keras deep learning tutorial

the loss function (target function) SGD = SGD (l2=0.0,lr=0.05, decay=1e-6, momentum=0.9, nesterov=true) Model.compile ( LosS= ' categorical_crossentropy ', optimizer=sgd,class_mode= "categorical") #调用fit方法, is a training process. The number of epochs trained is set to 10,batch_size of 100. #数据经过随机打乱shuffle =true. Verbose=1, the information that is output during the training process, 0, 1, 23 ways can, does not matter. Show_accuracy=true, each epoch of the training output accuracy. #validation_s

Keras Learning Notes Original address: http://blog.csdn.net/hjimce/article/details/49095199 Author: hjimce Keras and the use of Torch7 is very similar to the recent fire up the depth of the open source Library, the bottom is used Theano. Keras can be said to be a python version of Torch7, very handy for building a CNN

Python vector: Import NumPy as np a = Np.array ([[[1,2],[3,4],[5,6]]) SUM0 = Np.sum (A, axis=0) sum1 = Np.sum (A, Axis=1) PR int SUM0 Print sum1 > Results: [9 12][3 7] Dropout In the training process of the deep Learning Network, for the Neural network unit, it is temporarily discarded from the network according to certain probability.Dropout is a big kill for CNN to prevent the effect of fitting. Output

" convolutional neural Networks (CNNs): An illustrated explanation"5" convolutional neural Networks backpropagation:from intuition to derivation"6" Congratulations to the end. CS231N Official notes Authorized translation of the anthology published DL and Keras Related:Activation function Guidance in "1" depth learningDiscussion on the problem of over-fitting of "2" depth network"3" How to improve Deep

the difficulty of getting started with deep learning algorithms .. in other words, it is a silly call. In fact, almost all of them are used. I remember that this year's Deep Learning summit in London had a great opportunity to get a class comparison chart of the python library (searching for the source image...). I c

bit, the Python-based library actually has a decaf , this is called more thoroughly than PYLEARN2 abandoned building stop development (escape do not know which home the strongest, only know pylearn2 the worst. It took about one months to learn, it was a nightmare, fortunately stopped development. First of all, Pylearn2 can also be ranked among them ... The main topic is Baidu has a hundreds of years ago, "experience post" it. Second, the main question is "which library to use." If from the "Lo

Spark ML Model pipelines on distributed deep neural Nets This notebook describes what to build machine learning pipelines with Spark ML for distributed versions of Keras deep learn ING models. As data set we use the Otto Product Classification challenge from Kaggle. The reason we chose this data is, it is small and ve

Nowadays, AI is getting more and more attention, and this is largely attributed to the rapid development of deep learning. The successful cross-border between AI and different industries has a profound impact on traditional industries.Recently, I also began to keep in touch with deep learning, before I read a lot of ar

,nb_epoch=15,shuffle=true,verbose=1,show_accuracy=true,validation_ Data= (TestData, TestLabel)) #设置测试评估参数, with test set sample Model.evaluate (TestData, TestLabel, Batch_size=16,verbose=1,show_accur acy=true) ############ #主模块 ########### #trainpath = '/home/emmons/carband_resize/train/' Testpath = '/home/emmons/ carband_resize/test/' testcount=__getnum__ (testpath) traincount=__getnum__ (trainpath) testdata,testlabel= __data_ Label__ (Testpath, Testcount) traindata,trainlabel= __data_label__ (

["Predictions"] = []# Loop over the results and add them to the list of# returned predictions for(Imagenetid, label, prob)inchresults[0]: R = {"Label": Label,"Probability":float(Prob)} data["Predictions"].append (R)# indicate that's the request was a successdata["Success"] =True# Return the data dictionary as a JSON responsereturnFlask.jsonify (data)Although it is a core part, it is very easy to be reused. is the process of reading the data and then processing it. # If This is the main thread o

Tags: arc update. So dia switch Linu HTTPS installation tutorial DevelopThe Deep learning Framework Keras is based on TensorFlow, so installing Keras requires the installation of TensorFlow:1. The installation tutorial is mainly r

still very large. So in general, for the less complex verification code should choose a smaller network, only to encounter more complex verification code such as Chinese idioms, our experience is a complex network under the effect is better.In short, captcha recognition can be learned as a practiced hand project for deep learning, and it is easier to understand many of the concepts in

Deep learning Keras Frame Notes Autoencoder class use notes　　This is a very common auto-coding model for building. If the parameter is Output_reconstruction=true, then Dim (input) =dim (output), otherwise dim (output) =dim (hidden).Inputshape: Depends on the definition of encoderOutputshape: Depends on the definition of decoderParameters: Encoder: Encode

RNN model of deep learning--keras training RNN principle: (Recurrent neural Networks) cyclic neural network. It interacts with each neuron in the hidden layer and is able to handle the problems associated with the input and back. In RNN, the output from the previous moment is passed along with the input of the next moment, which is equivalent to a stream of data

Keras. Why Keras is considered to be the future of deep learning. Install Keras Step by step on Ubuntu. Keras tensorflow Tutorial: Keras ba

RNN, or the combination of both Seamless CPU and GPU switching ?? If you want to use Keras on your computer, you need the following tools: Python TensorFlow Keras Here we choose TensorFlow as the back-end tool for Keras. Use the following Python code to output the version numbers of Python, TensorFlow, and Keras:import sysimport

of epsilon items! If the epsilon value is too low, the data after the whitening will appear to be noisy; Conversely, if the epsilon value is too high, the albino data will be too blurry compared to the original data.Epsilon method of selection:A. Draw the eigenvalues of the data graphically; b. Select a characteristic value that is larger than most of the noise in the data to reflect the epsilon .2. How to adjust the epsilon specifically? I don't know, if I had a exercise, I'd be fine.2. When p

to use Keras neural network model and Scikit-learn. How to use Keras to define a neural network for multi-class classification. How to evaluate the Keras neural network model using Scikit-learn with k-fold cross-validation 10, reference http://machinelearningmastery.com/multi-class-classification-tutorial