tensorflow | 易学教程

How do I know the maximum number of threads per block in python code with either numba or tensorflow installed?

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问题 Is there any code in python with either numba or tensorflow installed? For example, if I would like to know the GPU memory info, I can simply use: from numba import cuda gpus = cuda.gpus.lst for gpu in gpus: with gpu: meminfo = cuda.current_context().get_memory_info() print("%s, free: %s bytes, total, %s bytes" % (gpu, meminfo[0], meminfo[1])) in numba. But I can not find any code that gives me the maximum threads per block info. I would like the code to detect the maximum number of threads

从2020昇腾计算产业峰会，看产业生态如何加速AI普惠

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文 | 曾响铃来源 | 科技向令说（xiangling0815）最近，以“昇腾万里，让智能无所不及”为主题的首届昇腾计算产业峰会在上海举办，业内专家、行业先锋、生态伙伴约500多人见证两年后昇腾AI计算产业的全面落地进程。这个峰会，距离2018年华为Ascend（昇腾）系列产品面世，整整两年时间。两年间，华为持续投入AI战略，推动昇腾计算产业生态快速发展。随着此次峰会上《昇腾计算产业发展白皮书》、《昇腾万里伙伴计划》的发布，昇腾计算产业的全面繁荣已经在行业共识基础上按下了启动键。而在业内引发广泛关注的这次峰会，也在AI加速落地的时代勾勒出昇腾通过产业生态的强化推动“AI普惠”的图景——这正是华为两年前发布AI战略和全栈全场景AI解决方案时的重要目标。市场数据显示，虽然AI在某些特定领域特定场景下准确率已经超过人类，但其全行业渗透率仅有4%，在中国市场，只有10%的B2C应用涉及AI。这些数字，距离AI走向普罗大众，让每个人、每个家庭、每个组织都能享受到人工智能的价值，还有很大的距离。这个距离，恰恰是昇腾计算产业的价值空间。 01 全栈技术体系，让昇腾计算产业生态拥有全面且灵活的技术支撑这次昇腾峰会主要动作都聚焦在生态构建上，在谈这些生态动作之前，有必要对昇腾当下已有的技术底子做一个全面剖析。总体来看，在昇腾生态的主要推动者华为营造下

Unknown number of steps - Training convolution neural network at Google Colab Pro

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问题 I am trying to run (training) my CNN at Google Colab Pro , when I run my code, all is allright, but It does not know the number of steps, so an infinite loop is created. Mounted at /content/drive 2.2.0-rc3 Found 10018 images belonging to 2 classes. Found 1336 images belonging to 2 classes. WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen. Epoch 1/300 8/Unknown - 364s 45s/step - loss: 54.9278 - accuracy: 0.5410 I am

4 个场景揭秘，如何低成本让容器化应用 Serverless 化？

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作者 | changshuai 来源 | Serverless 公众号 FaaS 的门槛 Serverless 形态的云服务帮助开发者承担了大量复杂的扩缩容、运维、容量规划、云产品打通集成等责任，使得开发者可以专注业务逻辑、提高交付速度 (Time-to-market) ，持续优化成本。Function-as-a-Service (FaaS) 作为云上最早也是应用最广泛的 Serverless 计算形态，在几年的时间内吸引了大批开发者，逐渐建立了 Serverless 优先的选型逻辑。然而从传统应用迁移到 FaaS 在开发者体验上还面临诸多挑战：环境不统一：各厂商定义的交付物格式，运行环境兼容性、丰富度都不尽相同，需要开发者适配，甚至重新编译；学习成本：打包依赖库、构建成压缩代码包和熟悉的开发部署方式不同；服务限制：如代码包限制在百 MB 级别，迫使交付物代码依赖分离，加大管理和发布难度；交付物缺乏版本管理：格式不标准，最佳实践不统一，需要开发者自行负责；生态不成熟：缺少流行开源工具（如 CI/CD 流水线）的支持和集成。另一方面，容器在可移植性和交付敏捷性上实现了颠覆式创新。围绕容器的生态沉淀非常丰富且成熟，被广泛接受使用，应用容器化正在快速成为开发和部署的事实标准。然而容器本身并没有减轻运维、扩缩容、闲置成本、和云服务集成等难题。函数计算支持容器镜像

How to run Tensorflow GPU in Pycharm?

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问题 I want to run Tensorflow GPU in Pycharm on Linux Mint. I tried some guides like these https://medium.com/@p.venkata.kishore/install-anaconda-tenserflow-gpu-keras-and-pycharm-on-windows-10-6bfb39630e4e https://medium.com/@kekayan/step-by-step-guide-to-install-tensorflow-gpu-on-ubuntu-18-04-lts-6feceb0df5c0 I run this code import tensorflow as tf sess = tf.Session(config=tf.ConfigProto(log_device_placement=True)) I have got this error Traceback (most recent call last): File "/home/alex

Show examples in tensorflow datasets not working

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问题 I want to load the tensorflow flowers dataset and visualize it using tfds.show_examples. import tensorflow as tf import numpy as np import matplotlib.pyplot as plt import tensorflow_hub as hub import tensorflow_datasets as tfds from tensorflow.keras import layers import logging logger = tf.get_logger() logger.setLevel(logging.ERROR) (train_examples, validation_examples), info = tfds.load( 'tf_flowers', with_info=True, as_supervised=True, split=['train[:70%]', 'train[70%:]'], ) fig = tfds.show

TensorFlow Federated: How can I write an Input Spec for a model with more than one input

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问题 I'm trying to make an image captioning model using the federated learning library provided by tensorflow, but I'm stuck at this error Input 0 of layer dense is incompatible with the layer: : expected min_ndim=2, found ndim=1. this is my input_spec: input_spec=collections.OrderedDict(x=(tf.TensorSpec(shape=(2048,), dtype=tf.float32), tf.TensorSpec(shape=(34,), dtype=tf.int32)), y=tf.TensorSpec(shape=(None), dtype=tf.int32)) The model takes image features as the first input and a list of

TensorFlow Federated: How can I write an Input Spec for a model with more than one input

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Unknown regularizer: L2 in tensorflowjs

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问题 I have trained a model in python using model reg = 0.000001 model = Sequential() model.add(Dense(24, activation='tanh', name='input_dense', input_shape=input_shape)) model.add(GRU(24, activation='tanh', recurrent_activation='sigmoid', return_sequences=True, kernel_regularizer=regularizers.l2(reg), recurrent_regularizer=regularizers.l2(reg), reset_after=False)) model.add(Flatten()) model.add(Dense(2, activation='softmax')) But when I converted this model using "tensorflowjs_converter --input

Keras CNN for non-image matrix

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问题 I have recently started learning about Deep Learning and Reinforcement Learning, and I am trying to figure out how to code a Convolutional Neural Network using Keras for a matrix of 0s and 1s with 10 rows and 3 columns. The input matrix would look like this for example [ [1, 0, 0], [0, 1, 0], [0, 0, 0], ... ] The output should be another matrix of 0s and 1s, different from the aforementioned input matrix and with a different number of rows and columns. The location of 0s and 1s in the output

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