johnolafenwa

from torch.optim import Adam


# Check if gpu support is available
cuda_avail = torch.cuda.is_available()

# Create model, optimizer and loss function
model = SimpleNet(num_classes=10)

johnolafenwa

# Create a learning rate adjustment function that divides the learning rate by 10 every 30 epochs
def adjust_learning_rate(epoch):
    lr = 0.001

    if epoch > 180:
        lr = lr / 1000000
    elif epoch > 150:
        lr = lr / 100000
    elif epoch > 120:

johnolafenwa

def save_models(epoch):
    torch.save(model.state_dict(), "cifar10model_{}.model".format(epoch))
    print("Chekcpoint saved")

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def test():
    model.eval()
    test_acc = 0.0
    for i, (images, labels) in enumerate(test_loader):

        if cuda_avail:
            images = Variable(images.cuda())
            labels = Variable(labels.cuda())

johnolafenwa

def train(num_epochs):
    best_acc = 0.0

    for epoch in range(num_epochs):
        model.train()
        train_acc = 0.0
        train_loss = 0.0
        for i, (images, labels) in enumerate(train_loader):
            # Move images and labels to gpu if available

johnolafenwa

#Import needed packages
import torch
import torch.nn as nn
from torchvision.datasets import CIFAR10
from torchvision.transforms import transforms
from torch.utils.data import DataLoader
from torch.optim import Adam
from torch.autograd import Variable
import numpy as np

johnolafenwa

# Import needed packages
import torch
import torch.nn as nn
from torchvision.transforms import transforms
import matplotlib.pyplot as plt
import numpy as np
from torch.autograd import Variable
from torchvision.models import squeezenet1_1
import torch.functional as F
import requests

johnolafenwa

# Import needed packages
import torch
import torch.nn as nn
from torchvision.transforms import transforms
from torch.autograd import Variable
from torchvision.models import squeezenet1_1
import requests
import shutil
from io import open
import os

johnolafenwa

def predict_image(image_path):
    print("Prediction in progress")
    image = Image.open(image_path)

    # Define transformations for the image, should (note that imagenet models are trained with image size 224)
    transformation = transforms.Compose([
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

johnolafenwa

if __name__ == "__main__":

    imagefile = "image.png"

    imagepath = os.path.join(os.getcwd(), imagefile)
    # Donwload image if it doesn't exist
    if not os.path.exists(imagepath):
        data = requests.get(
            "https://github.com/OlafenwaMoses/ImageAI/raw/master/images/3.jpg", stream=True)