sneaxiy
7 years ago
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import paddle.fluid as fluid
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import paddle
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import unittest
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import six
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import numpy as np
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def tanh(x):
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return np.tanh(x)
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def tanh_grad(y, dy):
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return np.array(dy) * (1 - np.square(np.array(y)))
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def cross_entropy(logits, labels):
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logits = np.array(logits)
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labels = np.array(labels)
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M = logits.shape[0]
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N = logits.shape[1]
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ret = np.ndarray([M, 1]).astype(logits.dtype)
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for idx in six.moves.range(M):
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ret[idx][0] = -np.log(logits[idx][labels[idx][0]])
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return ret
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def cross_entropy_grad(logits, labels, bwd_dout):
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logits = np.array(logits)
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labels = np.array(labels)
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bwd_dout = np.array(bwd_dout)
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M = logits.shape[0]
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N = logits.shape[1]
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dlogits = np.zeros([M, N]).astype(logits.dtype)
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for idx in six.moves.range(M):
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dlogits[idx][labels[idx][0]] = -bwd_dout[idx] / logits[idx][labels[idx][
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0]]
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return dlogits, None
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def simple_fc_net(img, label, use_py_func_op):
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hidden = img
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for idx in range(4):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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if use_py_func_op:
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hidden = fluid.layers.tanh(hidden)
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else:
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new_hidden = fluid.default_main_program().current_block(
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).create_var(
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name='hidden_{}'.format(idx),
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dtype='float32',
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shape=hidden.shape)
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hidden = fluid.layers.py_func(
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func=tanh,
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x=hidden,
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out=new_hidden,
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backward_func=tanh_grad,
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skip_vars_in_backward_input=hidden)
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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if not use_py_func_op:
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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else:
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loss = fluid.default_main_program().current_block().create_var(
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name='loss', dtype='float32', shape=[-1, 1])
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fluid.layers.py_func(
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func=cross_entropy,
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x=[prediction, label],
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out=loss,
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backward_func=cross_entropy_grad,
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skip_vars_in_backward_input=loss)
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loss = fluid.layers.mean(loss)
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return loss
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def reader():
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for _ in six.moves.range(100):
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yield np.random.random([784]), np.random.random_integers(
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size=[1], low=0, high=9)
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def test_main(use_cuda, use_py_func_op):
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if use_cuda and not fluid.core.is_compiled_with_cuda():
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return None
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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with fluid.scope_guard(fluid.core.Scope()):
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fluid.default_main_program().random_seed = 1
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fluid.default_startup_program().random_seed = 1
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np.random.seed(1)
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img = fluid.layers.data(name='image', shape=[784], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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loss = simple_fc_net(img, label, use_py_func_op)
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optimizer = fluid.optimizer.SGD(learning_rate=1e-3)
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optimizer.minimize(loss)
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place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
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feeder = fluid.DataFeeder(feed_list=[img, label], place=place)
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r = paddle.batch(reader, batch_size=10)
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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ret = []
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for epoch_id in six.moves.range(2):
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for d in r():
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L, = exe.run(feed=feeder.feed(d), fetch_list=[loss])
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ret.append(L[0])
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return np.array(ret)
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class TestPyFuncOp(unittest.TestCase):
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def test_loss_diff(self):
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losses = []
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for use_cuda in [True, False]:
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for use_py_func_op in [True, False]:
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L = test_main(use_cuda, use_py_func_op)
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if L is not None:
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losses.append(L)
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for idx in six.moves.range(len(losses) - 1):
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max_diff = np.max(np.abs(losses[idx] - losses[0]))
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self.assertAlmostEqual(max_diff, 0, delta=1e-3)
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if __name__ == '__main__':
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unittest.main()
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