Udacity Android Learning Note: Lesson 4 Part A/titer1/archimedes of dry Goods shop choresSource: Https://code.csdn.net/titer1Contact: 13,073,161,968Disclaimer: This document is licensed under the following protocols: Free reprint-Non-commercial-non-derivative-retention Attribution | Creative Commons by-nc-nd 3.0, reproduced please specify the author and source.Tips:https://code.csdn.net/titer1/pat_aha/blob/master/markdown/android/SQL lesson4a-15课开始,之前
Udacity-android Study Notes: lesson 2, udacityandroidUdacity android lesson 2 Study Notes
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Udacity Android Learning Note: Lesson 4 Part B/titer1/archimedes of dry Goods shop choresSource: Https://code.csdn.net/titer1Contact: 13,073,161,968Disclaimer: This document is licensed under the following agreement: Free reprint-Non-commercial-non-derivative-retention Attribution | Creative Commons by-nc-nd 3.0, reproduced please specify the author and source.Tips:https://code.csdn.net/titer1/pat_aha/blob/master/markdown/android/4b,后期将拆分为4大小节强烈建议保留自己
Udacity Android Practice Note: Lesson 4 Part A/titer1/archimedes of dry Goods shop choresSource: Https://code.csdn.net/titer1Contact: 13,073,161,968 (SMS Best)Disclaimer: This document is licensed under the following protocols: Free reprint-Non-commercial-non-derivative-retention Attribution | Creative Commons by-nc-nd 3.0, reproduced please specify the author and source.Tips:https://code.csdn.net/titer1/pat_aha/blob/master/markdown/android/PrefaceThi
Udacity Android Practice Note: Lesson 4 Part B/titer1/archimedes of dry Goods shop choresSource: Https://code.csdn.net/titer1Contact: 13,073,161,968 (SMS Best)Disclaimer: This document is licensed under the following protocols: Free reprint-Non-commercial-non-derivative-retention Attribution | Creative Commons by-nc-nd 3.0. Reprint please indicate the author and source.Tips:https://code.csdn.net/titer1/pat_aha/blob/master/markdown/android/Summary
1. Why add pooling (pooling) to the convolutional networkIf you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.Advantages of pooling:1. Pooled operation does not increase parameters2. Experimental results show that the model with pooling is more accurateDisadvantages of pooling:1. Because the stride of t
Udacity android Practice Notes: lesson 4 part B, udacityandroidUdacity android Practice Notes: lesson 4 part B
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Udacity android Practice Notes: lesson 4 part a, udacityandroidUdacity android Practice Notes: lesson 4 part
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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
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
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
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
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
Introduction and use of Caffe-tensorflow conversion
Caffe-tensorflow can convert Caffe network definition file and pre-training parameters into TensorFlow form, including TensorFlow network structure source code and NPY format weight file.Download the source code from GitHub and enter the source directory to run conve
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 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
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