I need to avoid downloading the model from the web (due to restrictions on the machine installed). This works, but it downloads the model from the Internet mode
In the example below, pipe = Pipeline([ ('scale', StandardScaler()), ('reduce_dims', PCA(n_components=4)), ('clf', SVC(kernel = 'linear
I am trying to train a decision tree model, save it, and then reload it when I need it later. However, I keep getting the following error: This DecisionTre
Is there any way to use multiple time-series to train one model and use this model for predictions given a new time-series as an input? It is rather a theoretic
I am currently performing multi class SVM with linear kernel using python's scikit library. The sample training data and testing data are as given below: Mode
I am using some text for some NLP analyses. I have cleaned the text taking steps to remove non-alphanumeric characters, blanks, duplicate words and stopwords, a
My Data set contains categorical variables so I am using label encoding and one hot encoder and my code is as follows can I use a loop to ensure that my cod
I do not understand why do I get the error KeyError: '[ 1351 1352 1353 ... 13500 13501 13502] not in index' when I run this code: cv = KFold(n_splits=10) fo
CODE import numpy as np import cv2 from google.colab.patches import cv2_imshow img=cv2.imread('/gdrive/My Drive/Colab Notebooks/merlin_190860876_3e2e2660-237f-4
I am trying to save the the weights of a pytorch model into a .txt or .json. When writing it to a .txt, #import torch model = torch.load("model_path") string =
How do you compute the true- and false- positive rates of a multi-class classification problem? Say, y_true = [1, -1, 0, 0, 1, -1, 1, 0,
How do you compute the true- and false- positive rates of a multi-class classification problem? Say, y_true = [1, -1, 0, 0, 1, -1, 1, 0,
Currently I have a dataset below and I try to accumulate the value if ColA is 0 while reset the value to 0 (restart counting again) if the ColA is 1 again. Col
In my CNN model I want to extract X_train and y_train from train_generator. I want to use ensemble learning, bagging and boosting to evaluate the model. the mai
I am developing a mini autonomous car using 3 CNNs and a camera sensor using this approach. One of the CNNs detects lanes on the images and outputs images wit
I'm trying to build a model which can be trained on both audio and video samples but I get this error ValueError: Please initialize `TimeDistributed` layer with
I am working on an image classification task to classify among cars and buses. The problem is that in most car images, there is buses in the background and vice
I am trying to use Tensorflow to create a recommendation system. What I want to do is to read data from two csv files, one containing 'item_id' and the other co
I've loaded in my train and validation sets from CIFAR10 like so: train = tfds.load('cifar10', split='train[:90%]', shuffle_files=True) validation = tfds.load('
For a very simple classification problem where I have a target vector [0,0,0,....0] and a prediction vector [0,0.1,0.2,....1] would cross-entropy loss converge