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oling square num_pool 2 side length of convolution square num_conv 3 dropout rate for regularization dropout_rate 0 5 hidden number of neurons first layer num_hidden 128 validation data validation_split 0 2 20 optimizer used optimizer sgd lr 0 01 decay 1e 6 momentum 0 9 nesterov true output classes number of minst digits num_classes 10 shape of an minst digit image shape_x shape_y 28 28 channels on minst img_channels 1 load the minst data split in training and test data x_train y_train x_test y_test mnist load_data x_train x_train reshape x_train shape 0 1 shape_x shape_y x_test x_test reshape x_test shape 0 1 shape_x shape_y convert in float32 representation for gpu computation x_train x_train astype float32 x_test x_test astype float32 if normalize normalize each pixerl by dividing by max_value 255 x_train 255 x_test 255 print x_train shape x_train shape print x_train shape 0 train samples print x_test shape 0 test samples keras needs to represent each output class into ohe representation y_train np_utils to_categorical y_train num_classes y_test np_utils to_categorical y_test num_classes nn sequential first layer of convnets pooling dropout apply a num_conv x num_conf convolution with num_filters output for the first layer it is also required to define the input shape activation function is rectified linear nn add convolution2d num_filters num_conv num_conv input_shape img_channels shape_x shape_y nn add activation relu nn add convolution2d num_filters num_conv num_conv nn add activation relu nn add maxpooling2d pool_size num_pool num_pool nn add dropout dropout_rate second layer of convnets pooling dropout apply a num_conv x num_conf convolution with num_filters output nn add convolution2d num_filters num_conv num_conv nn add activation relu nn add convolution2d num_filters num_conv num_conv nn add activation relu nn add maxpooling2d pool_size num_pool num_pool nn add dropout dropout_rate flatten the shape for dense connections nn add flatten first hidden layer of dense network nn add dense num_hidden nn add activation relu nn add dropout dropout_rate outfut layer with num_classes outputs activation is softmax regularization is l2 nn add dense num_classes w_regularizer l2 0 01 nn add activation softmax summary nn summary plot the model plot nn set an early stopping value early_stopping earlystopping monitor val_loss patience 2 compile the model loss_function is categorical_crossentropy optimizer is parametric nn compile loss categorical_crossentropy optimizer optimizer metrics accuracy start time time fit the model with validation data fitlog nn fit x_train y_train batch_size batch_size nb_epoch num_epochs verbose verbose validation_split validation_split callbacks early_stopping elapsed time time start test the network results nn evaluate x_test y_test verbose verbose print accuracy results 1 just to get the list of input parameters and their value frame inspect currentframe args _ _ values inspect getargvalues frame used for printing pretty arguments print_graph fitlog elapsed args values return fitlog 2 epochs log convnet_lenet optimizer adam num_epochs 2 print log history 20 epochs log convnet_lenet optimizer adam num_epochs 20 print log history default optimizer sgd log convnet_lenet num_epochs 20 print log history default optimizer rmsprop log convnet_lenet optimizer rmsprop num_epochs 20 print log history default optimizer log convnet_lenet optimizer adam dropout_rate 0 print log history default optimizer log convnet_lenet optimizer adam dropout_rate 0 1 print log history default optimizer log convnet_lenet optimizer adam dropout_rate 0 2 print log history default optimizer log convnet_lenet optimizer adam dropout_rate 0 4 print log history default optimizer log convnet_lenet optimizer adam batch_size 64 print log history log convnet_lenet optimizer adam batch_size 128 print log history log convnet_lenet optimizer adam batch_size 256 print log history log convnet_lenet optimizer adam batch_size 512 print log history log convnet_lenet optimizer adam batch_size 1024 print log history log convnet_lenet optimizer adam batch_size 2048 print log history log convnet_lenet optimizer adam batch_size 4096 print log history log convnet_lenet optimizer adam validation_split 0 8 print log history log convnet_lenet optimizer adam validation_split 0 6 print log history log convnet_lenet optimizer adam validation_split 0 4 print log history log convnet_lenet optimizer adam validation_split 0 2 print log history log convnet_lenet optimizer adam validation_split 0 2 normalize false print log history log convnet_lenet optimizer adam validation_split 0 2 num_filters 64 print log history log convnet_lenet optimizer adam num_filters 128 print log history log convnet_lenet optimizer adam num_filters 256 print log history x log convnet_lenet optimizer adam num_pool 4 x print log history log convnet_lenet optimizer adam num_pool 8 print log history log convnet_lenet optimizer adam num_conv 4 print log history x log convnet_lenet optimizer adam num_conv 8 x print log history log convnet_lenet optimizer adam num_hidden 32 print log history log convnet_lenet optimizer adam num_hidden 64 print log history log convnet_lenet optimizer adam num_hidden 256 print log history log convnet_lenet optimizer adam num_hidden 512 print log history log convnet_lenet optimizer adam num_hidden 1024 print log history verbose 1 normlize normalize true network parameters batch_size 128 num_epochs 100 number of convolutional filters num_filters 32 side length of maxpooling square num_pool 2 side length of convolution square num_conv 3 dropout rate for regularization dropout_rate 0 5 hidden number of neurons first layer n_hidden 128 validation data validation_split 0 2 20 optimizer used optimizer sgd lr 0 01 decay 1e 6 momentum 0 9 nesterov true plt show next posting is about describing the code then you will see dozens of experiments for exploring the hyper parameters space and inferring some rules of thumbs for fine tuning our deep learning nets stay tuned during the next months we will see more than 20 nets for deep learning in different contexts and show super human capacity pubblicato da codingplayground a 1 24 pm no comments email this blogthis share to x share to facebook share to pinterest tuesday december 8 2015 3rd and 7th on amazon pubblicato da codingplayground a 12 52 am no comments email this blogthis share to x share to facebook share to pinterest thursday december 3 2015 special edition programming