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98 lines
3.4 KiB
98 lines
3.4 KiB
# Copyright (c) 2019 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 numpy as np
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def simple_fc_net_with_inputs(img, label, class_num=10):
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hidden = img
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for _ 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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act='relu',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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prediction = fluid.layers.fc(hidden, size=class_num, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def simple_fc_net(use_feed=None):
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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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return simple_fc_net_with_inputs(img, label, class_num=10)
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def fc_with_batchnorm(use_feed=None):
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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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hidden = img
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for _ in range(2):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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act='relu',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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hidden = fluid.layers.batch_norm(input=hidden)
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def bow_net(use_feed,
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dict_dim,
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is_sparse=False,
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emb_dim=128,
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hid_dim=128,
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hid_dim2=96,
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class_dim=2):
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"""
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BOW net
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This model is from https://github.com/PaddlePaddle/models:
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fluid/PaddleNLP/text_classification/nets.py
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"""
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data = fluid.layers.data(
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name="words", shape=[1], dtype="int64", lod_level=1)
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label = fluid.layers.data(name="label", shape=[1], dtype="int64")
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emb = fluid.layers.embedding(
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input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim])
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bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
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bow_tanh = fluid.layers.tanh(bow)
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fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
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fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
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prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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avg_cost = fluid.layers.mean(x=cost)
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return avg_cost
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def init_data(batch_size=32, img_shape=[784], label_range=9):
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np.random.seed(5)
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assert isinstance(img_shape, list)
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input_shape = [batch_size] + img_shape
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img = np.random.random(size=input_shape).astype(np.float32)
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label = np.array(
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[np.random.randint(0, label_range) for _ in range(batch_size)]).reshape(
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(-1, 1)).astype("int64")
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return img, label
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