keras resnet50

1. Use of the framework: Tensorflow+keras 2. The core function of extracting a layer of depth network features: Import Keras.backend as K def get_activation (model, layer, X_batch):Get_activations= k.function ([Model.layers[0].input, K.learning_phase ()],[model.layers[layer].output])Activations=get_activations ([X_batch, 0])return activations Use examples: # Generate Extract-feature DataX_extractfeature = Hdf5matrix (Hdf5path, ' X ', 0, datasetlength

more data (millions above), remove bad data (noise, false data or null values, etc., the data in the presence of Nan value will cause the model loss into Nan).2. Do data augment when you are not good enough. For images that can be flipped horizontally, randomly trimmed crop, rotated, twisted, scaled, stretched, changed hue, saturation (HSV), etc., can also be randomly combined. Attention should be paid to the changes in the picture (vertical flip) Whether it conforms to the actual, whether the

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1:define A set of function Step 2:goodness of function Step 3:pick the best function X_train, Y_train Batch_size Epoch Speed speed-why Mini-batch is faster than stochastic gradient? For the GPU, let it do the two opera

Pytorch currently supports the platform has Linux and OSX, on the Pytorch website each platform provides Conda, Pip, source three kinds of installation methods, but also can be based on the GPU for CUDA installation, here to ubuntu14.04 for installation learning. 1. Anaconda Installation ConfigurationThe installation process references my previous Anaconda+tensorflow+theano+keras installation blog.Due to wall problems, installing pytorch with Conda w

abc[:] = range (len ( ABC)) #用0-integers of 2416 are re-assigned to each word sequentially, and an integer represents a word Word_set = set (Abc.index) #构建字典 Five, Corpus pretreatment two def doc2num (S, maxlen): #构建将文本转化为数字向量的函数, maxlen=200 s = [i-I in S if I in Word_set] s = s[:maxlen] #截取 200 Word return list (Abc[s]) all_[' doc2num '] = all_[0].apply (lambda s:doc2num (S, maxlen)) #使用函数将文本转化为数字向量 # #all_中的文本向量表示形式: [8, 65, 795, 90, 152, 152, 289, 37, 22,

obtained by different temperature were shown. The larger the temperature, the more uniform the new probability distribution, the greater the randomness, and the easier it is to generate some unexpected words. def sample(p, temperature=1.0): # 定义采样策略 distribution = np.log(p) / temperature distribution = np.exp(distribution) return distribution / np.sum(distribution)p = [0.05, 0.2, 0.1, 0.5, 0.15]for i, t in zip(range(4), [0.1, 0.4, 0.8, 1.5]): plt.subplot(2, 2, i+1) plt.bar(np.ar

the BP algorithm.E=12n∑ (xi−yi) 2 E = \frac{1}{2n}\sum (x_i-y_i) ^2 Code This is a Keras-based code found on GitHub: Def getmodel (): input_img = input (shape= (1) x = convolution2d (3, 3, activation= ' Relu ', border_mode= ' s Ame ', dim_ordering= ' TF ') (input_img) x = Maxpooling2d ((2, 2), border_mode= ' same ', dim_ordering= ' TF ') (x) x = Convolu Tion2d (3, 3, activation= ' Relu ', border_mode= ' same ', dim_ordering= ' TF ') (input_img) x = M

inspired by the Scikit-learn project and summed up the drawbacks of MLlib in dealing with complex machine learning issues, designed to provide users with a higher-level API library based on DataFrame to make it easier to build complex Machine learning workflow applications. A Pipeline is structurally composed of one or more pipelinestage, each pipelinestage a task, such as data set processing conversions, model training, parameter setting, or data prediction, such that the pipelinestage in ML T

* *.Second, installation Scikit-learnExecute command:Conda Install Scikit-learnSecond, installation KrasExecute command:Conda Install KerasThe required tensorflow is automatically installation during installation of the Keras process.At this point, deep learning, machine learning development environment has been installed, you can commandSpyderOrJupyter NotebookOpen your favorite IDE for development, enter the following code, if there is no error, it

The main concern of data science, Python has Numpy,scipy,statsmodels,scikit-learn,seaborn,pymc,pandas,keras,lasagne and so on. What are the features of the popular packages of R languages that Python does not have corresponding packages for? What other R languages can provide functionality that Python cannot directly implement (user-written algorithms do not count)? Reply content:Ggplot2,r inside the most hot bag, python inside only a cottage, the fun

