tensorflow

Plot validation loss in Tensorflow Object Detection API

由 做~自己de王妃 提交于 2021-02-08 03:27:47
问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

Plot validation loss in Tensorflow Object Detection API

由 江枫思渺然 提交于 2021-02-08 03:27:02
问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

Plot validation loss in Tensorflow Object Detection API

由 删除回忆录丶 提交于 2021-02-08 03:26:27
问题 I'm using Tensorflow Object Detection API for detection and localization of one class object in images. For these purposes I use pre-trained faster_rcnn_resnet50_coco_2018_01_28 model. I want to detect under/overfitting after training of the model. I see training loss, but after evaluating Tensorboard only shows mAP and Precision metrics and no loss. Is this possible to plot a validation loss on Tensorboard too? 回答1: There is validation loss. Assuming you're using the latest API, the curve

24 目标识别MobileNet

由 為{幸葍}努か 提交于 2021-02-08 02:44:59
下载 代码和MobileNet训练模型可以从以下位置下载： https://github.com/djmv/MobilNet_SSD_opencv https://github.com/chuanqi305/MobileNet-SSD http://www.ebenezertechs.com/mobilenet-ssd-using-opencv-3-4-1-deep-learning-module-python/ https://github.com/djmv/MobilNet_SSD_opencv 网友加速 在Raspberry Pi上设置TensorFlow对象检测API的教程 https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi#6-detect-objects https://github.com/EdjeElectronics/TensorFlow-Object-Detection-on-the-Raspberry-Pi https://www.youtube.com/watch?v=gGqVNuYol6o&feature=youtu.be https://blog.csdn.net/weixin_43558453/article/details

Converting Array of Lists to Keras Input

由 谁说胖子不能爱 提交于 2021-02-08 01:49:30
问题 Given an array of the form array([list([21603, 125, 737, 579, 2065, 10399, 1175, 0, 0, 0]), ... list([1896, 3917, 498, 296, 1452, 523, 754, 450, 3795, 341])], dtype=object) How do you prepare it to be consumed by a Keras model in TensorFlow 2.0 RC0? In its current form it throws the error ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list). and I can't seem to get it in the form I'm familiar with, array([[21603, 125, 737, 579, 2065, 10399, 1175, 0, 0, 0], ..

How to make predictions using a model that requires an input shape with more than two dimensions using MLflow?

由 雨燕双飞 提交于 2021-02-07 23:00:16
问题 I'm trying to implement a tensorflow (keras) based model into mlflow while learning how it works and if it suite our needs. I'm trying to implement the Fashion MNIST example from tensorflow website Here the link I was able to train and to log the model successfully into mlflow using this code: import mlflow import mlflow.tensorflow import mlflow.keras # TensorFlow and tf.keras import tensorflow as tf from tensorflow import keras # Helper libraries import numpy as np import matplotlib.pyplot

login password required to access jupyter notebook running in nvidia-docker container

由 為{幸葍}努か 提交于 2021-02-07 21:51:12
问题 I run this command in the following order in order to run tensoflow in docker container after successful installation in Ubuntu 16.04 (NVIDIA GPU GeFORCE 840M) . 1.sudo service docker start 2.sudo nvidia-docker run -it -p 8888:8888 gcr.io/tensorflow/tensorflow:latest-gpu Then I try to access jupyter in firefox browser by typing localhost:8888 and I am asked to enter the login password in the browser. What is the solution? 回答1: add option "-e PASSWORD=password" to set the environment variable.

零基础Python学习路线图，Python学习不容错过

由 不羁岁月 提交于 2021-02-07 21:35:46
最近有很多人在问小编Python培训方面的问题，一开始小编还挺疑惑，后来特地请教了一下度娘，果真互联网行业的风向变了，近几年Python的受欢迎程度可谓是扶摇直上，当然了学习的人也是愈来愈多。一些学习Python的小白在学习初期，总希望能够得到一份Python学习路线图，小编经过多方汇总为大家汇总了一份Python学习路线图。 Python学习路线一：Python基础 必学知识：【Linux基础】【Python基础语法】【Python字符串】【文件操作】【异常处理】【Python面向对象】【项目实战】 路线讲解：该路线循序渐进，科学合理，帮助学习者建立正确的编程思想，具备基本的编程能力; Python学习路线二：Python高级编程 必学知识：【Python平台迁移Linux】【Python常用第三方库】【Python高级语法】【Python正则表达式】【网路编程】【系统编程】【数据结构与算法】【项目实战】 路线讲解：该路线强调数据结构和算法的学习，着重提升学习者的编程核心能力;使学习者能够熟练掌握Python高级用法及网络相关知识，能够独立承担Python网络相关的开发; Python学习路线三：web前端开发 必学知识：【HTML】【CSS】【UI基础】【JavaScript】【DOM】【事件】【jQuery】【混合开发】【项目实战】 路线讲解

TensorFlow - show all variables in session

由 浪尽此生 提交于 2021-02-07 21:24:24
问题 I have played a little import tensorflow as tf x = tf.Variable([1.0, 2.0]) initializer = tf.global_variables_initializer() session.run(initializer) x <tf.Variable 'Variable:0' shape=(2,) dtype=float32_ref> y = 2 * x y <tf.Tensor 'mul:0' shape=(2,) dtype=float32> z = y + 1 z <tf.Tensor 'add:0' shape=(2,) dtype=float32> v = session.run(x) sess.run(initializer) v = sess.run(x) print (v) [ 1. 2.] v1 = sess.run(z) print (v1) [ 3. 5.] v = sess.run(x) I have 3 variables x,y,z.Is it possible to show

TensorFlow - show all variables in session

由 一个人想着一个人 提交于 2021-02-07 21:23:16
问题 I have played a little import tensorflow as tf x = tf.Variable([1.0, 2.0]) initializer = tf.global_variables_initializer() session.run(initializer) x <tf.Variable 'Variable:0' shape=(2,) dtype=float32_ref> y = 2 * x y <tf.Tensor 'mul:0' shape=(2,) dtype=float32> z = y + 1 z <tf.Tensor 'add:0' shape=(2,) dtype=float32> v = session.run(x) sess.run(initializer) v = sess.run(x) print (v) [ 1. 2.] v1 = sess.run(z) print (v1) [ 3. 5.] v = sess.run(x) I have 3 variables x,y,z.Is it possible to show
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