tensorflow

How to configure dataset pipelines with Tensorflow make_csv_dataset for Keras Model

由 天涯浪子 提交于 2020-12-04 05:12:45
问题 I have a structured dataset(csv features files) of around 200 GB. I'm using make_csv_dataset to make the input pipelines. Here is my code def pack_features_vector(features, labels): """Pack the features into a single array.""" features = tf.stack(list(features.values()), axis=1) return features, labels def main(): defaults=[float()]*len(selected_columns) data_set=tf.data.experimental.make_csv_dataset( file_pattern = "./../path-to-dataset/Train_DS/*/*.csv", column_names=all_columns, # all

浏览器人体检测

由 一世执手 提交于 2020-12-02 07:40:37
Pose Animator：使用实时TensorFlow.js模型的SVG动画工具 下面的在手机上延时超过1秒 新的演示版 https://storage.googleapis.com/tfjs-models/demos/body-pix/index.html GitHub https://github.com/tensorflow/tfjs-models/tree/master/body-pix https://mp.weixin.qq.com/s?__biz=MzU1OTMyNDcxMQ==&mid=2247491846&idx=1&sn=0264cbf70e96414ec73d5668b7e8269f&chksm=fc1baa4ecb6c23586f796b64519cbc360339cfc621d200e61477990a9e7edc6cf2debb933f0e&mpshare=1&scene=1&srcid=1201U0paIzR1R6d4qw8hUKkj&sharer_sharetime=1606828485902&sharer_shareid=ab5aa3530015c5ae813227bf34b4fc84&key

Why Bert transformer uses [CLS] token for classification instead of average over all tokens?

由 强颜欢笑 提交于 2020-12-01 12:00:50
问题 I am doing experiments on bert architecture and found out that most of the fine-tuning task takes the final hidden layer as text representation and later they pass it to other models for the further downstream task. Bert's last layer looks like this : Where we take the [CLS] token of each sentence : Image source I went through many discussion on this huggingface issue, datascience forum question, github issue Most of the data scientist gives this explanation : BERT is bidirectional, the [CLS]

Why Bert transformer uses [CLS] token for classification instead of average over all tokens?

由 旧巷老猫 提交于 2020-12-01 12:00:35
问题 I am doing experiments on bert architecture and found out that most of the fine-tuning task takes the final hidden layer as text representation and later they pass it to other models for the further downstream task. Bert's last layer looks like this : Where we take the [CLS] token of each sentence : Image source I went through many discussion on this huggingface issue, datascience forum question, github issue Most of the data scientist gives this explanation : BERT is bidirectional, the [CLS]

Linuxer-"Linux开发者自己的媒体"第四月稿件录取和赠书名单

由 ╄→尐↘猪︶ㄣ 提交于 2020-12-01 10:35:26
原创 Linuxer Linux阅码场 2017-11-11 Linuxer已经从一个单纯的读者服务公众号转变为一个为广大用户解决linux学习，工作以及职业生涯实际问题的平台。用户参与，才能让这个平台更加实用，有效。Linuxer平台号召各路大虾一起来建设这个平台，“Linuxer”是广大linuxer的“Linuxer”。 第4个月稿件录取和赠书名单如下： 作者 赠送书 作品 明鑫 奔跑吧Linux内核》 吴锦华/明鑫: 用户态文件系统(FUSE)框架分析和实战 吴锦华 《奔跑吧linux内核》 吴锦华/明鑫: 用户态文件系统(FUSE)框架分析和实战 王玉成 《Deep Learning 深度学习》 王玉成： Android Things 第3个月稿件录取和赠书名单如下： 作者 赠送书 作品 魏永明 《微信小程序开发实战》 魏永明： MiniGUI的涅槃重生之路 郭健 《奔跑吧linux内核》 郭健： Linux内存逆向映射（reverse mapping）技术的前世今生 谢宝友 《奔跑吧linux内核》 谢宝友： 深入理解Linux RCU之一——从硬件说起 谢宝友：深入理解Linux RCU：从硬件说起之内存屏障 黄伟亮 《机器人爱好者（第4辑）》 黄伟亮： 探秘Linux的块设备和根 宋牧春 《奔跑吧linux内核》 宋牧春： Linux设备树文件结构与解析深度分析(1

What is tensorflow.python.data.ops.dataset_ops._OptionsDataset?

由 蹲街弑〆低调 提交于 2020-12-01 09:46:52
问题 I am using the Transformer code from tensorflow - https://www.tensorflow.org/beta/tutorials/text/transformer In this code, the dataset used is loaded like this - examples, metadata = tfds.load('ted_hrlr_translate/pt_to_en', with_info=True, as_supervised=True) train_examples, val_examples = examples['train'], examples['validation'] When I check the type of train_examples using : type(train_examples) I get the following as output - tensorflow.python.data.ops.dataset_ops._OptionsDataset Now I

module 'tensorflow._api.v2.train' has no attribute 'GradientDescentOptimizer'

由 ≡放荡痞女 提交于 2020-12-01 09:36:19
问题 I used Python 3.7.3 and installed tensorflow 2.0.0-alpha0,But there are some problems。such as module 'tensorflow._api.v2.train' has no attribute 'GradientDescentOptimizer' Here's all my code import tensorflow as tf import numpy as np x_data=np.random.rand(1,10).astype(np.float32) y_data=x_data*0.1+0.3 Weights = tf.Variable(tf.random.uniform([1], -1.0, 1.0)) biases = tf.Variable(tf.zeros([1])) y=Weights*x_data+biases loss=tf.reduce_mean(tf.square(y-y_data)) optimizer=tf.train

Tensorflow LinearRegressor Feature Cannot have rank 0

由 独自空忆成欢 提交于 2020-12-01 07:20:45
问题 I am following the tutorial but failed to build a linear regressor for a dataset generated on top of y=x . Here is the last part of my code, and you can find the complete source code here if you want to reproduce my error: _CSV_COLUMN_DEFAULTS = [[0],[0]] _CSV_COLUMNS = ['x', 'y'] def input_fn(data_file): def parse_csv(value): print('Parsing', data_file) columns = tf.decode_csv(value, record_defaults=_CSV_COLUMN_DEFAULTS) features = dict(zip(_CSV_COLUMNS, columns)) labels = features.pop('y')

Keras initialize large embeddings layer with pretrained embeddings

由 心已入冬 提交于 2020-12-01 06:12:50
问题 I am trying to re-train a word2vec model in Keras 2 with Tensorflow backend by using pretrained embeddings and custom corpus. This is how I initialize the embeddings layer with pretrained embeddings: embedding = Embedding(vocab_size, embedding_dim, input_length=1, name='embedding', embeddings_initializer=lambda x: pretrained_embeddings) where pretrained_embeddings is a big matrix of size vocab_size x embedding_dim This works as long as pretrained_embeddings is not too big. In my case

Keras: change learning rate

由 倖福魔咒の 提交于 2020-12-01 03:41:40
问题 I'm trying to change the learning rate of my model after it has been trained with a different learning rate. I read here, here, here and some other places i can't even find anymore. I tried: model.optimizer.learning_rate.set_value(0.1) model.optimizer.lr = 0.1 model.optimizer.learning_rate = 0.1 K.set_value(model.optimizer.learning_rate, 0.1) K.set_value(model.optimizer.lr, 0.1) model.optimizer.lr.assign(0.1) ... but none of them worked! I don't understand how there could be such confusion
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