How can you write a python script to read Tensorboard log files, extracting the loss and accuracy and other numerical data, without launching the GUI tensorboard –logdir=…? Answer You can use TensorBoard’s Python classes or script to extract the data: How can I export data from TensorBoard? If you’d like to export data to visualize elsewhere (e.g. iPython Notebook), that’s
Tag: machine-learning
Export weights of neural network using tensorflow
I wrote neural-network using tensorflow tools. everything working and now I want to export the final weights of my neural network to make a single prediction method. How can I do this? Answer You will need to save your model at the end of training by using the tf.train.Saver class. While initializing the Saver object, you will need to pass
What does the value of ‘leaf’ in the following xgboost model tree diagram means?
I am guessing that it is conditional probability given that the above (tree branch) condition exists. However, I am not clear on it. If you want to read more about the data used or how do we get this diagram then go to : http://machinelearningmastery.com/visualize-gradient-boosting-decision-trees-xgboost-python/ Answer Attribute leaf is the predicted value. In other words, if the evaluation of a
How to use `Dirichlet Process Gaussian Mixture Model` in Scikit-learn? (n_components?)
My understanding of “an infinite mixture model with the Dirichlet Process as a prior distribution on the number of clusters” is that the number of clusters is determined by the data as they converge to a certain amount of clusters. This R Implementation https://github.com/jacobian1980/ecostates decides on the number of clusters in this way. Although, the R implementation uses a Gibbs
How to predict new values using statsmodels.formula.api (python)
I trained the logistic model using the following, from breast cancer data and ONLY using one feature ‘mean_area’ There is a built in predict method in the trained model. However that gives the predicted values of all the training samples. As follows Suppose I want the prediction for a new value say 30 How do I used the trained model
python divide by zero encountered in log – logistic regression
I’m trying to implement a multiclass logistic regression classifier that distinguishes between k different classes. This is my code. I can verify that cost and gradient are returning values that are in the right dimension (cost returns a scalar, and gradient returns a 1 by n row vector), but i get the error why is this happening and how can
tflearn / tensorflow does not learn xor
Following code was written to learn the XOR function, but about half of the time the network does not learn and the loss after each epoch stays the same. Sometimes I get correct results like this: But often this: My 2x2x1 network should be able to perform XOR, and there is even some evidence that suggests that this network should
Compute the gradient of the SVM loss function
I am trying to implement the SVM loss function and its gradient. I found some example projects that implement these two, but I could not figure out how they can use the loss function when computing the gradient. Here is the formula of loss function: What I cannot understand is that how can I use the loss function’s result while
What are logits? What is the difference between softmax and softmax_cross_entropy_with_logits?
In the tensorflow API docs they use a keyword called logits. What is it? A lot of methods are written like: If logits is just a generic Tensor input, why is it named logits? Secondly, what is the difference between the following two methods? I know what tf.nn.softmax does, but not the other. An example would be really helpful. Answer
Ordered Logit in Python?
I’m interested in running an ordered logit regression in python (using pandas, numpy, sklearn, or something that ecosystem). But I cannot find any way to do this. Is my google-skill lacking? Or is this not something that’s been implemented in a standard package? Answer Update: Logit and Probit Ordinal regression models are now built in to statsmodels. https://www.statsmodels.org/devel/examples/notebooks/generated/ordinal_regression.html Examples are