Introduction Some Features: advanced packaging based on backend, which can be tensorflow. Due to the advanced package, it is convenient for fast experiments. Concise and Rich Documents slower than native tensorflow All kinds of new trick are available, such as Batch normalization, PReLU, etc. architecture is flexible, model is based on layer by layer superposition, and is very pluggable. Author Introduction keras's father, google brain researcher Fran
1, the last blog post is a simple regression neural network.2. Classification. when using data, keras data can be MNIST. import numpy as np np.random.seed(1337) from keras.datasets import mnist from keras.utils import np_utils from keras.models import Sequential from keras.layers import Dense, Activation from keras.optimizers import RMSprop A. Do some simple processing on the data.X, each picture is 28x28 pixels, the pixel value is between 0 and 255, divided by 255
There are many blogs that talk about Caffe multi-label input, but they always feel that they are not thorough enough and do not give detailed guidance from a practical point of view. Therefore, this article tries to give detailed practical process and explanation. Caffe multi-label input commonly used methods are as follows:1. Modify the CAFE source code to support multilabel input, refer to CSDN blog CAFE Implementation of Multi-Label and
# yum install yum-utils
set source: [ base-src ] name = centos-5.4-basesrc-baseurl = http://vault.centos.org/5.4/os/srpms/# mirrorlist = http://mirrorlist.centos.org/?release=5.4&arch=SRPMS&repo=osgpgcheck=1gpgkey=http://vault.centos.org/RPM-GPG-KEY-CentOS-5
[updates-src]name=CentOS-5.4 - Updates src-centosbaseurl=http://vault.centos.org/5.4/updates/SRPMS/#mirrorlist=http://mirrorlist.centos.org/?release=5.4&arch=SRPMS&repo=updatesgpgcheck=1gpgkey=http://vault.centos.org/RPM-GPG-KEY-CentOS-5
#packages used/produced in the build but not released[addons-src]name=CentOS-5.4 - Addons src -baseurl=http://vault.centos.org/5.4/addons/SRPMS/#mirrorlist=http://mirrorlist.centos.org/?release=5.4&arch=SRPMS&repo=addonsgpgcheck=1gpgkey=http://vault.centos.org/RPM-GPG-KEY-CentOS-5
#additional packages that may be useful[extras-src]name=CentOS-5.4 - Extras src-centosbaseurl=http://vault.centos.org/5.4/extras/SRPMS/#mirrorlist=http://mirrorlist.centos.org/?release=5.4&arch=SRPMS&repo=extrasgpgcheck=1gpgkey=http://vault.centos.org/RPM-GPG-KEY-CentOS-5
#additional packages that extend functionality of existing packages[centosplus-src]name=CentOS-5.4 - Plus src-centosbaseurl=http://vault.centos.org/5.4/centosplus/SRPMS/#mirrorlist=http://mirrorlist.centos.org/?release=5.4&arch=SRPMS&repo=centosplusgpgcheck=1enabled=0gpgkey=http://vault.centos.org/RPM-GPG-KEY-CentOS-5
#contrib - packages by Centos Users[contrib-src]name=CentOS-5.4 - Contrib src-centosbaseurl=http://vault.centos.org/5.4/contrib/SRPMS/#mirrorlist=http://mirrorlist.centos.org/?release=5.4&arch=SRPMS&repo=contribgpgcheck=1enabled=0gpgkey=http://vault.centos.org/RPM-GPG-KEY-CentOS-5
How to view the source code of a command
Keras is a high-level neural network API. Keras is written by pure Python and is based on Tensorflow, Theano and CNTK backend.Keras was born to support rapid experiments and can quickly convert your idea into results. If you have the following requirements, please choose Keras:
Simple and Fast Prototype Design (keras is highly modular, minimalist, and extensible)Supports CNN and RNN, or a combination of bothSeamless CPU and GPU Switching Keras is available in Python versions: Python 2.
keras is an open source python deep learning library, which can be based on theano or tenserflow. The following is a general introduction to several important modules of keras Important Modules 1, optimizers optimizer is a method to adjust the weight of each node. Look at a code example: model = Sequential() model.add(Dense(64, init='uniform', input_dim=10)) model.add(Activation('tanh')) model.add(Activation('softmax')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='mean_squared_error', optimizer=sgd) You can see that the
impressions keras provides flow_from_directory for single label classification, but there is no support for multi-label classification of pictures, which requires us to implement ImageDataGenerator by ourselves. Here I share the custom DataGenerator that I have implemented for multi-label classification, and readers can modify it according to their own situation.
Data Set I used the NUS-WIDE data set after sorting, and the download address is: https://download.csdn.net/download/w5688414/10816132
My data set is placed in a txt file.
Caffe practice-training and deployment of Multi-label multi-label labeling based on ResNet101 Previous attempts have been made to modify the source code of Caffe ImageDataLayer to read multiple labels-ImageMultiLabelDataLayer [ Caffe Practice-Training and Deployment Based on VGG16 Multi-Label Classification ]. The way to modify the source code may seem a bit tedious, after all, it needs to be recompiled. Here, a new way to automatically label multiple labels is tried. unlike
directory:Deep Learning Language Model (1)-Development of Word2VCEDeep Learning Language Model (2)-Word Vector, Neural Probability Network Model (keras Version)Deep learning language model (3)-word2vce Negative Sampling model (keras version) 1. Neural Probability Network Model (2003), the steps are as follows:(1) input layer, each word is represented by a random 100-dimensional vector(2) Projection layer, which splices words of a context, for example, if the sliding window is 3, there is (batch_size,6,100)(3) The hidden
There are more than one label for pictures in the multi-label image classification task. Therefore, the standard of ordinary single-label image classification, namely mean accuracy, cannot be used for evaluation. The task adopts a similar method to that in information retrieval, namely —mAP(mean Average Precision).Although the literal meaning of mAP is similar to that of mean accuracy, the calculation method is much more complicated. The following is the calculation method of mAP: