TensorFlow can identify the image files that can be used via NumPy, using TF. Variable or tf.placeholder is loaded into the tensorflow, or it can be read by a function (Tf.read), and when there are too many image files, the pipeline is usually read using the method of the queue. Here are two ways to generate TensorFlow image formats, which provide input and outpu
TensorFlow and tensorflow
Overview
The newly uploaded mcnn contains complete data read/write examples. For details, refer.
The official website provides three methods for Tensorflow to read data:
Feeding: each step of TensorFlow execution allows Python code to supply data.
Read data from a file: at the beginning o
software environment used in the study. For the last 4 years, open source software Torch7, the machine learning Library, has been our primary research platform, combining the perfect flexibility and very fast runtime execution to ensure rapid modeling. Our team is proud to have contributed to the open source project, which has evolved from the occasional bug fix to being the core maintainer of several key modules. With Google ' s recent open source release oftensorflow, we INITiated a project t
The TensorFlow model is used to store/load the tensorflow model.
TensorFlow model saving/loading
When we use an algorithm model online, we must first save the trained model. Tensorflow saves models in a different way than sklearn. sklearn is very direct. the dump and load methods of sklearn. externals. joblib can be sa
TensorFlow is a deep learning package developed by Google and is currently only supported on Linux and OSX. But this fall may have a Windows-enabled version of it, so for developers who use Windows, there's no need to wait for the fall or go to Linux and OSX TensorFlow. There are two ways to run on Windows, one is to install the virtual machine and install the Ubuntu system, install
Readers may recall the Tf.nn module in this series (ii) and (vi), the most concerned of which is the conv2d function.First, the blog (ii) MNIST routine convolutional.py key source list: DEF model (data, Train=false): "" "the model definition. " " # 2D convolution, with ' same ' padding (i.e. the output feature map has # the same size as the input). Note that {strides} is a 4D array whose # shape matches the data layout: [image index, y, x, depth]. CONV = tf.nn.conv2d (data,
TensorFlow Neural Network Optimization Strategy Learning, tensorflow Network Optimization
During the optimization of the neural network model, we will encounter many problems, such as how to set the learning rate. We can quickly approach the optimal solution in the early stage of training through exponential attenuation, after training, the system enters the optimal region stably. For the over-fitting probl
Spark Communication Module
1, Spark Cluster Manager can have local, standalone, mesos, yarn and other deployment methods, in order to
Centralized communication mode
1, RPC remote produce call
Spark Communication mechanism:
The advantages and characteristics of Akka are as follows:
1, parallel and distributed: Akka in design with asynchronous communication and dis
TensorFlow realize Classic Depth Learning Network (4): TensorFlow realize ResNet
ResNet (Residual neural network)-He Keming residual, a team of Microsoft Paper Networks, has successfully trained 152-layer neural networks using residual unit to shine on ILSVRC 2015 , get the first place achievement, obtain 3.57% top-5 error rate, the effect is very outstanding. The structure of ResNet can accelerate the tra
Tensorflow creates variables and searches for variables by name. tensorflow Variables
Environment: Ubuntu14.04, tensorflow = 1.4 (bazel source code installation), Anaconda python = 3.6
There are two main methods to declare variables:Tf. VariableAndTf. get_variable, The biggest difference between the two is:
(1) tf. Variable is a class with many attribute function
Use tensorflow to implement the elastic network regression algorithm and tensorflow Algorithm
This article provides examples of tensorflow's implementation of the elastic network Regression Algorithm for your reference. The specific content is as follows:
Python code:
# Using tensorflow to implement an elastic network algorithm (multi-variable) # using the iris d
Install spark
Spark must be installed on the master, slave1, and slave2 machines.
First, install spark on the master. The specific steps are as follows:
Step 1: Decompress spark on the master:
Decompress the package directly to the current directory:
In this case, create the spa
Step 1: software required by the spark cluster;
Build a spark cluster on the basis of the hadoop cluster built from scratch in Articles 1 and 2. We will use the spark 1.0.0 version released in May 30, 2014, that is, the latest version of spark, to build a spark Cluster Based
Step 1: Test spark through spark Shell
Step 1:Start the spark cluster. This is very detailed in the third part. After the spark cluster is started, webui is as follows:
Step 2:Start spark shell:
In this case, you can view the shell in the following Web console:
Step 3:Co
Install spark
Spark must be installed on the master, slave1, and slave2 machines.
First, install spark on the master. The specific steps are as follows:
Step 1: Decompress spark on the master:
Decompress the package directly to the current directory:
In this case, create the
the bottom of the wiki site2. As we said earlier, we are using our own standalone Cluster manager, so just point3. After entering the tutorial, the first step is to copy and paste, if not git, follow the prompts to install git;The second step is not required because you have installed spark; The third step in the instruction, or you will find that you do not open (I will not open ...) ), you can choose here: Tens
Tensorflow simple verification code recognition application, tensorflow Verification Code
Simple Tensorflow verification code recognition application for your reference. The specific content is as follows:
1. Tensorflow Installation MethodI will not go into details here.
2. Training setAs well as testing and the follow
TensorFlow is used for simple linear regression and gradient descent examples. tensorflow gradient
Linear regression is supervised learning. Therefore, the method and supervised learning should be the same. First, a training set is given and a linear function is learned based on the training set, then, test whether the function is trained (that is, whether the function is sufficient to fit the training set
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