tensorflow pix2pix

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

In general, there are two functions for printing tensorflow variables:tf.trainable_variables () and Tf.all_variables ()The difference is:Tf.trainable_variables () refers to the variables that need to be trainedTf.all_variables () refers to all variables In general, we are more concerned with training variables that need to be trained:It is important to note that the entire graph is initialized when the variable name is output First, print the name of

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

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 a classifier and tensorflow implements classification. The examples in this article share the code used to create a classifier in TensorFlow for your reference. The details are as follows: Create a classifier for the iris dataset. Load the sample data set and implement a simple binary classifier to predict whether a flower is an iris. There are

TensorFlow variable management details, tensorflow variable details I. TensorFlow variable Management 1. TensorFLow also provides the tf. get_variable function to create or obtain variables. When tf. variable is used to create variables, its functions are basically equivalent to tf. Variable. The initialization method

Use tensorflow to build CNN and tensorflow to build cnn Convolutional Neural Networks Convolutional Neural Network (CNN) transfers the data of an image to CNN. The original coating is composed of RGB, And then CNN thickened the thickness and the length and width become smaller, each layer is stretched to form a classifier. There are several important concepts in CNN: Stride Padding Pooling Stride i

01 Simple Convolution network # "TensorFlow Combat" TensorFlow realize convolution neural network # WIN10 Tensorflow1.0.1 python3.5.3 # CUDA v8.0 cudnn-8.0-windows10-x64-v5.1 # Filen ame:sz05.01.py # Simple convolution network from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf mnist = Input_ Data.read_data_sets ("mnist_data/", o

Original address machine learning in the Cloud, with TensorFlowWednesday, MarchPosted by Slaven Bilac, software Engineer, Google analyticsmachine learning in the cloud with TensorFlowat Google, researchers collaborate closely and product teams, applying the latest advances in machine learning to Exi Sting products and Services-such asSpeech recognition in the Google app,Search in Google Photos and theSmart Reply feature in Inbox by Gmail-In order to do them more useful. A growing number of Googl

Mnist is an entry-level computer-vision dataset that contains 60,000 training data and 10,000 test data. Each sample is a variety of handwritten digital pictures below: It also contains the corresponding label for each picture, telling us this is a number. For example, the above four pictures are labeled 5,0,4,1. Mnist's official website: http://yann.lecun.com/exdb/mnist/ You can view the current maximum record for the project: http://rodrigob.github.io/are_we_there_yet/build/classification_dat

1. Overview As with the old version of TensorFlow, the model needs to be saved, and this preservation is cyclical. Because in many cases the gradient will swing around the local minimum, that is to say, in many cases, the last training model is not necessarily optimal. 2. Save the Model We can create a location where the checkpoint is saved when we build the model, and we can start by creating a folder with the following command. You can add paramet

As a result of the recent busy, until the holidays are empty, so will learn from their own knowledge to share. If there is a wrong place, please point out, thank you! At present the deep study is getting more and more fire, the related worker who learns, uses TensorFlow more and more. Recently, a Python script was used to train the model under the TensorFlow line, and the Freeze_graph tool was used to outpu

I. Recommended TWO websites TensorFlow Official Document: Https://www.tensorflow.org/install/install_windows TensorFlow Chinese Community: http://www.tensorfly.cn/tfdoc/get_started/os_setup.html Two. install TensorFlow on WindowsDirectory: Determine the TensorFlow to install Requirements

=Tf.reduce_mean (Tf.abs (A)) L2_a_loss=Tf.reduce_mean (Tf.square (A)) E1_term=tf.multiply (elastic_p1,l1_a_loss) e2_term=tf.multiply (Elastic_p2,l2_a_loss)#here A is an irregular shape that corresponds to the array form of the 3,1 loss also expands the arrays formLoss=tf.expand_dims (Tf.add (Tf.add (Tf.reduce_mean (Tf.square (y_target-model_out)), e1_term), e2_term), 0)#Initialize Variablesinit=Tf.global_variables_initializer () sess.run (init)#Gradient Descentmy_opt=Tf.train.GradientDescentOpti

(): Image_size= 224Images=TF. Variable (Tf.random_normal ([Batch_size, Image_size, Image_size,3], Dtype=Tf.float32, StdDev=1e-1)) Keep_prob=Tf.placeholder (tf.float32) predictions, Softmax, FC8, p=inference_op (images, keep_prob) init=tf.global_variables_initializer () config=TF. Configproto () Config.gpu_options.allocator_type='BFC'Sess= TF. Session (config=config) sess.run (init) time_tensorflow_run (sess, predictions, {keep_prob:1.0},"Forward") O

1. Overview A feature column is a bridge between the original data and the model. In general, the essence of artificial intelligence is to do weights and offset operations to determine the shape of the model. Before using the TensorFlow version, the data must be processed in a kind and distributed way before it can be used by the artificial intelligence model. The appearance of feature columns makes the work of data processing much easier. 2, the fun

Recently reinstalled the system, installed the TensorFlow configuration environmentSum up.Resourceshttp://blog.csdn.net/ZWX2445205419/article/details/69429518http://blog.csdn.net/u013294888/article/details/56666023Http://www.2cto.com/kf/201612/578337.htmlhttp://blog.csdn.net/10km/article/details/61915535Nvidia driver installation MethodHttps://wiki.ubuntu.com.cn/NVIDIAQuery the NVIDIA Driver modelHttp://www.nvidia.com/Download/index.aspx?lang=en-usQue

The TensorFlow and the black Tech. Google hosted the first TensorFlow developer summit in Mountain View, California, February 16, 2017 (Beijing time) 2 o'clock in the morning. Google site announced the world's leading deep learning open source Framework TensorFlow officially released the V1.0 version, and to ensure that Google's current release API interface to m

Install TensorFlow in virtualenv mode on Ubuntu This article describes how to install tensorflow in virtualenv mode on Ubuntu. Install pip and virtualenv: # Ubuntu/Linux 64-bit Sudo apt-get install python-pip python-dev python-virtualenv # Mac OS X Sudo easy_install pip Sudo pip install -- upgrade virtualenv Create a Virtualenv virtual environment: Go to the parent directory where you want to install

Brief Introduction Tensorflow-bitcoin-robot: A Bitcoin price prediction robot based on the TensorFlow lstm model. The article includes a few parts:1. Why try to do this project.2. Why did you choose this model?3. Where does the data from the model come from.4. The optimization process of the model.5. The direction in which the project can be further improved. The recent performance of the digital currency,