If my goal is to fine-tune the network for the entire dataset It is not clear what you mean by “fine-tune”, or even what exactly is your purpose for performing cross-validation (CV); in general, CV serves one of the following purposes: Model selection (choose the values of hyperparameters) Model assessment Since you don’t define any … Read more
Your understanding is pretty much spot on, albeit very, very basic. TensorFlow is more of a low-level library. Basically, we can think of TensorFlow as the Lego bricks (similar to NumPy and SciPy) that we can use to implement machine learning algorithms whereas Scikit-Learn comes with off-the-shelf algorithms, e.g., algorithms for classification such as SVMs, … Read more
Your input to confusion_matrix must be an array of int not one hot encodings.
It looks like sklearn requires the data shape of (row number, column number). If your data shape is (row number, ) like (999, ), it does not work. By using numpy.reshape(), you should change the shape of the array to (999, 1), e.g. using In my case, it worked with that.
I know I could implement a root mean squared error function like this: What I’m looking for if this rmse function is implemented in a library somewhere, perhaps in scipy or scikit-learn?
You might need to reinstall numpy. It doesn’t seem to have installed correctly. sklearn is how you type the scikit-learn name in python. Also, try running the standard tests in scikit-learn and check the output. You will have detailed error information there. Do you have nosetests installed? Try: nosetests -v sklearn. You type this in bash, not in the … Read more
According to MAINT: silence Cython warnings about changes dtype/ufunc size. – numpy/numpy: These warnings are visible whenever you import scipy (or another package) that was compiled against an older numpy than is installed. and the checks are inserted by Cython (hence are present in any module compiled with it). Long story short, these warnings should be benign … Read more
scikit-learn expects 2d num arrays for the training dataset for a fit function. The dataset you are passing in is a 3d array you need to reshape the array into a 2d.
Scikit-Learn is just telling you it doesn’t recognise the argument “stratify”, not that you’re using it incorrectly. This is because the parameter was added in version 0.17 as indicated in the documentation you quoted. So you just need to update Scikit-Learn.
I had a similar problem using the Pystruct pystruct.learners.OneSlackSSVM. It occured because my training labels were floats, in stead of integers. In my case, it was because I initialized the labels with np.ones, without specifying dtype=np.int8. Hope it helps.