alexnet in keras

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,

) Reducelronplateau when the indicator becomes Reduce learning rate Reducelronplateau (monitor= ' Val_loss ', factor=0.1, patience=10, mode= ' auto ', epsilon=0.0001, CoolD Own=0, min_lr=0) modelcheckpoint Example: From keras.callbacks import modelcheckpoint model = sequential () model.add (Dense, input_dim=784, kernel_ initializer= ' uniform ')) Model.add (Activation (' Softmax ')) model.compile (loss= ' categorical_crossentropy ') , optimizer= ' R

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

about the Keras 2.0 version of the Run demo error problem Because it is the neural network small white, when running the demo does not understand Keras version problem, appeared a warning: C:\ProgramData\Anaconda2\python.exe "F:/program Files (x86)/jetbrains/pycharmprojects/untitled1/cnn4.py" Using Theano backend. F:/program Files (x86)/jetbrains/pycharmprojects/untitled1/cnn4.py:27:userwarning:update your

Learning Data Augmentation Based on keras, augmentationkeras In deep learning, when the data size is not large enough, the following 4 methods are often used: 1. Manually increase the size of the training set. A batch of "new" Data is created from existing Data by means of translation, flip, and Noise addition. That is, Data Augmentation.2. regularization. A small amount of data may lead to over-fitting of the model, making the training error small a

Keras is a Theano and TensorFlow-compatible neural network Premium package that uses him to component a neural network more quickly, and several statements are done. and a wide range of compatibility allows Keras to run unhindered on Windows and MacOS or Linux.Today to compare learning to use Keras to build the following common neural network: Regression

Keras error ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'tensor ("embedding_1/random_uniform: 0", shape = (5001,128), dtype = float32 )', Train and save the model on the server. After the model is copied to the local machine, the load_model () error is returned: ValueError: Tensor conversion requested dtype int32 for Tensor with dtype float32: 'tensor ("embedding_1/random_uniform: 0", shape = (5001,128), dtyp

under the successful installation Anaconda, First, install MinGW: Open prompt-- Input:Conda config--add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/--in input: Conda config--set show_cha Nnel_urls yes-- last input: Conda install MinGW Libpython (so the purpose of the installation is to download more quickly) Second, Open Prompt , you will see a path inside the window, depending on your path, locate the corresponding directory, and create a new text document in the dir

The curve fitting is realized, that is, the regression problem. The model was created with single input output, and two hidden layers were 100 and 50 neurons. In the official document of Keras, the examples given are mostly about classification. As a result, some problems were encountered in testing regression. In conclusion, attention should be paid to the following aspects: 1 training data should be matrix type, where the input and output is 1000*1,

The Keras Python Library makes creating deep learning models fast and easy. The sequential API allows you to create models Layer-by-layer for most problems. It is limited the it does not allow the to create models that share layers or have multiple inputs or outputs. The functional API in Keras is a alternate way of creating models, offers a lot flexibility more complex models. In this tutorial, you'll disc

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

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

Python provides two libraries for fast numerical computations, Theano and TensorFlow, which are very powerful libraries, but it's hard to use them directly to create deep learning models, so Keras came into being, Keras provides a fast and efficient way to create deep learning models based on Theano or TensorFlow.About the installation of Keras, you can see my ot

find MinGW.4, restart the computerV. Installation of TheanoIt is easiest to install directly using the command line:1. Open cmd2, input pip install Theano, after the return is pleasing to download the progress bar, this is very small, so the installation is relatively fast.3, in cmd, input python into the Python environment, and then enter import Theano carriage return, need to wait for some time.Vi. installation of KerasKeras This library on the basis of Theano continue to encapsulate, modular

Migration learning, with off-the-shelf network, run their own data: to retain the network in addition to the output layer of the weight of other layers, change the existing network output layer output class number. Train your network based on existing network weights,Take Keras 2.1.5/vgg16net as an example. Import the necessary libraries From keras.preprocessing.image import Imagedatagenerator to keras impo

This script is a training Keras mnist digital Recognition program, previously sent, today to achieve the forecast, # larger CNN for the mnist Dataset # 2.Negative dimension size caused by subtracting 5 from 1 for ' conv2d_4/convolution ' ( OP: ' conv2d ') with input shapes # 3.userwarning:update your ' conv2d ' call to the Keras 2 Api:http://blog.csdn.net/johini eli/article/details/69222956 # 4.Error check