alexnet in keras

4.1 Keras specifying runtime graphics and limiting GPU usage https://blog.csdn.net/A632189007/article/details/77978058 #!/usr/bin/env python # encoding:utf-8 "" " @version: python3.6 @author: Xiangguo Sun @contact: sunxiangguo@seu.edu.cn @site: http://blog.csdn.net/github_36326955 @software: Pycharm @file: 2clstm.py @time: 17-7-27 5:15pm "" " import os import TensorFlow as TF import Keras.backend.tensorflow_backend as KTF #进行配置, each GPU uses 60%

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

Keras in the use of the GPU when the feature is that the default is full of video memory. That way, if you have multiple models that need to run with a GPU, the restrictions are huge and a waste to the GPU. So when using Keras, you need to consciously set how much capacity you need to use the video card when you run it. There are generally three situations in this setting:1. Specify the video card2. Limit G

design is improper, training super parameter set improper, data set after cleaning problems. Q: How to visualize the Keras training process (changes in loss and ACC). the visualization function is defined by the following statement: Import Keras from keras.utils import np_utils import matplotlib.pyplot as plt%matplotlib inline #写一个LossHistory类, save loss and ACC class Losshistory (keras.callbacks.Callback

Because the display does not support GPU acceleration, there is no configuration associated with this article.1. Install the Homebrew,macos Essential Kit manager./usr/bin/ruby-e "$ (curl-fssl https://raw.githubusercontent.com/Homebrew/install/master/install)"2. Install Python2.1 Check if Python is already installed.Python-vIf you have installed a version of 2.7 or 3.5, you can skip the Python installation.2.2 Installing Python:Brew Install Python 3. Install pip TensorFlow needs to be installe

Import numpy Import Skimage.io import Matplotlib.pyplot as plt from keras.models import sequential from Keras.layers Imp ORT dense from keras.layers import dropout to keras.layers import flatten from keras.layers.convolutional import conv2d From keras.layers.convolutional import maxpooling2d to keras.models import Load_model #if The picture is bigger than 28 *28 'll get below error #ValueError: cannot reshape array of size 775440 into shape (1,28,28,1) image = ' d:\\sthself\\ml \\reshape7.jpg '

Embedding layer Keras.layers.embeddings.Embedding (Input_dim, Output_dim, embeddings_initializer= ' uniform ', embeddings_regularizer =none, Activity_regularizer=none, Embeddings_constraint=none, Mask_zero=false, Input_length=none) Input_dim: Large or equal to 0 integer, dictionary length, i.e. input data max subscript +1 Output_dim: An integer greater than 0 that represents the fully connected embedded dimension input shape Shape (samples,sequence_length) 2D tensor output shape 3D tensor of

KERAS:ACC and Val_acc was constant over epochs, was this normal? Https://stats.stackexchange.com/questions/259418/keras-acc-and-val-acc-are-constant-over-300-epochs-is-this-normal It seems that your model was not able to make sensible adjustments to your weights. The log loss is decreasing a tiny bit, and then gets stuck. It is just randomly guessing. I think the root of the problem is so you have sparse positive inputs, positive initial weights and a

The premise needs to be installed well: ①anaconda3-4.2.0-windows-x86_64 ②pycharm Because the reason for my graphics card is only CPU installed Install the Anaconda is installed in the Python environment, you enter in the cmd there python to see if it shows your Python version informationNow start to install TensorFlow, because in the visit abroad website download is relatively slow, so we want to call Alibaba's imageYou enter%appdata% in the Explorer, go to the directory, create a new

Keras Series-early stopping in training, there are times when you need to stop at a stopped position. But earyly stopping can implement these functions, these times the model generalization ability is stronger. Similar to L2 regularization, a neural network with a relatively small parameter w norm is chosen. There are times when early stopping can be used. Early stopping Advantage: only run once gradient drop, you can find the relatively small valu

Visualization of Keras Models: Model Model = sequential () # INPUT:100X100 images with 3 channels, (3) tensors. # This applies, convolution filters of size 3x3 each. Model.add (Zeropadding2d (1), Input_shape= (3, 3)) Model.add (conv2d (+)' Relu ', padding=' Same ') # Model.add (conv2d (3, 3), activation= ' Relu ', padding= ' same ')) Model.add (Batchnormalization ()) Model.add ( Maxpooling2d (Pool_size= (2, 2)) Model.add (Dropout (0.25)) Model.add (c

Example of Keras (start): 1 Multi-class Softmax based on multilayer perceptron: From keras.models import sequential from keras.layers import dense, dropout, activationfrom keras.optimizers import S GD model = sequential () # Dense (a) is a fully-connected layer with a hidden units. # in the first layer, you must specify the expected input data shape: # here, 20-dimensional vectors. Model.add (Dense (input_dim=20, init= ' uniform ')) Model.add ( Activ

First, IntroductionPartial response normalization of LRNLRN is used for results after convolution and pooling. Due to the use of multiple convolution cores, the resulting feature map has multiple "channels".The direction of the summation is the

This article is the author uses the earlystopping the experience, many is the author own ponder, welcome everybody discussion advice.Please refer to the official documentation and source code for the use of specific earlystop. What's

? The "Fire" model of Squeezenet The squeezenet architecture has alexnet-level accuracy by using the squeeze convolutional layer and the expansion layer (a combination of 1x1 and 3x3 convolution cores), and the model size is only 4.9MB.Although the Squeezenet model is very small, its training requires skill. in my forthcoming book, "Deep learning computer vision and Python," I'll explain in detail how to train squeezenet from scra

Wrapper wrappertimedistributed Packaging Devicekeras.layers.wrappers.TimeDistributed(layer)The wrapper can apply a layer to each time step of the inputParameters Layer:keras Layer Object Entering a dimension of at least 3D and

To import the desired lib: Import NumPy as NP from keras.datasets import mnist to keras.utils import np_utils from keras.models Import Sequential from keras.optimizers import Adam from keras.layers import dense,activation,convolution2d,