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83 lines
2.7 KiB
83 lines
2.7 KiB
# 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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from __future__ import print_function
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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BATCH_SIZE = 128
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CLIP_MAX = 2e-6
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CLIP_MIN = -1e-6
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prog = fluid.framework.Program()
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with fluid.program_guard(main_program=prog):
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image = fluid.layers.data(name='x', shape=[784], dtype='float32')
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hidden1 = fluid.layers.fc(input=image, size=128, act='relu')
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hidden2 = fluid.layers.fc(input=hidden1, size=64, act='relu')
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predict = fluid.layers.fc(input=hidden2, size=10, act='softmax')
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label = fluid.layers.data(name='y', shape=[1], dtype='int64')
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cost = fluid.layers.cross_entropy(input=predict, label=label)
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avg_cost = fluid.layers.mean(cost)
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prog_clip = prog.clone()
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prog_clip.block(0).var(hidden1.name)._set_error_clip(
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fluid.clip.ErrorClipByValue(
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max=CLIP_MAX, min=CLIP_MIN))
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avg_cost_clip = prog_clip.block(0).var(avg_cost.name)
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fluid.backward.append_backward(loss=avg_cost)
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fluid.backward.append_backward(
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loss=avg_cost_clip, callbacks=[fluid.clip.error_clip_callback])
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hidden1_grad = prog.block(0).var(hidden1.name + "@GRAD")
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hidden1_grad_clip = prog_clip.block(0).var(hidden1.name + "@GRAD")
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hidden2_grad = prog.block(0).var(hidden2.name + "@GRAD")
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hidden2_grad_clip = prog_clip.block(0).var(hidden2.name + "@GRAD")
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.mnist.train(), buf_size=8192),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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feeder = fluid.DataFeeder(feed_list=[image, label], place=place)
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exe.run(fluid.default_startup_program())
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count = 0
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for data in train_reader():
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count += 1
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if count > 5:
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break
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out1, out2 = exe.run(prog,
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feed=feeder.feed(data),
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fetch_list=[hidden1_grad, hidden2_grad])
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out1_clip, out2_clip = exe.run(
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prog_clip,
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feed=feeder.feed(data),
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fetch_list=[hidden1_grad_clip, hidden2_grad_clip])
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if not ((out1.clip(
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min=CLIP_MIN, max=CLIP_MAX) == out1_clip).all() and
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(out2 == out2_clip).all()):
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exit(1)
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exit(0)
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