deeplearning4j vs tensorflow

deeplearning4j-nd4j Method Quick Index ND4J and nd4s are scientific computing libraries for the JVM and are designed for production environments, where routines run fast and have low RAM requirements. Main Features: Multi-purpose Multidimensional Array object multi-platform function, including GPU linear algebra and signal processing functions Due to the gap in usability, Java, Scala, and Clojure programmers cannot take full advantage of the most pow

In the previous blog, we used the RBM-based deep autoencoder to compress the mnist data set, which should be said to have achieved good results. Here, we replace the neural network with the traditional fully-connected feedforward neural network to compress the Mnist data set to see what the similarities and differences between the two effects are. The entire code is still implemented using deeplearning4j, and we combine it with the spark platform to f

TensorFlow Learning Notes 4: Distributed TensorFlow Brief Introduction The TensorFlow API provides cluster, server, and supervisor to support distributed training of models. The distributed training introduction about TensorFlow can refer to distributed TensorFlow. A simpl

1. Download and install Anaconda1.1 downloadDownload the Linux version from Anaconda official website (https://www.continuum.io/downloads)https://repo.continuum.io/archive/(Recommended python3.5)1.2 InstallationCD ~/downloadssudo bash anaconda2-5.0.1-linux-x86_64.sh (download the corresponding version of Python2.7 here)Ask if you want to add the Anaconda bin to the user's environment variable and select yes!Installation is complete.2. Install tensorflow2.1 set up

Install the TENSORFLOW-GPU environment: Python environment, TENSORFLOW-GPU package, CUDA,CUDNNFirst, install the PYTHON,PIP3 directly to the official website to download, download and install your favorite versionHttps://www. python. org/Tip: Remember to check the ADD environment variable when you install the last stepIn the cmd input PIP3 test PIP3 can use, can not use, manually open the path of the Python

Ubuntu1604 install tensorflow and tensorflow Operating System: ubuntu-16.04.2-desktop-amd64Tensorflow version: 1.0.0Python version: 2.7.12 Enable ssh: sudo apt install openssh-server sudo service ssh start Install pip: sudo apt-get install python-pip Install tensorflow: Github address: https://github.com/tensorflow

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

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

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,

Development environment: Mac OS 10.12.5Python 2.7.10GCC 4.2.1Mac default is no pip, install PIP.sudo easy_install pip1. Installing virtualenvsudo pip install virtualenv--upgradeCreate a working directory:sudo virtualenv--system-site-packages ~/tensorflowMake the directory, activate the sandboxCD ~/tensorflowSOURCE Bin/activateInstall TensorFlow in 2.virtualenvAfter entering the sandbox, execute the following command to install

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

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

Preface: TensorFlow There are many basic concepts to understand, the best way is to go to the official website followed by the tutorial step by step, there are some translated version, compared to see to help understand: tensorflow1.0 document translation text: One, the necessary process of building and executing the calculation diagram 1,graph (Figure calculation): see TF. Graph classUsing TensorFlow to t

Through a few routines, we gradually established a perceptual knowledge of tensorflow. This article will further from the internal principle of deep understanding, and then for reading source to lay a good foundation.1. Graph (graph)The TensorFlow calculation is abstracted as a forward graph that includes several nodes. As shown in the example:The corresponding TensorFl