keras resnet

sentiment : increase the diversity of training samples can be understood: if you want to do cat and dog classification, then different types, different postures, sleeping meals and so on the cat, a variety of things to improve your algorithm effect is helpful. if you want to increase the number of iterations can be judged according to your loss situation, when your loss is not stable, and the model is not too well-timed, you can increase the number of iterations. Experience requires that you ru

dimensions. The input to the DUC is the output of the ResNet network feature map HXWXC, we use DUC output feature map sizeHXWX (R*RXL), finally reshaped the input image size HXWXL. Completed the magnification workWhere L is the total number of categories of semantic segmentation, R is the downsampling factor in ResNet. The core idea of Duc is to divide the complete label map into R*r of the same size, and

sample is trained less often, so a real sample is more helpful for training) Inception V3:Inception V3 is mainly reformed in two areas: The idea of factorization into small convolutions was introduced to split a larger two-dimensional convolution into two smaller one-bit convolution, such as splitting the 7*7 convolution into 1*7 convolution and 7*1 convolution (3*3 split into 1*3 and 3*1). on the one hand, a large number of parameters are saved, the acceleration operation is subt

, at least not the increase in the error on the training set.The solution is to introduce residuals: a layer of network input is x, the expected output is H (x), if we direct input x to the output as an equivalent mapping, and the middle of the non-linear layer is f (x) =h (x)-X as residuals. We guess that optimizing the residual mapping is easier than optimizing the original mapping, and in extreme cases the residual f (x) is compressed to 0. ：The above is the residual unit of

author used the VGG19 model as a loss network. In the original paper, the VGG16 is used. To maintain consistency, I used the TensorFlow slim to repackage the loss network. Slim is an extended library of TensorFlow that provides a number of functions related to image classification, and has already been well trained models (such as VGG, Inception series, and ResNet series). The following figure is the model supported by Slim: Using slim to replace th

Deep Residual network in the 2015 ILSVRC competition to achieve the first achievement, ICLR2016 is also one of the key issues. Its main idea is simply to add a hop to bypass some layers of connectivity on a standard feedforward convolution network. Each bypass layer produces a residual block (residual blocks), and the convolution layer predicts the residuals of the input tensor. As shown in the following illustration: Common depth feedforward networks are difficult to optimize. In addition to

First, we look at the new progress of target detection from CVPR2016. The 2016 CVPR conference target detection method is mainly based on convolution neural network framework, Representative work has resnet (in faster r-cnn ResNet replacement Vgg), YOLO (regression detection framework), locnet (more accurate positioning), Hypernet (High level information of neural network is advantageous to the identificati

Preface Bloggers have spent a lot of time explaining how to Kitti raw data into an SSD-trained format, and then use the relevant Caffe code to implement SSD training. Download Vgg pre-training model The SSD is used for its own inspection task, it is required fine-tuning a pretrained network, the friends who have read the paper may know that the SSD framework in the paper is made up of the Vgg network as the base (base). In addition to this, the authors also provide two other types of network: Z

-related environment variablesNew environment variable Pythonpath, variable value is:C:\Anaconda2\Lib\site-packages\theano;Test Theano Installation success: Import Theano, no error, Theano installation success6. Installing KerasDownload Keras on GitHubIn cmd, go to the folder where you downloaded the Keras, and then use the Python setup.py install command Keras7. Install Pycharm Community (free)After instal

He is good at python, theano, and keras frameworks. He wants to introduce some new and interesting papers. Note: painting has been realized. Reply content: I have already received more than 400 likes without knowing it. Recently, I have finally made some time to add more interesting things. The content in the back will not be broken down ...... (No more than deep learning ......) 0. GitHub-Rochester-NRTRocAlphaGo: Anindependent, student-ledreplication

Recently in the race to do an image classification, as the first contact with deep learning Rookie, get started Keras. To tell the truth, in addition to the Keras tutorial, Chinese Blog Technical support is too poor. In the study of the big head ... Needless to say, record some of the small details of your study. In the Encounter generator.flow_from_directory (' Data/train ' ...) This function, you need to

capture and WEB capture framework developed by Python that allows users to easily implement a crawler that crawls Web content as well as various images with a simple custom development of several modules. Scrapy can be used for data mining, monitoring and automated testing in a wide range of applications.The attraction of Scrapy is that it is a framework that anyone can easily modify as needed. It also provides base classes for various types of crawlers, such as Basespider, sitemap crawlers, et

Python data analysis (Basic)First, install the anaconda:https://www.anaconda.com/download/#windowsIi. NumPy (Basic package of scientific calculation)Three, matplotlib (chart)Iv. SciPy (collection of packages for solving various standard problem domains in scientific calculations)V. Pandas (Treatment of structured data)Vi. Sciket-learn (machine learning and decision tree)1, data mining and machine learning are divided into three steps: Data preprocessing, data modeling, validationVii.

Directory1. What is regularization?2. How does regularization reduce overfitting?3. Various regularization techniques in deep learning:Regularization of L2 and L1DropoutData Enhancement (augmentation)Stop early (Early stopping)4. Case study: Case studies using Keras on Mnist datasets1. What is regularization?Before going into this topic, take a look at these pictures:Have you seen this picture before? From left to right, our model learns too much deta

=5176.8366600.0.0.6021311f0wiltq raceid=231601postsid=2947 "So for the improvement of the network, as far as I'm concerned, tried: 1, in the last layer (after the last sampling, before the first sampling) to join a full-join layer, the purpose is to add a cross-entropy loss function, in order to add additional information (such as whether a picture is a certain type of things)2, for each time the sample is output (prediction), the results will be a fusion (similar to the FPN network (feature pyr

would be is implied on each input. The function would run after the image is resized and augmented. The function should take one argument:one image (Numpy tensor with rank 3), and should output a Numpy tensor with the SAM E shape. Data_format=none One of {"Channels_first", "Channels_last"}. "Channels_last" mode means that the images should has shape (samples, height, width, channels), "Channels_first" mode means that the images should has shape (samples, channels, height, width). It defaults to

These images will be trained in this section, as described in the previous chapters, and we can get a good sample of the training samples. The main use is Keras. I. Building a DataSet class 1.1 Init Complete Initialization work def __init__ (self,path_name): self.train_img = none self.train_labels = None self.valid_img = None self.valid_labels = None self.test_img = None self.test_labels = non

. There is absolutely no need, and will cause the Spyder to start when the window, kernel died, and so on, this is my test, engaged a day ... "" When installing anaconda, do not install Python version 3.5, the total display GPU is not available. And do not install Spyder3 series, that is, more than Anaconda4.2.0. Instead, Python chooses 2.7,spyder to select the 2 series, which is the Anaconda4.1.1 version and below. What is the reason? Because Spyder3 always does not call the Ipythonw.exe interp

Sklearn.cluster.Birch Unknown K-medoids Clustering Pyclust. Kmedoids (Reliability unknown) Cluster.pam Association Rules category Python R Apriori algorithm Apriori (Unknown reliability, py3 not supported), Pyfim (Reliability unknown, PIP installation not available) Arules::apriori Fp-growth algorithm Fp-growth (Unknown reliability, py3 not supported), Pyfim (Reliability u

function, |a|>1, it means that the curve is getting smoother, Z-values tend to be closer to 1 or 0, which can also cause gradients to disappear.What if we can give a suitable value to W when we initialize the weights in each layer of the network, can we reduce the possibility of this gradient explosion or gradient disappearing? Let's see how to choose.One, random distribution weightsIn Keras, whose function is: k.random_uniform_variable (), let's tak