def compare_ifelse_op_and_numpy(self, place):
self.set_test_case()
prog = Program()
startup_prog = Program()
with program_guard(prog, startup_prog):
src = layers.data(name='data', shape=[1], dtype='float32')
cond = layers.fill_constant(
[1], dtype='float32', value=self.cond_value)
ifcond = layers.less_than(x=src, y=cond)
ie = layers.IfElse(ifcond)
with ie.true_block():
true_target = ie.input(src)
true_target = fluid.layers.exp(true_target)
ie.output(true_target)
with ie.false_block():
false_target = ie.input(src)
false_target = fluid.layers.tanh(false_target)
ie.output(false_target)
if_out = ie()
out = layers.reduce_sum(if_out)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
fetch_list = [out]
o1, = exe.run(fluid.default_main_program(),
feed={'data': self.data},
fetch_list=[out])
o2 = self.numpy_cal()
self.assertTrue(
np.allclose(
o1, o2, atol=1e-8),
"IfElse result : " + str(o1) + "\n Numpy result :" + str(o2))
def test_ifelse(self):
prog = Program()
startup_prog = Program()
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_batch_size_like(input=label,
dtype='int64',
shape=[1],
value=5.0)
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.reader.shuffle(
paddle.dataset.mnist.train(), buf_size=8192),
batch_size=200)
place = core.CPUPlace()
exe = Executor(place)
exe.run(kwargs['startup_program'])
PASS_NUM = 100
for pass_id in range(PASS_NUM):
for data in train_reader():
x_data = np.array(map(lambda x: x[0], data)).astype("float32")
y_data = np.array(map(lambda x: x[1], data)).astype("int64")
y_data = y_data.reshape((y_data.shape[0], 1))
outs = exe.run(kwargs['main_program'],
feed={
'x': x_data,
'y': y_data
},
fetch_list=[avg_loss])
print outs[0]
if outs[0] < 1.0:
return
self.assertFalse(True)
def test_input_type_error(self):
main_program = Program()
startup_program = Program()
with program_guard(main_program, startup_program):
src = layers.data(name='data', shape=[1], dtype='float32')
const_value = layers.fill_constant(
[1], dtype='float32', value=123.0)
ifcond = layers.less_than(x=src, y=const_value)
with self.assertRaises(TypeError):
ie = layers.IfElse(set())
with self.assertRaises(TypeError):
ie = layers.IfElse(ifcond, set())
with self.assertRaises(TypeError):
ie = layers.IfElse(ifcond)
with ie.true_block():
true_target = ie.input(src)
true_target = fluid.layers.exp(true_target)
ie.output([])
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