Training and Predicting with MXNet Logistic Regression Classifier

This code defines a simple logistic regression classifier using MXNet and scikit-learn for training and prediction. First, it defines the MXNet model, then initializes the parameters, converts the input and label to MXNet's NDArray, uses the SGD optimizer to train, and finally predicts using the trained model.

Technology Stack : MXNet, scikit-learn, NumPy

Code Type : Machine Learning Classifier Training and Prediction

Code Difficulty : Intermediate

                
                    
import mxnet as mx
import numpy as np
from sklearn.linear_model import LogisticRegression

def train_classifier(input_array, label_array):
    # Define a simple logistic regression model using MXNet and scikit-learn
    model = mx.mod.Module(
        symbol=mx.symbol.logistic(data=mx.sym.var(name="data")),
        context=mx.cpu(),
        label_name="softmax_label",
        num_classes=10
    )
    
    # Initialize the model
    model.init_params()
    
    # Convert input and label to MXNet NDArray
    data = mx.nd.array(input_array)
    label = mx.nd.array(label_array)
    
    # Fit the model
    model.fit(data=[data], label=[label], epoch=1, optimizer='sgd', optimizer_params={'learning_rate': 0.1})
    
    # Predict using the trained model
    pred = model.predict(data)
    return pred