keras resnet

Environment Construction Download Anaconda Change Anaconda Package management source to Tsinghua Mirror source CMD input: Conda Config--add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ Conda config--set show_channel_urls Yes change pip source to Tsinghua Mirror C:/USER/PIP, new file Pip.ini, as follows: [Global] Index-url = Https://pypi.tuna.tsinghua.edu.cn/simple Add Jupyter kernel Use Anaconda Navigator Virtual Environment Python2 and install common third party package

When I used the Keras visualization model, I met the above error with the following error message: Traceback (most recent): File "harrison_feature_model.py", line The solution is: Pip install pydot-ng pip install GraphvizAnd then it's solved, my system for Ubuntu 16.04 Or: sudo pip3 install pydot sudo pip3 install graphviz sudo apt-get install Graphviz The solution below is also Ubuntu 16.04, but it's Python3 Reference Documents[1].

November 9, 2015 Google Open source of the artificial intelligence platform TensorFlow, but also become the 2015 's most popular open source projects. After 12 iterations from v0.1 to v0.12, Google released its version of TensorFlow 1.0 on February 15, 2017, and hosted the first TensorFlow Dev Summit conference in Mountain View, California, USA. TensorFlow 1.0 and Dev Summit (2017) Review Compared with previous versions, the features of TensorFlow 1.0 are mainly reflected in the following aspect

), which has already appeared, 8. Summary Two key issues: 1. Why has the memory function. This is the problem solved in the RNN, because there is a recursive effect, the state of the hidden layer at the moment to participate in the calculation of this moment, the explicit point of the statement is the selection and decision-making reference to the last state. 2. Why lstm remember the long time. Because the specially designed structure has the characteristics of CEC, error up a last state when

. activation functionsBefore looking at Keras document mentioned Relu, thought very complex, in fact, the formula is very simple, simple is good ah.It is important to understand the reasons behind* sigmoid sigmoid a variety of bad, and then began to improve.TLDR is too long; doesn ' t readData PreprocessingUFLDL inside the Zca albino what.weight Initialization is to tell you a conclusion, weight is not initialized good, will affect the b

Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in computer vision (ComputerVision) and AlphaGO. Since the last investigation, attention to deep learning has increased significantly. Deep learning is a prominent topic in the AI field. it has been around for a long time. It has received much attention because it has made breakthroughs beyond human capabilities in Comp

Oaching to me and hides the screen.Specifically, Keras is used to implement neural network for learning his face, a Web camera was used to recognize that he I s approaching, and switching the screen.MissionThe mission is-to-switch the screen automatically when my boss was approaching to me.The situation is as follows:It is on 6 or 7 meters from the seat to my seat. He reaches my seat in 4 or 5 seconds after he leaves his seat. Therefore, it's necessa

neural network implemented by JavaScript and its common modules, and includes a large number of browser-based instances. These documents and instances are numerous and complete. Don't let JavaScript and neural networks combine to scare you away, which is a very popular and useful project. 4. Keras Keras is also a library of Python deep learning programs, but it leverages TensorFlow and Theano, which means

. We should use a multi-parameter and not less-than-fit network model. The tradeoff between too much capacity and too little capacity.Unfortunately, there is no effective rule or method to determine the size of the model parameters. You must constantly try to find the optimal parameter size on the validation set. a general approach to determining the size of a model: start with a relatively simple model, gradually increase or decrease the number of neurons or the number of network layers until t

Summary In order to follow the in-depth study of the introductory, usually see the relevant sites and videos, here back up. A comparison of 5 depth learning frameworks Share a comparison video about the most popular 5 depth learning frameworks (Scikit Learn,tensorflow,theano,keras, and Caffe): http://weibo.com/p/ 23044464933dbb5463a1b0cef9ebcb4207b869. Iterate through each of the pros and cons, as well as some sample code, to make a definitive concl

output, the intermediate node classification effect is good. The secondary classification node (auxiliary classifiers) is used, and the intermediate layer outputs the node as a classification node. The result is added to the final classification result by a smaller weight (0.3. Equivalent Model fusion, adding reverse propagation gradient signals to the network and providing additional regularization. Google Inception Net family: "Going Deeper with Convolutions" Inception V1 in September 2014, t

In some tasks, we need to train a number of different neural network models for different situations, at which point we need to call multiple pre-trained models to make predictions in the test phase. To call a single pre-trained model click here Figuring out how to invoke a single model is actually a logical way to invoke multiple models. We just need to build multiple graphs, then import a model for each diagram, and then create a session for each diagram to make predictions

finally through a scale operation to the normalized weight weighted to each channel characteristics.In addition, the SE module can be embedded in a module containing skip-connections. The image on the right is an example of embedding se into the ResNet module, and the operation process is basically the same as se-inception, except that the residual feature on the branch is re-calibrated before the addition. If the features on the main branch of addit

TensorFlow Get variables print weights and Other methods In the use of tensorflow, we often need to get the value of a variable, such as: print the weight of a layer, usually we can directly use the variable's Name property to get, but when we use some third party library to construct neural network layer, There is a situation where we cannot define variables of this layer ourselves because they are automatically defined. For example, when using TensorFlow's slim library: def resnet_stack (i

We fell for recurrent neural networks (RNN), Long-short term-memory (LSTM), and all their variants. Now it's time to drop them! IT is the year 2014 and Lstm and RNN make a great come-back from the dead. We all read Colah's blog and Karpathy ' s ode to RNN. But We were all young and unexperienced. For a few years this is the way to solve sequence learning, sequence translation (SEQ2SEQ), which also resulted in Amazin G results in speech to text comprehension and the raise of Siri, Cortana, Google

; Mountain and Hill,wall, house, building and skyscraper3) inconspicuous Classes large target small target problemTo sum up, the main problem is contextual relationship and global information for different receptive fields 3.2. Pyramid Pooling Module In a deep network, the size of the field determines how much context information we can use. Theoretically, the resnet size of the field is larger than that of the input image. But the literature "42" poi

Table of Contents: part I: Source partial II: Applications, role III: effects (dimensionality reduction, ascending dimension, trans-channel interaction, increasing of nonlinearity)--from the perspective of fully-connected layers First, Source: [1312.4400] Network in Network (if 1x1 convolution is followed by a normal convolution layer, the network in network structure can be implemented with the activation function.) ） second, the application: The residual module in the inception and

Download Address http://openaccess.thecvf.com/ICCV2017.py Also attached Perhaps useful: vggface2+senet far beyond vggface+resnet. Ijb-a,1:1,far=1e-3 Ascend 28 points, ijb-b,1:1,far=1e-5 ascend 33 points. SENET+3M Data Training can achieve the results of resnet+11m data. Welcome to the small partner download use http://www.robots.ox.ac.uk/~vgg/data/vgg_face2/Thank momenta Detection includes identifi

, researchers such as Yanpei Liu presented a model-based approach to attack, saying that when a sample can be tricked into multiple known models of integration, it has a very large likelihood of deceiving unknown models. The Tsail team also said that the integration approach was important in the actual race, and they integrated multiple common convolutional neural networks to build confrontation samples such as Inception V4, ResNet, and Dencenet. Beca