So to prevent another dozen of answers suggesting invalid in the context of this thread suggestion to !kill -9 -1, let’s close this thread: The answer is simple: As of this writing Google simply gives only 5% of GPU to some of us, whereas 100% to the others. Period. dec-2019 update: The problem still exists … Read more
For machine learning, you almost definitely want to use sklearn.OneHotEncoder. For other tasks like simple analyses, you might be able to use pd.get_dummies, which is a bit more convenient. Note that sklearn.OneHotEncoder has been updated in the latest version so that it does accept strings for categorical variables, as well as integers. The crux of … Read more
Intro I assume that you have a matrix X where each row/line is a sample/observation and each column is a variable/feature (this is the expected input for any sklearn ML function by the way — X.shape should be [number_of_samples, number_of_features]). Core of method The main idea is to normalize/standardize i.e. μ = 0 and σ … Read more
You don’t need to use to_categorical since I guess you are doing multi-label classification. To avoid any confusion once and for all(!), let me explain this. If you are doing binary classification, meaning each sample may belong to only one of two classes e.g. cat vs dog or happy vs sad or positive review vs … Read more
[*] [**] What is overfitting Overfitting ( or underfitting) occurs when a model is too specific (or not specific enough) to the training data, and doesn’t extrapolate well to the true domain. I’ll just say overfitting from now on to save my poor typing fingers [*] I think the wikipedia image is good: Clearly, the … Read more
First, you should rather not load your model every time a request arrives, but rahter have it loaded once at startup (you could use the startup event for this) and store it on the app instance—using the generic app.state attribute (see implementation of State too)—which you can later retrieve, as described here and here. For … Read more
In the Latest Tensorflow Version (2.2), when we Save the Model using tf.keras.models.save_model, the Model will be Saved in not just a pb file but it will be Saved in a Folder, which comprises Variables Folder and Assets Folder, in addition to the saved_model.pb file, as shown in the screenshot below: For example, if the … Read more
Decision boundary is generally much more complex then just a line, and so (in 2d dimensional case) it is better to use the code for generic case, which will also work well with linear classifiers. The simplest idea is to plot contour plot of the decision function # X – some data in 2dimensional np.array … Read more
Foreword I think you are falling prey to the XY problem, since trying to find 153.600 dimensions in your data is completely non-physical, please ask about the problem (X) and not your proposed solution (Y) in order to get a meaningful answer. I will use this post only to tell you why PCA is not … Read more
One problem with a Tensorflow placeholder is that you can only feed it with a Python list or Numpy array (I think). So you can’t save the state between runs in tuples of LSTMStateTuple. I solved this by saving the state in a tensor like this initial_state = np.zeros((num_layers, 2, batch_size, state_size)) You have two … Read more