hmcyrus

# age_classification_with_resnet.py

import os
import argparse
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision import datasets, models, transforms
from sklearn.metrics import classification_report, confusion_matrix

hmcyrus

# -*- coding: utf-8 -*-
"""
A refactored, reusable script for training and evaluating a dog age classifier.

This script uses a pre-trained ResNet-18 model for transfer learning.
It includes functionalities for training, validation, testing, logging, and
checkpointing, all controllable via command-line arguments.

Example Usage:

hmcyrus

# -*- coding: utf-8 -*-
"""age_classification_with_resnet_18.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1wIsjO2LhN8fsH0pP8Hy2Ub9bKocK28Vy
"""

from google.colab import drive

hmcyrus

fn divide(num: i32) -> i32 {
    return num/2;
}

fn main() {
    println!("Hello, world! {}", divide(10));
}

hmcyrus

import java.util.Arrays;
import java.util.stream.Collectors;

public class ApprovalRule {

    static boolean ifAnySubRuleIsExhausted(String actingRule, String approveForGroup){
        System.out.println("Applying approval for group -> " + approveForGroup + " on rule -> " + actingRule);

        return Arrays.stream(actingRule.split("--"))
                     .map(r -> r.replaceFirst(approveForGroup, ""))

hmcyrus

public class RemoveDuplicatesFromSortedArray {
    public int removeDuplicates(int[] nums) {
        int uniqueValCount=0, currentVal=nums[0];
        for (int i =0; i<nums.length; i++){
            if(nums[i]>currentVal){
                uniqueValCount++;
                nums[uniqueValCount]= nums[i];
                currentVal = nums[i];
            }
        }

hmcyrus

#include <stdio.h>

main()
{
printf("hi");
}