This article mainly introduces the question and answer section of Keras, in fact, very simple, may not be in detail behind, cooling a bit ahead, easy to look over.
Keras Introduction:
Keras is an extremely simplified and highly modular neural network Third-party library. Based on Python+theano development, the GPU and CPU operation are fully played. The purpose o
The content of a simple experiment lesson.
First, the size of the given sample material is 32*32, which can be done in Python batch and OpenCV function resize (), where I do not list the code.
List some of the pictures that are well-shrunk.
Then in the use of Keras CV convolutional neural network model, before doing this experiment, the computer should be configured Python+theano+keras environment.
#生成一个
Keras a pre-trained model with multiple networks that can be easily used.Installation and use main references official tutorial: https://keras.io/zh/applications/https://keras-cn.readthedocs.io/en/latest/other/application/An example of using RESNET50 for ImageNet classification is given on the official website. fromKeras.applications.resnet50ImportResNet50 fromKeras.preprocessingImportImage fromKeras.applic
Logs/000/trained_weights_final.h5 placement after training weightKeras-yolo3-masterKeras/tensorflow + Python + yolo3 train your own datasetCode: https://github.com/qqwweee/keras-yolo3Modify the yolov3.cfg file: 79695109Use yolo3 to train your own dataset for Target DetectionVocdevkit/voc2007/Annotations XML fileVocdevkit/voc2007/javasimages jpgimageFour files under vocdevkit/voc2007/imagesets/Main, create the file test. py under voc2007,Run voc_annota
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: Encoder, which is a layer type or layer container type.
Decoder: Decoder, which is a layer t
,output_dim=300
Back to the original question: the embedded layer converts a positive integer (subscript) to a vector with a fixed size, such as [[4],[20]]->[[0.25,0.1],[0.6,-0.2]]
Give me a chestnut: if the Word table size is 1000, the word vector dimension is 2, after the word frequency statistics, Tom corresponds to the id=4, and Jerry corresponding to the id=20, after the conversion, we will get a m1000x2 matrix, and Tom corresponds to the matrix of the 4th line, The data to remove the row i
Today, the GPU is used to speed up computing, that feeling is soaring, close to graduation season, we are doing experiments, the server is already overwhelmed, our house server A pile of people to use, card to the explosion, training a model of a rough calculation of the iteration 100 times will take 3, 4 days of time, not worth the candle, Just next door there is an idle GPU depth learning server, decided to get started.
Deep learning I was also preliminary contact, decisive choice of the simp
Recently paid attention to a burst of keras, feeling this thing quite convenient, today tried to find it really quite convenient. Not only provide the commonly used algorithms such as layers, normalization, regularation, activation, but also include several commonly used databases such as cifar-10 and mnist, etc.
The following code is Keras HelloWorld bar. Mnist handwritten digit recognition with MLP implem
The model saved with H5py has very little space to take up. Before you can use H5py to save Keras trained models, you need to install h5py, and the specific installation process will refer to my blog post about H5py installation: http://blog.csdn.net/linmingan/article/details/50736300
the code to save and read the Keras model using H5py is as follows:
Import h5py from keras.models import model_from_json
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 over time. Unlike Feedforward neural networks, RNN can receive serialized data as input,
index is to assign an integer ID to each word in turn. Traversing all the news texts, we keep only the 20,000 words we see most, and each news text retains a maximum of 1000 words. Generates a word vector matrix. Column I is a word vector that represents the word index for I. Load the word vector matrix into the Keras embedding layer, set the weight of the layer can not be trained (that is, in the course of network training, the word vector will no l
According to the description of the kaggle:invasive species monitoring problem, we need to judge whether the image contains invasive species, that is, to classify the images (0: No invasive species in the image; 1: The images contain invasive species), According to the data given (2295 graphs and categories of the training set, 1531 graphs of the test set), it is clear that this kind of image classification task is very suitable to be solved by CNN, KERA Application Module application provides
Deeplearning library is quite a lot of, now GitHub on the most hot should be caffe. However, I personally think that the Caffe package is too dead, many things are packaged into a library, to learn the principle, or to see the Theano version.My personal use of the library is recommended by Friends Keras, is based on Theano, the advantage is easy to use, can be developed quickly.Network frameworkThe network framework references Caffe's CIFAR-10 framew
The program demonstrates the process of re-fine-tuning a pre-trained model on a new data set. We freeze the convolution layer and only adjust the full connection layer. Use the first five digits on the mnist dataset [0 ... 4] Training of a convolutional network. In the latter five digits [5 ... 9] Using convolutional networks to classify, freeze convolutional layers and fine-tune all connected layers One, variable initialization
now = Datetime.datetime.now
batch_size =
nb_classes = 5
Nb_epoch =
This article is void
My next installment is the TensorFlow and Keras truth.
Environment:
Anaconda4.2;python3.5;windows10,64,cuda
Previous hard cuda9.1 useless, we want to use the GPU must choose cuda8.0, I thought the official will be corresponding update, naive. First TensorFlow don't recognize, moreover cudnn own all do not recognize, only 8.0.
Keras and TensorFlow are both Pip,pytorch and OpenCV are go
Given dataset data, the tag label for the data setindex = [I for I in range (len data)] random.shuffle (index) data = Data[index]label = Label[index](1) First, to obtain all index of the data set, is actually 0,1,2,...., num-1 (num is the number of examples in the dataset, note that the Python index is starting from 0, so the first element index is 0, Last element index is num-1)"Sample number of functions in a DataSet num=sampnum = Len (data)"index = [I for I in range (len data)] (2) rand
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 theory in this practical project.Reproduced in: http://www.saluzi.com/t/topic/16027How to use dee
Preface
Keras provides a functional plot_model of neural network visualization, and can store the visualization results locally. Use the following methods:
From keras.utils import Plot_model
Plot_model (Encoder_model, to_file= ' encomodel.png ', show_shapes=true)
The author encountered such a problem in the course of using
runtimeerror:failed to import Pydot. Must install Pydot and Graphviz for Pydotprint to work
I see this error, I would like to
Recently tried to learn tensorflow, but because the problem of learning resources leads to a series of problems, in simple terms, to learn tensorflow, to directly view the guidance of the GitHub, rather than according to the blog, Baidu on the guidance, because the version of the change too fast, similar to the College of Geeks, Blog guidance and code has not run, according to the error step-by-step processing instead into a dead end, the more mistakes, the following gives me in the installation
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