def test_api(self):
startup_program = Program()
main_program = Program()
startup_program.random_seed = SEED
main_program.random_seed = SEED
with program_guard(main_program, startup_program):
input = np.random.random([2, 2, 25]).astype("float32")
x = fluid.layers.data(
name="x",
shape=[2, 2, 25],
append_batch_size=False,
dtype="float32")
out_1 = fluid.layers.fc(input=x, size=1, num_flatten_dims=-1)
out_2 = fluid.layers.fc(input=x, size=1, num_flatten_dims=2)
place = fluid.CPUPlace() if not core.is_compiled_with_cuda(
) else fluid.CUDAPlace(0)
exe = fluid.Executor(place=place)
exe.run(startup_program)
res_1, res_2 = exe.run(main_program,
feed={"x": input},
fetch_list=[out_1, out_2])
assert np.array_equal(res_1, res_2)
def check_network_convergence(self,
use_cuda=True,
use_mem_opt=False,
iter_num=5):
prog = Program()
startup_prog = Program()
prog.random_seed = 100
startup_prog.random_seed = 100
with program_guard(prog, startup_prog):
image = layers.data(name='x', shape=[784], dtype='float32')
label = layers.data(name='y', shape=[1], dtype='int64')
limit = layers.fill_constant(shape=[1], dtype='int64', value=5)
cond = layers.less_than(x=label, y=limit)
ie = layers.IfElse(cond)
with ie.true_block():
true_image = ie.input(image)
hidden = layers.fc(input=true_image, size=100, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
with ie.false_block():
false_image = ie.input(image)
hidden = layers.fc(input=false_image, size=200, act='tanh')
prob = layers.fc(input=hidden, size=10, act='softmax')
ie.output(prob)
prob = ie()
loss = layers.cross_entropy(input=prob[0], label=label)
avg_loss = layers.mean(loss)
optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
optimizer.minimize(avg_loss, startup_prog)
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=200)
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = Executor(place)
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_cuda = use_cuda
build_strategy = fluid.BuildStrategy()
build_strategy.memory_optimize = use_mem_opt
train_cp = compiler.CompiledProgram(fluid.default_main_program())
train_cp = train_cp.with_data_parallel(
loss_name=avg_loss.name,
exec_strategy=exec_strategy,
build_strategy=build_strategy)
fetch_list = [avg_loss.name]
exe.run(startup_prog)
PASS_NUM = 100
loop = 0
ret = []
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array([x[0] for x in data]).astype("float32")
y_data = np.array([x[1] for x in data]).astype("int64")
y_data = y_data.reshape((y_data.shape[0], 1))
outs = exe.run(train_cp,
feed={'x': x_data,
'y': y_data},
fetch_list=[avg_loss])
loop += 1
ret.append(outs[0])
if iter_num == loop:
return ret
return ret