卷积神经网络的应用分析
【源码】卷积神经网络在tensorflow文本分类中的应用
this code belongs to the implementing a cnn for text classification in tensorflow blog post.
it is slightly simplified implementation of kim's convolutional neural networks for sentence classification paper in tensorflow.
requirements
python 3
tensorflow > 0.12
numpy
training
print parameters:
./train.py --helpoptional arguments: -h, --help show this help message and exit --embedding_dim embedding_dim dimensionality of character embedding (default: 128) --filter_sizes filter_sizes comma-separated filter sizes (default: '3,4,5') --num_filters num_filters number of filters per filter size (default: 128) --l2_reg_lambda l2_reg_lambda l2 regularizaion lambda (default: 0.0) --dropout_keep_prob dropout_keep_prob dropout keep probability (default: 0.5) --batch_size batch_size batch size (default: 64) --num_epochs num_epochs number of training epochs (default: 100) --evaluate_every evaluate_every evaluate model on dev set after this many steps (default: 100) --checkpoint_every checkpoint_every save model after this many steps (default: 100) --allow_soft_placement allow_soft_placement allow device soft device placement --noallow_soft_placement --log_device_placement log_device_placement log placement of ops on devices --nolog_device_placement train:
./train.py evaluating
./eval.py --eval_train --checkpoint_dir=./runs/1459637919/checkpoints/ replace the checkpoint dir with the output from the training. to use your own data, change the eval.py script to load your data.
references
convolutional neural networks for sentence classification
a sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification
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this code belongs to the implementing a cnn for text classification in tensorflow blog post.
it is slightly simplified implementation of kim's convolutional neural networks for sentence classification paper in tensorflow.
requirements
python 3
tensorflow > 0.12
numpy
training
print parameters:
./train.py --helpoptional arguments: -h, --help show this help message and exit --embedding_dim embedding_dim dimensionality of character embedding (default: 128) --filter_sizes filter_sizes comma-separated filter sizes (default: '3,4,5') --num_filters num_filters number of filters per filter size (default: 128) --l2_reg_lambda l2_reg_lambda l2 regularizaion lambda (default: 0.0) --dropout_keep_prob dropout_keep_prob dropout keep probability (default: 0.5) --batch_size batch_size batch size (default: 64) --num_epochs num_epochs number of training epochs (default: 100) --evaluate_every evaluate_every evaluate model on dev set after this many steps (default: 100) --checkpoint_every checkpoint_every save model after this many steps (default: 100) --allow_soft_placement allow_soft_placement allow device soft device placement --noallow_soft_placement --log_device_placement log_device_placement log placement of ops on devices --nolog_device_placement train:
./train.py evaluating
./eval.py --eval_train --checkpoint_dir=./runs/1459637919/checkpoints/ replace the checkpoint dir with the output from the training. to use your own data, change the eval.py script to load your data.
references
convolutional neural networks for sentence classification
a sensitivity analysis of (and practitioners' guide to) convolutional neural networks for sentence classification
分布式无纸化交互系统如何支持移动设备和远程办公？
存储芯片减产，AI芯片暴增！
中青宝李逸伦当选深圳市大数据产业促进会元宇宙专委会主任
一位老IT工程师眼中的理想
Flyme8疑似将在7月10日正式发布
卷积神经网络的应用分析
差分跳频的解调窗口同步算法
5G你太美!联发科高端芯片梦想要成
射频PA常见指标和测试方法
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小米6遭遇最强劲敌一加5!小米6与一加5有什么区别?小米6与一加5区别对比评测
贾跃亭大力夸赞FF 91 认为其为全球超豪华电动车续航之王
什么是静止轨道(GEO)卫星系统
一文汇总PCBA贴片不良的原因
华为官宣鸿蒙OS2.0手机Beta版本
关于输入纹波测量和滤波
国产特斯拉在电池的选用上成迷 松下预估不会采用其制造的车用电池
人工智能发声 将引领市场演变和转型
48V一体式伺服足以应对大多数工控项目
iQOO手机拆解：热管+铜箔+石墨导热膜该有的都有了