Perhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to... morePerhaps too general a question, but can anyone explain what would cause a Convolutional Neural Network to diverge?
Specifics:
I am using Tensorflow's iris_training model with some of my own data and keep getting
ERROR:tensorflow:Model diverged with loss = NaN.
Traceback...
tensorflow.contrib.learn.python.learn.monitors.NanLossDuringTrainingError: NaN loss during training.
I've tried adjusting the optimizer, using a zero for learning rate, and using no optimizer. Any insights into network layers, data size, etc is appreciated. less
What is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?
In my opinion, 'VALID' means there will be no zero padding outside the edges when we... moreWhat is the difference between 'SAME' and 'VALID' padding in tf.nn.max_pool of tensorflow?
In my opinion, 'VALID' means there will be no zero padding outside the edges when we do max pool.
According to A guide to convolution arithmetic for deep learning, it says that there will be no padding in pool operator, i.e. just use 'VALID' of tensorflow. But what is 'SAME' padding of max pool in tensorflow?
i have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to... morei have some data and Y variable is a factor - Good or Bad. I am building a Support vector machine using 'train' method from 'caret' package. Using 'train' function i was able to finalize values of various tuning parameters and got the final Support vector machine . For the test data i can predict the 'class'. But when i try to predict probabilities for test data, i get below error (for example my model tells me that 1st data point in test data has y='good', but i want to know what is the probability of getting 'good' ...generally in case of support vector machine, model will calculate probability of prediction..if Y variable has 2 outcomes then model will predict probability of each outcome. The outcome which has the maximum probability is considered as the final solution)
**Warning message: In probFunction(method, modelFit, ppUnk) : kernlab class probability calculations failed; returning NAs**
sample code as below
library(caret) trainset <- data.frame( class=factor(c("Good", "Bad", "Good",... less
Right now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs... moreRight now I'm importing a fairly large CSV as a dataframe every time I run the script. Is there a good solution for keeping that dataframe constantly available in between runs so I don't have to spend all that time waiting for the script to run?
How to save/restore a model after training? (26... moreThis question already has answers here:
How to save/restore a model after training? (26 answers)
Closed 3 years ago.
I'm relatively new to machine learning and the Tensorflow framework. I was trying to take my trained model heavily influenced by the code presented here, using the MNIST handwritten digit dataset and perform inferences on testing examples that I have created. However, I am doing the training on a remote machine with a GPU and am trying to save the data to a directory so that I can transfer the data and inference on a local machine
It seems that I was able to save some of the model with tf.saved_model.simple_save, however, I'm unsure of how to use the saved data to do inferencing and to use the data to make a prediction given a new image. It seems like there are multiple ways to save a model, but I am unsure of what the convention or of what the "correct way" is to do it with the Tensorflow framwork.
So far, this is the line that I think I would need, but am unsure if it is... less