interview questions solved in c tree graph bit dynamic programming and design patterns special collections on programming volume 1 a collection of more than 150 interview questions in c useful to nail your next job interview pubblicato da codingplayground a 11 20 pm no comments email this blogthis share to x share to facebook share to pinterest wednesday december 2 2015 a collection of advanced data science and machine learning interview questions solved in python and spark ii volume 7 hands on big data and machine programming interview questions volume 7 advanced machine learning and handson examples on spark and python pubblicato da codingplayground a 11 17 pm no comments email this blogthis share to x share to facebook share to pinterest tuesday december 1 2015 a collection of data science interview questions solved in python and spark volume 6 hands on big data and machine learning volume 6 a new book for machine learning and data mining with practical hands on examples in spark and python pubblicato da codingplayground a 11 15 pm no comments email this blogthis share to x share to facebook share to pinterest sunday november 22 2015 elsevier machine learning content discoverability http www slideshare net antoniogulli 2015 machine learning elsevier demos pubblicato da codingplayground a 10 38 pm no comments email this blogthis share to x share to facebook share to pinterest older posts home subscribe to posts atom antonio gulli gulli family subscribe to posts atom posts all comments atom all comments search this blog popular posts k means in c k means is a classical clustering algorithm here you have a c code for k means clustering edit 12 05 013 see also my more rece adaboost improve your weak performance adaboost is one of my favorite machine learning algorithm the idea is quite intriguing you start from a set of weak classifiers and learn nearest neighbour on kd tree in c and boost wikipedia describes the pseudo code for computing the nearest neighbour nn on an already built kdtree here you have a boost implementatio place n queens on a chessboard typical recursive solution where we tentatively put a queen if this doesn t violate conditions in column i then continue in submatrix a robot is moving in a rectangular board it can move either down or right and the board is n x m how many path does the robot have solution steps are n m and we can chose n discuss memory layout for c programs ideally you should discuss all the different areas that are used dbscan clustering algorithm dbscan is a well known clustering algorithm which is easy to implement quoting wikipedia basically a point q is directly densit design patterns c full collection of gamma s patterns full collection of gamma s patterns in c creational abstract factory builder factory prototype object pool singleton struct learning linear regression with gradient descend last week i restarted an old and good behavior see a collection of algos and data structures published here every day i take an well k pca dimensional reduction in eigen pca principal component analisys is a classical machine learning method to reduce the dimensionality of a problem pca involves the calcu antonio gulli google google antonio gulli microsoft antonio gulli microsoft antonio gulli ask com antonio gulli ask com antonio gulli highlander antonio gulli highlander antonio gulli university antonio gulli university antonio gulli elsevier antonio gulli elsevier antonio gulli my ferrari 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ago process algebra diary notes on gandalf 2026 for several reasons i have attended very few conferences and workshops for quite a while however i made an exception for gandalf 2026 which was held in 1 week ago facebook developer blog announcing meta business messaging mcp set up and manage whatsapp business from your ai agent business messaging mcp connects your ai coding agent to the whatsapp business platform so you can create accounts add numbers build templates and send 2 weeks ago petesearch why the software industry needs a lot of regulation structural engineers have a physical stamp that they apply to plans they approve and if a building falls down due to design problems they re directly and 3 weeks ago techcrunch how i get free traffic from chatgpt in 2025 aio vs seo three weeks ago i tested something that completely changed how i think about organic traffic i opened chatgpt and asked a simple question what s the 10 months ago gigaom reclaiming control digital sovereignty in 2025 sovereignty has mattered since the invention of the nation state defined by borders laws and taxes that apply within and without while many the post r 1 year ago techcrunch europe rummy nabob the royal destination for ultimate card gaming online gaming in india has undergone a dramatic transformation in the past decade and one of the brightest stars in this evolution is the classic game o 1 year ago search engine land news about search engines search marketing tiktok unveils 5 new advertising tools tiktok introduced smart gmv max pets conversion lift studies and out of phone retail to help brands drive stronger results 1 year ago in theory focs test of time awards the focs test of time award recognizes each year a paper or papers each from the focs conference of 10 20 and 30 years earlier for the impact they have 2 years ago zen and the art of programming i ask basic questions during technical interviews over the past 18 years at ibm i have interviewed over a thousand programmers for positions within my team which has a fair number of interns most of 2 years ago official google research blog generative ai to quantify uncertainty in weather forecasting posted by lizao larry li software engineer and rob carver research scientist google research accurate weather forecasts can have a direct impact on 2 years ago geeking with greg my book algorithms and misinformation misinformation and disinformation are the biggest problems on the internet to solve a problem you need to understand the problem in algorithms and mis 2 years ago geeksforgeeks python flask projects with source code beginners to advanced flask a python web application framework was created by armin ronacher known for its lightweight and efficient nature flask is designed for quick sta 3 years ago seo by the sea identifying subjective attributes of entities identifying ugc subjective attributes of entities this recently granted patent is about identifying subjective attributes of entities i haven t seen a p 4 years ago my biase...