best tensorflow course

The first blog, the main record of the installation of deep Learning Framework (TensorFlow), installed very simple (a word), started I thought it was troublesome, no n card, do not know how to start. Okay, here we are.I generally like the pydev of Eclipse (personal feeling is very useful), then Python is mainly Anconda (Tsinghua Mirror), loading TensorFlow1. Download Anconda3, Baidu ancond Tsinghua Mirror, download anconda3-cp35-cp35m-xxxx, download t

Official website: http://tensorflow.org/Installation steps:1, sudo apt-get install Python-pip python-dev python-virtualenv2, virtualenv--system-site-packages ~/tensorflow3. CD ~/tensorflow4. Source Bin/activate # If using bash5, (TensorFlow) $ pip Install TENSORFLOW-0.5.0-CP27-NONE-LINUX_X86_64.WHLTest:1. Open the terminal input CD TensorFlow2. SOURCE Bin/activate3. Python4. Enter the following example afte

TensorFlow learning --- getting started (1) ----- MNIST machine learning, References: http://www.tensorfly.cn/tfdoc/tutorials/mnist_beginners.html Data: http://wiki.jikexueyuan.com/project/tensorflow-zh/tutorials/mnist_download.html Environment: windows + Python3.5 + tensorflow Python code From tensorflow. examples. tu

In deep learning, regardless of the learning framework, we encounter an important problem, that is, after training, how to store the depth of the network parameters. How these network parameters are invoked at the time of the test. In response to these two questions, this blog post explores how TensorFlow solves them. This blog is divided into three parts, the first is to explain tensorflow related function

first, to enter the Anaconda website Download https://www.anaconda.com/download/ Everyone according to their own computer configuration download the corresponding version, I download here is the version of python3.6version 64bit After downloading and installing, open CMD, enter ' Conda--version ', if output the following information 4.3.21 Anaconda installation was successful. 2. Install TensorFlow Because the foreign mirror download is s

In a previous tutorial series I went over some of the theory behind recurrent neural (Networks) and the Rnns N of a simple RNN from scratch. That's a useful exercise, but in practice we do libraries like tensorflow with high-level primitives for dealing S. With this using an RNN should is as easy as calling a function, right? Unfortunately that ' s not quite the case. In this post I want the some of the best practices for working with Rnns in

The previous article has introduced the implementation of convolution, this article we learn the deconvolution principle, again, after understanding the deconvolution principle, in the back hand-written Python code to implement the deconvolution. 1 Inverse convolution principle The deconvolution principle does not work well with text descriptions, where the deconvolution process is described directly in a simple example. Suppose the input is as follows: [[1,0,1], [0,2,1], [1,1,0]] The deconvo

This book is published by only cloud technology Caicloud, the main content is familiar with the basic structure of TensorFlow framework and practical application in the field of depth learning.For specific code see:1. Official:Caicloud/tensorflow-tutorial:example tensorflow codes and Caicloud TensorFlow as a Service de

3 Ways of reading data There are 3 ways to read data in a TensorFlow program:Supply data (feeding): At each step in the TensorFlow program, let the Python code supply the data.Reading data from a file: At the beginning of the TensorFlow graph, let an input pipeline read the data from the file.Preload data: Define constants or variables in the

TensorFlow Serving,gpu TensorFlow serving is an open source tool that is designed to deploy a trained model for inference.TensorFlow serving GitHub AddressThis paper mainly introduces the installation of TensorFlow serving and supports the GPU model. Install dependent Bazel TensorFlow serving requires 0.4.5 above Bazel

This article references from: The Python-tensorflow Tutorial series TensorFlow Getting Started: Using graphs to represent computational tasks. Executes the diagram in the context of what is referred to as a conversation (session). Use tensor (tensor) to represent the data. Maintains state through variable (Variable). Use feeds and fetches to assign values to or fetch data from any operation (arbitrary opera

Contact TensorFlow Small white, online tutorials a lot, image classification should belong to a more classic example, especially Google pushed slim, but the online tutorial omitted many details will lead to run, after debugging finally ran out The result is OK, share My environment, cuda8.0+cudnn5.1+python2.7. About TENSORFLOW,CUDA+CUDNN Installation Recommended Tutorials: http://blog.csdn.net/xierhacker/ar

the output order of magnitude The tf.nn.conv2d function has four parameters, the first dimension is the input image, the second dimension is the convolution layer weight, the third dimension is the step of different dimensions (in CNN, the first and fourth dimensions are fixed to 1), and the fourth dimension is filled (same represents full 0 padding, valid is not filled). Depth of the layer: for example, an input image is 28*28*3, where 3 represents the depth, that is (R,G,B) The depth of the c

TensorFlow provides a number of commonly used image processing interface, allowing us to easily manipulate the image data, the following first shows a piece of the original image of the code, and then on this basis, practice tensorflow different APIs.Show original picture1 ImportMatplotlib.pyplot as Plt2 ImportTensorFlow as TF3 4Raw_data = Tf.gfile.FastGFile ('./new.jpg','RB'). Read ()5 6 With TF. Session (

I machine for 64-bit Win7To install Python first, it is important to note that Tennsorflow to use the PYTHON3.0 series version cannot use the 2.0 series version, but the TensorFlow installation package currently does not support Python 3.6 for the Windows version.The diagram is a view of the installation package results currently supported by TensorFlow (linked to HTTPS://PYPI.PYTHON.ORG/PYPI/