I am trying to use SMOTE in python to handle highly imbalanced data set. After splitting the data set into train and test I generate synthetic samples using SMO
I am trying to convert some code from tensorflow 1.x to tensorflow 2.x. It's been going well so far, but I'm stuck on atrous convolution. Unlike other layers, t
I am trying to develop a GAN, I have created the generator and the discriminator and now I am trying to train it. I am using the Mnist dataset but I plan to use
I have written a basic program to understand what's happening in MLP classifier? from sklearn.neural_network import MLPClassifier data: a dataset of body met
I pulled some ML code that ran on kaggle (linux) and tried to run it in a jupyter notebook on a windows machine. Here is the code (some of it): ##### RUN XGBOO
I have time series training data of about 5000 numbers. For each 100 numbers, I am trying to predict the 101st. At the end of the series, I would put in the pre
pipe = Pipeline([('reduce_dim', LinearDiscriminantAnalysis()),('classify', LogisticRegression())]) param_grid = [{'classify__penalty': ['l1', 'l2'],
while using the RandomForestRegressor I noticed something strange. To illustrate the problem, here a small example. I applied the RandomForestRegressor on a tes
I am trying to build an autoencoder with the following code import matplotlib.pyplot as plt import numpy as np import pandas as pd import seaborn as sns import
I am using dice loss for my implementation of a Fully Convolutional Network(FCN) which involves hypernetworks. The model has two inputs and one output which is
I am using dice loss for my implementation of a Fully Convolutional Network(FCN) which involves hypernetworks. The model has two inputs and one output which is
My first multiclass classication. I have values X and Y. Y have 5 values [0,1,2,3,4]. But i get this "multiclass format is not supported". Understand that i nee
I want to use default hyperparams in randomized search, how can I do it? (per_float_feature_quantization param here) grid = {'learning_rate': [0.1, 0.16, 0.2],
Can some one with expertise explain how the following vectorized format of multiple linear regression is derived from given independent variable matrix with int
I know that the input_shape for Inception V3 is (299,299,3). But in Keras it is possible to construct versions of Inception V3 that have custom input_shape if
I am having difficulties extracting misclassified images, I tried to use the following line of code: inc= np.nonzero(model.predict_classes(test_data).reshape(-
Basically, I want to plot like this: I already managed to plot the clusters using sns.scatterplot(X[:,0], X[:,1], hue=y, palette=['red', 'blue', 'purple', 'gre
I am trying to manage the results of machine learning with mlflow and hydra. So I tried to run it using the multi-run feature of hydra. I used the following cod
Below is my code for deploying my ml model on streamlit. I am using Google Collab !pip install streamlit --quiet !pip install pyngrok==4.1.1 from pyngrok import
I just read about the Keras weight initializers in here. In the documentation, only different initializers has been introduced. Such as: mode