一.源代码下载 代码最初来源于Github:https://github.com/vijayvee/Recursive-neural-networks-TensorFlow,代码介绍如下:“This repository contains the implementation of a single hidden layer Recursive Neural Network.Implemented in python using TensorFlow. Used the trained mode
本随笔记载与2019年1月23日,若随着技术发展,本随笔记录的困难被攻克也是可能的. 参考(https://www.reddit.com/r/docker/comments/86vzna/gpu_access_from_docker_container_windows_10/) Well you need to understand that direct access to the graphics card is done by a driver. If your host system i
keras+tensorflow: based on AMD GPU https://rustyonrampage.github.io/deep-learning/2018/10/18/tensorfow-amd.html 在win7上简单试验了一下,会有版本匹配的问题,可能会出现keras中某些方法不支持的问题. ---未完待续---
列出可用GPU from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) from keras import backend as K K.tensorflow_backend._get_available_gpus() 切换 import os os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID" # The GP
在根据教程http://blog.csdn.net/sb19931201/article/details/53648615安装好全部的时候,却无情的给我抛了几个错: 1.AttributeError: module 'tensorflow' has no attribute 'device' 这貌似是我先pip了tensorflow-gpu的包,再添加cuDnn库. 2.ImportError: Could not find 'cudart64_80.dll'. TensorFl