tensorflow

TensorFlow model serving on Google AI Platform online prediction too slow with instance batches

由 ∥☆過路亽．° 提交于 2020-12-12 02:52:56
问题 I'm trying to deploy a TensorFlow model to Google AI Platform for Online Prediction. I'm having latency and throughput issues . The model runs on my machine in less than 1 second (with only an Intel Core I7 4790K CPU) for a single image. I deployed it to AI Platform on a machine with 8 cores and an NVIDIA T4 GPU. When running the model on AI Platform on the mentioned configuration, it takes a little less than a second when sending only one image. If I start sending many requests, each with

AttributeError: 'str' object has no attribute 'dim' in pytorch

由 流过昼夜 提交于 2020-12-12 02:06:59
问题 I got the following error output in the PyTorch when sent model predictions into the model. Does anyone know what's going on? Following are the architecture model that I created, in the error output, it shows the issue exists in the x = self.fc1(cls_hs) line. class BERT_Arch(nn.Module): def __init__(self, bert): super(BERT_Arch, self).__init__() self.bert = bert # dropout layer self.dropout = nn.Dropout(0.1) # relu activation function self.relu = nn.ReLU() # dense layer 1 self.fc1 = nn.Linear

AttributeError: 'str' object has no attribute 'dim' in pytorch

由 孤街浪徒 提交于 2020-12-12 02:06:16
问题 I got the following error output in the PyTorch when sent model predictions into the model. Does anyone know what's going on? Following are the architecture model that I created, in the error output, it shows the issue exists in the x = self.fc1(cls_hs) line. class BERT_Arch(nn.Module): def __init__(self, bert): super(BERT_Arch, self).__init__() self.bert = bert # dropout layer self.dropout = nn.Dropout(0.1) # relu activation function self.relu = nn.ReLU() # dense layer 1 self.fc1 = nn.Linear

AttributeError: 'str' object has no attribute 'dim' in pytorch

由 痞子三分冷 提交于 2020-12-12 02:06:03
问题 I got the following error output in the PyTorch when sent model predictions into the model. Does anyone know what's going on? Following are the architecture model that I created, in the error output, it shows the issue exists in the x = self.fc1(cls_hs) line. class BERT_Arch(nn.Module): def __init__(self, bert): super(BERT_Arch, self).__init__() self.bert = bert # dropout layer self.dropout = nn.Dropout(0.1) # relu activation function self.relu = nn.ReLU() # dense layer 1 self.fc1 = nn.Linear

【12月8日更新】如何入门 TensorFlow ? “开发者出道计划”第一期话题精华内容汇总

由 和自甴很熟 提交于 2020-12-12 01:55:49
大家好！ “TensorFlow 开发者出道计划” 第一期正在火热展开中！在11月-1月，出道计划第一期围绕“如何入门 TensorFlow”，社区内上线了超级多的实用技术干货，更重磅邀请来自 CSDN 的百大热门技术博主倾囊分享成长心得。在社区的问答版块，关于 TensorFlow 的讨论也在实时火热进行中！ 为了帮助开发者伙伴更方便检索到这些优质内容，小编特地整理了本篇精华帖，为大家收录优质内容的链接直通车，帮助大家一步到位 get 到自己想要看的好内容！本篇精华帖将持续更新，建议你动动手指把它收录到自己的“收藏夹”，随时查阅哦！ 特邀 社区作者 分享学习成长心路历程 & 技巧！（持续更新中） 我是管小亮： 《关于 TensorFlow 入坑那些事儿》 文章提要： 新手入门 TensorFlow，如何学习？如何高效、快速地学习？CSDN 百万粉丝博客专家@我是管小亮 畅谈经验和见解，为你打开通向新世界的大门！ 希望像上面的作者一样，让您的个人洞见、项目实践被更多社区开发者看到，欢迎 点击这里 了解更多。 在 社区问答版块 也有了不少开发者关心的内容得到了热烈的 回复 ，看看是否也有你所关心的话题： 问题1: 从零接触 TensorFlow，我该选择1.0还是2.0版本？ 是不是感受到了这些优质回答确实有帮您解答了在入门 TensorFlow 遇到的一些学习瓶颈或是实操问题

Keras predict gives different error than evaluate, loss different from metrics

由 こ雲淡風輕ζ 提交于 2020-12-11 15:53:51
问题 I have the following problem: I have an autoencoder in Keras, and train it for a few epochs. The training overview shows a validation MAE of 0.0422 and an MSE of 0.0024. However, if I then call network.predict and manually calculate the validation errors, I get 0.035 and 0.0024. One would assume that my manual calculation of the MAE is simply incorrect, but the weird thing is that if I use an identity model (simply outputs what you input) and use that to evaluate the predicted values, the

Keras predict gives different error than evaluate, loss different from metrics

由 谁说胖子不能爱 提交于 2020-12-11 15:52:02
问题 I have the following problem: I have an autoencoder in Keras, and train it for a few epochs. The training overview shows a validation MAE of 0.0422 and an MSE of 0.0024. However, if I then call network.predict and manually calculate the validation errors, I get 0.035 and 0.0024. One would assume that my manual calculation of the MAE is simply incorrect, but the weird thing is that if I use an identity model (simply outputs what you input) and use that to evaluate the predicted values, the

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray). in trying to predict tesla stock

由 ♀尐吖头ヾ 提交于 2020-12-11 08:51:23
问题 In the end you can see that i have tried converting this into a numpy array but I don't understand why tensorflow dosen't support it? I have looked at the other related pages but none seemed to help. Is there some other format i have to do to the data in order to properly fit in model? this is what keras says: x Vector, matrix, or array of training data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data. x can

ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray). in trying to predict tesla stock

由 左心房为你撑大大i 提交于 2020-12-11 08:47:04
问题 In the end you can see that i have tried converting this into a numpy array but I don't understand why tensorflow dosen't support it? I have looked at the other related pages but none seemed to help. Is there some other format i have to do to the data in order to properly fit in model? this is what keras says: x Vector, matrix, or array of training data (or list if the model has multiple inputs). If all inputs in the model are named, you can also pass a list mapping input names to data. x can

How to predict new data with a trained neural network (Tensorflow 2.0, regression analysis)?

由 我的梦境 提交于 2020-12-11 05:17:49
问题 I am new to machine learning and using Tensorflow. I have trained a neural network for regression following the tutorial on the Tensorflow website. I have 3 input columns and 2 output columns which I have marked as "labels". The network seemingly predicts data fine when using testing data, but when I try to predict data outside the testing and training set, by importing a file with 3 input columns only, it gives me an error saying "expected dense_input to have shape (5,) but got array with
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