog = fluid.framework.Program()
startup_prog = fluid.framework.Program()
startup_prog.random_seed = 1
with self.scope_prog_guard(main_prog=main_prog,
startup_prog=startup_prog):
data = fluid.layers.data(name="words",
shape=[1],
dtype="int64",
lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
avg_cost_l2 = model(data, label, len(self.word_dict))
param_list = fluid.default_main_program().block(0).all_parameters()
para_sum = []
for para in param_list:
para_mul = fluid.layers.square(x=para)
para_sum.append(fluid.layers.reduce_sum(input=para_mul))
avg_cost_l2 += fluid.layers.sums(para_sum) * .5
optimizer = fluid.optimizer.Adagrad(learning_rate=0.1)
optimizer.minimize(avg_cost_l2)
param_sum = self.run_program(place, [data, label])
return param_sum
def check_l2decay_regularizer(self, place, model):
paddle.seed(1)
paddle.framework.random._manual_program_seed(1)
main_prog = fluid.framework.Program()
startup_prog = fluid.framework.Program()
with self.scope_prog_guard(
main_prog=main_prog, startup_prog=startup_prog):
data = fluid.layers.data(
name="words", shape=[1], dtype="int64", lod_level=1)
label = fluid.layers.data(name="label", shape=[1], dtype="int64")
avg_cost = model(data, label, len(self.word_dict))
optimizer = fluid.optimizer.Adagrad(
learning_rate=0.1,
regularization=fluid.regularizer.L2Decay(1.0))
optimizer.minimize(avg_cost)
param_sum = self.run_program(place, [data, label])
return param_sum