Caret Training Issues in R

Issue I started playing with caret package recently and I’m trying to understand the training arguments. Below I used the Sonar dataset and created three imputs and the output. library(caret) library(mlbench) data(Sonar) set.seed(107) SonarImput1<-Sonar[,1:60] SonarImput2<-Sonar[,1:2] SonarImput3<-Sonar[,1] SonarOutCome<-Sonar[,61] mlp <- caret::train(SonarImput1,SonarOutCome,

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Caffe – aborted training

Issue I am trying to train from scratch a caffe model (in docker). pwd: root@982adaaca24f:~/sharedfolder/caffe/docker/image/happyNet# relevant files path: models/ Custom_Model/ deploy.prototxt solver.prototxt train.prototxt datasets/ training_set_lmdb/ data.mdb (5,01 GB) lock.mdb validation_set_lmdb/ data.mdb (163,8 GB) lock.mdb for that I’m running: #~/caffe/build/tools/caffe train

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Resize images and merge data sets in python

Issue I have two datasets, images1 and images2(generated in the function below, by reading images in a loop via given path) def measure(images1,path): images2=[] for filename in glob.glob(path): #looking for pngs temp = cv2.imread(filename).astype(float) images2.append (augm_img) print(np.array(images2).dtype) print(np.array(images).dtype) print(np.array(images2).shape) print(np.array(images).shape)

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Tfrecord vs TF.image?

Issue I was under the impression that having a pre-computed Tfrecord file was the most efficient way to feed your input function. However, I keep seeing great looking articles such as this one where the input function takes a reference

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what the bars in keras training show?

Issue I am using keras and part of my network and parameters are as follows: parser.add_argument(“–batch_size”, default=396, type=int, help=”batch size”) parser.add_argument(“–n_epochs”, default=10, type=int, help=”number of epoch”) parser.add_argument(“–epoch_steps”, default=10, type=int, help=”number of epoch step”) parser.add_argument(“–val_steps”, default=4, type=int, help=”number of valdation step”)

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