
import pandas as pd
import numpy as np
import torch
from torch import nn
from torch import optim
import torchvision
from torchvision import datasets, transforms, models
from PIL import Image


path = 'flowers'

def dataloader(path):
    data_dir = path
    train_dir = data_dir + '/train'
    valid_dir = data_dir + '/valid'
    test_dir = data_dir + '/test'

    # definfing the transforms for the training set
    data_transforms_train = transforms.Compose([
                            transforms.RandomResizedCrop(224),
                            transforms.RandomRotation(30),
                            transforms.RandomHorizontalFlip(),
                            transforms.ToTensor(),
                            transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])

    # definfing the transforms for the validation and testing set
    data_transforms_valid = transforms.Compose([
                           transforms.Resize(256),
                           transforms.CenterCrop(224),
                           transforms.ToTensor(),
                           transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])

    data_transforms_test = transforms.Compose([
                           transforms.Resize(256),
                           transforms.CenterCrop(224),
                           transforms.ToTensor(),
                           transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])


    #Loading the datasets for the training date, the test data and the validation data with ImageFolder
    train_data = datasets.ImageFolder(train_dir, transform=data_transforms_train)
    test_data = datasets.ImageFolder(test_dir,transform=data_transforms_test )
    valid_data = datasets.ImageFolder(valid_dir, transform=data_transforms_valid)

    # Using the three image datasets and the trainforms to define the dataloaders
 

    batch_size = 64

    Trainloader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle = True)
    Testloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)
    Validloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size)
    
    return Trainloader, Testloader, Validloader, train_data, batch_size



b5fc5ac7752e17cf8c7384922057ef617634449f
<mask token>

<mask token>


def dataloader(path):
    data_dir = path
    train_dir = data_dir + '/train'
    valid_dir = data_dir + '/valid'
    test_dir = data_dir + '/test'
    data_transforms_train = transforms.Compose([transforms.
        RandomResizedCrop(224), transforms.RandomRotation(30), transforms.
        RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize
        ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    data_transforms_valid = transforms.Compose([transforms.Resize(256),
        transforms.CenterCrop(224), transforms.ToTensor(), transforms.
        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    data_transforms_test = transforms.Compose([transforms.Resize(256),
        transforms.CenterCrop(224), transforms.ToTensor(), transforms.
        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    train_data = datasets.ImageFolder(train_dir, transform=
        data_transforms_train)
    test_data = datasets.ImageFolder(test_dir, transform=data_transforms_test)
    valid_data = datasets.ImageFolder(valid_dir, transform=
        data_transforms_valid)
    batch_size = 64
    Trainloader = torch.utils.data.DataLoader(train_data, batch_size=
        batch_size, shuffle=True)
    Testloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)
    Validloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size
        )
    return Trainloader, Testloader, Validloader, train_data, batch_size

<mask token>
path = 'flowers'


def dataloader(path):
    data_dir = path
    train_dir = data_dir + '/train'
    valid_dir = data_dir + '/valid'
    test_dir = data_dir + '/test'
    data_transforms_train = transforms.Compose([transforms.
        RandomResizedCrop(224), transforms.RandomRotation(30), transforms.
        RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize
        ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    data_transforms_valid = transforms.Compose([transforms.Resize(256),
        transforms.CenterCrop(224), transforms.ToTensor(), transforms.
        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    data_transforms_test = transforms.Compose([transforms.Resize(256),
        transforms.CenterCrop(224), transforms.ToTensor(), transforms.
        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    train_data = datasets.ImageFolder(train_dir, transform=
        data_transforms_train)
    test_data = datasets.ImageFolder(test_dir, transform=data_transforms_test)
    valid_data = datasets.ImageFolder(valid_dir, transform=
        data_transforms_valid)
    batch_size = 64
    Trainloader = torch.utils.data.DataLoader(train_data, batch_size=
        batch_size, shuffle=True)
    Testloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)
    Validloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size
        )
    return Trainloader, Testloader, Validloader, train_data, batch_size

import pandas as pd
import numpy as np
import torch
from torch import nn
from torch import optim
import torchvision
from torchvision import datasets, transforms, models
from PIL import Image
path = 'flowers'


def dataloader(path):
    data_dir = path
    train_dir = data_dir + '/train'
    valid_dir = data_dir + '/valid'
    test_dir = data_dir + '/test'
    data_transforms_train = transforms.Compose([transforms.
        RandomResizedCrop(224), transforms.RandomRotation(30), transforms.
        RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize
        ([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    data_transforms_valid = transforms.Compose([transforms.Resize(256),
        transforms.CenterCrop(224), transforms.ToTensor(), transforms.
        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    data_transforms_test = transforms.Compose([transforms.Resize(256),
        transforms.CenterCrop(224), transforms.ToTensor(), transforms.
        Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
    train_data = datasets.ImageFolder(train_dir, transform=
        data_transforms_train)
    test_data = datasets.ImageFolder(test_dir, transform=data_transforms_test)
    valid_data = datasets.ImageFolder(valid_dir, transform=
        data_transforms_valid)
    batch_size = 64
    Trainloader = torch.utils.data.DataLoader(train_data, batch_size=
        batch_size, shuffle=True)
    Testloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)
    Validloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size
        )
    return Trainloader, Testloader, Validloader, train_data, batch_size


import pandas as pd
import numpy as np
import torch
from torch import nn
from torch import optim
import torchvision
from torchvision import datasets, transforms, models
from PIL import Image


path = 'flowers'

def dataloader(path):
    data_dir = path
    train_dir = data_dir + '/train'
    valid_dir = data_dir + '/valid'
    test_dir = data_dir + '/test'

    # definfing the transforms for the training set
    data_transforms_train = transforms.Compose([
                            transforms.RandomResizedCrop(224),
                            transforms.RandomRotation(30),
                            transforms.RandomHorizontalFlip(),
                            transforms.ToTensor(),
                            transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])

    # definfing the transforms for the validation and testing set
    data_transforms_valid = transforms.Compose([
                           transforms.Resize(256),
                           transforms.CenterCrop(224),
                           transforms.ToTensor(),
                           transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])

    data_transforms_test = transforms.Compose([
                           transforms.Resize(256),
                           transforms.CenterCrop(224),
                           transforms.ToTensor(),
                           transforms.Normalize([0.485, 0.456, 0.406],[0.229, 0.224, 0.225])])


    #Loading the datasets for the training date, the test data and the validation data with ImageFolder
    train_data = datasets.ImageFolder(train_dir, transform=data_transforms_train)
    test_data = datasets.ImageFolder(test_dir,transform=data_transforms_test )
    valid_data = datasets.ImageFolder(valid_dir, transform=data_transforms_valid)

    # Using the three image datasets and the trainforms to define the dataloaders
 

    batch_size = 64

    Trainloader = torch.utils.data.DataLoader(train_data, batch_size=batch_size, shuffle = True)
    Testloader = torch.utils.data.DataLoader(test_data, batch_size=batch_size)
    Validloader = torch.utils.data.DataLoader(valid_data, batch_size=batch_size)
    
    return Trainloader, Testloader, Validloader, train_data, batch_size