Caffe's own example of a new project, mainly the configuration include Lib DLLs are pits, but also divided into debug and release two versions.and add input items to be aware of, but also need to be compiled caffe.lib and so on a series of things to copy under the current project.Caffe's other pit is: F0519 14:54:12.494139 14504 layer_factory.hpp:77] Check failed:registry.count (t ype) = = 1 (0 vs. 1) Unknown Layer Type:input (known types:input) was originally to add header files! Http://blog.cs

your findings?I had tried optimizing kappa directly uisng Xgboost (see below), but it performed a bit worse than plain regression. This might has something to does with the Hessian, which I couldn ' t get to work unless I used some scaling and change it to Its absolute value (see here).Which tools did you use?I used Python for this competition. For feature engineering part, I heavily relied on pandas and Numpy for data manipulation, Tfidfvectorizer and SVD in Sklea RN for extracting text featur

The current popular deep learning library has Caffe,keras,theano, this article uses the Google Open source has used to make Alphago's deep learning system TensorFlow.1: Install TensorFlowThe earliest TensorFlow only supported Mac and Linux systems and currently supports Windows systems, but requires a python3.5 (64bit) version. TensorFlow has CPU and GPU versions, because this document uses a server that is Nvidia graphics card, so install the GPU ver

1. Default Python installation status In general, Linux is preloaded with Python, with lower versions, such as the UBUNTU15 system, which is typically preloaded with Python2.7.10.Use the command: which Python to view the current Python installation path :Open the corresponding path to see:If you do not explicitly have to use the Python3 version, you can use the 2.7.10 version directly.It is clear that you need to use the Python3 version before making changes.It is important to note that th

target function of the perceptron algorithm) Learning theory (just let the algorithm update have a confrontational guarantee) What is the perceptron algorithm? Suppose we have a linear equation in the form of:F (x) =?W,X?+BF (x) =?w,x?+b.We want to estimate the vector W and constant B to implement every time input Class 1 o'clock, F is always positive, and every time you enter Category 1, F is always negative. So we can do it in the following steps: Initialize W and b to 0 (or

1, NumPy: Basic module, efficient processing of data, providing array support2. Pandas: Data exploration and data analysis3. Matplotlib: Data mapping module to solve data visualization4, SciPy: Support numerical calculation, support matrix operation, provide advanced mathematics processing: integral, Fourier transform, differential equation solution5. Statsmodels: Statistical analysis6. Gensim: Text Mining7, Sklearn: Machine learning8. Keras: Deep Lea

: Pyplot,pylab (not recommended), object-oriented Adjustment of axes, addition of text annotations, area fills, and use of special graphics patches Financial students Note that: You can directly call the Yahoo Financial data mapping (real ... ） Scipy:A handy, easy-to-use Python toolkit designed for science and engineering. It includes statistics, optimization, integration, linear algebra modules, Fourier transforms, signal and image processing, ordinary differential equation solvers

What is NumPy's Ndarray? First, the core of NumPy is Ndarray.Then, Ndarray is an array, unlike a normal array, or a list of Python where it can have n dimensions (dimentions), or a simple understanding of arrays nested within arrays.Finally, NumPy provides a convenient operation function for Ndarray, and is superior in performance to the list of Python, so it is widely used in the fields of numerical computation, machine learning, artificial intelligence and neural network.NumPy is almost a co

: Https://github.com/fchollet/keras;Third, through the official website documents: https://www.python.org/doc/3, Learning neural Network: See CNN, RNN paperNn-lectures: University of Toronto courseware CSC321 Winter 2015:introduction to neural NetworksLink: http://pan.baidu.com/s/1gdpuo1t Password: Ueib4, install Ubuntu dual system (Ubuntu 14.04.1 LTS), and familiar with command operation, learn the use of vim command5, LSTM:Lstm Neural network in lay

. Overwhelmed by machine Learning:is there a ML101 book: This is a problem on the StackOverflow. A range of machine learning recommended books are available. The first answer that Jeff Moser provides is useful, including links to course videos and lectures. Related theories, books, papers, courses, blogs: [Book]Yoshua Bengio, Ian Goodfellow, Aaron Courville. Deep learning. 2015. [Book]Michael Nielsen. Neural Networks an