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226 lines
8.0 KiB
226 lines
8.0 KiB
# Copyright (c) 2020 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 paddle
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import paddle.fluid as fluid
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import paddle.fluid.layers as layers
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import numpy as np
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import os
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import shutil
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import unittest
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import math
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def conv_bn_layer(input, num_filters, filter_size, stride=1, groups=1,
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act=None):
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conv = fluid.layers.conv2d(
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input=input,
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num_filters=num_filters,
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filter_size=filter_size,
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stride=stride,
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padding=(filter_size - 1) // 2,
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groups=groups,
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act=None,
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bias_attr=False)
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return fluid.layers.batch_norm(
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input=conv,
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act=act, )
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def shortcut(input, ch_out, stride, is_first):
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ch_in = input.shape[1]
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if ch_in != ch_out or stride != 1 or is_first == True:
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return conv_bn_layer(input, ch_out, 1, stride)
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else:
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return input
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def bottleneck_block(input, num_filters, stride):
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conv0 = conv_bn_layer(
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input=input, num_filters=num_filters, filter_size=1, act='relu')
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conv1 = conv_bn_layer(
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input=conv0,
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num_filters=num_filters,
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filter_size=3,
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stride=stride,
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act='relu')
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conv2 = conv_bn_layer(
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input=conv1, num_filters=num_filters * 4, filter_size=1, act=None)
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short = shortcut(input, num_filters * 4, stride, is_first=False)
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return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
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def basic_block(input, num_filters, stride, is_first):
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conv0 = conv_bn_layer(
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input=input,
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num_filters=num_filters,
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filter_size=3,
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act='relu',
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stride=stride)
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conv1 = conv_bn_layer(
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input=conv0, num_filters=num_filters, filter_size=3, act=None)
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short = shortcut(input, num_filters, stride, is_first)
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return fluid.layers.elementwise_add(x=short, y=conv1, act='relu')
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def build_network(input, layers=50, class_dim=1000):
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supported_layers = [18, 34, 50, 101, 152]
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assert layers in supported_layers
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depth = None
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if layers == 18:
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depth = [2, 2, 2, 2]
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elif layers == 34 or layers == 50:
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depth = [3, 4, 6, 3]
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elif layers == 101:
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depth = [3, 4, 23, 3]
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elif layers == 152:
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depth = [3, 8, 36, 3]
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num_filters = [64, 128, 256, 512]
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with fluid.device_guard("cpu"):
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conv = conv_bn_layer(
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input=input, num_filters=64, filter_size=7, stride=2, act='relu')
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conv = fluid.layers.pool2d(
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input=conv,
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pool_size=3,
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pool_stride=2,
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pool_padding=1,
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pool_type='max')
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if layers >= 50:
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for block in range(len(depth)):
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with fluid.device_guard("gpu:0"):
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for i in range(depth[block]):
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conv = bottleneck_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1)
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with fluid.device_guard("gpu:0"):
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pool = fluid.layers.pool2d(
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input=conv, pool_size=7, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=pool,
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size=class_dim,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv)))
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else:
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for block in range(len(depth)):
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with fluid.device_guard("gpu:0"):
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for i in range(depth[block]):
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conv = basic_block(
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input=conv,
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num_filters=num_filters[block],
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stride=2 if i == 0 and block != 0 else 1,
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is_first=block == i == 0)
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with fluid.device_guard("gpu:0"):
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pool = fluid.layers.pool2d(
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input=conv, pool_size=7, pool_type='avg', global_pooling=True)
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stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
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out = fluid.layers.fc(
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input=pool,
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size=class_dim,
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param_attr=fluid.param_attr.ParamAttr(
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initializer=fluid.initializer.Uniform(-stdv, stdv)))
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return out
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class TestPipeline(unittest.TestCase):
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""" TestCases for Pipeline Training. """
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def _run(self, debug):
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main_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(main_prog, startup_prog):
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with fluid.device_guard("cpu"):
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image = fluid.layers.data(
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name="image", shape=[3, 224, 224], dtype="float32")
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label = fluid.layers.data(
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name="label", shape=[1], dtype="int64")
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data_loader = fluid.io.DataLoader.from_generator(
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feed_list=[image, label],
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capacity=64,
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use_double_buffer=True,
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iterable=False)
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fc = build_network(image, layers=50)
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with fluid.device_guard("gpu:0"):
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out, prob = fluid.layers.softmax_with_cross_entropy(
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logits=fc, label=label, return_softmax=True)
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loss = fluid.layers.mean(out)
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acc_top1 = fluid.layers.accuracy(input=prob, label=label, k=1)
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acc_top5 = fluid.layers.accuracy(input=prob, label=label, k=5)
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base_lr = 0.1
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passes = [30, 60, 80, 90]
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total_images = 1281167
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steps_per_pass = total_images // 128
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bd = [steps_per_pass * p for p in passes]
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lr = [base_lr * (0.1**i) for i in range(len(bd) + 1)]
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lr_val = fluid.layers.piecewise_decay(boundaries=bd, values=lr)
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optimizer = fluid.optimizer.MomentumOptimizer(
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lr_val,
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momentum=0.9,
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regularization=fluid.regularizer.L2Decay(1e-4))
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optimizer = fluid.optimizer.PipelineOptimizer(
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optimizer, num_microbatches=2)
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optimizer.minimize(loss)
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def train_reader():
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for _ in range(4):
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img = np.random.random(size=[3, 224, 224]).astype('float32')
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label = np.random.random(size=[1]).astype('int64')
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yield img, label
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data_loader.set_sample_generator(train_reader, batch_size=1)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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exe.run(startup_prog)
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data_loader.start()
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exe.train_from_dataset(main_prog, debug=debug)
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def test_pipeline(self):
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self._run(False)
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self._run(True)
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def test_pipeline_noneoptimizer(self):
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with fluid.device_guard("gpu:0"):
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x = fluid.layers.data(
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name='x', shape=[1], dtype='int64', lod_level=0)
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y = fluid.layers.data(
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name='y', shape=[1], dtype='int64', lod_level=0)
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emb_x = layers.embedding(
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input=x,
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param_attr=fluid.ParamAttr(name="embx"),
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size=[10, 2],
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is_sparse=False)
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fc = layers.fc(input=emb_x,
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name="fc",
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size=1,
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num_flatten_dims=1,
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bias_attr=False)
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loss = layers.reduce_mean(fc)
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optimizer = fluid.optimizer.SGD(learning_rate=0.5)
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with self.assertRaises(ValueError):
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optimizer = fluid.optimizer.PipelineOptimizer(
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dict(), num_microbatches=2)
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if __name__ == '__main__':
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unittest.main()
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