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## load the entire dataset
x, y = next(iter(train_dataset))

## print one example
dim = x.shape[1]
print("Dimension of image:", x.shape, "\n", 
      "Dimension of labels", y.shape)

plt.imshow(x[160].reshape(1, 3, 224, 224).squeeze().T.numpy())

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## configure root folder on your gdrive
data_dir = 'gdrive/My Drive/DAIR RESOURCES/TF to PT/datasets/hymenoptera_data'

## custom transformer to flatten the image tensors
class ReshapeTransform:
    def __init__(self, new_size):
        self.new_size = new_size

    def __call__(self, img):
        result = torch.reshape(img, self.new_size)

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## importing dataset
from google.colab import drive
drive.mount('gdrive', force_remount=True)

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## configuration to detect cuda or cpu
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print (device)

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## Import the usual libraries
import torch
import torchvision
import torch.nn as nn
from torchvision import datasets, models, transforms
import os
import numpy as np
import matplotlib.pyplot as plt

%matplotlib inline

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## test the model
sample = torch.tensor([10.0], dtype=torch.float)
predicted = model(sample)
print(predicted.detach().item())

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## training
for i in range(150):
    model = model.train()

    ## forward
    output = model(x)
    loss = criterion(output, y)
    optimizer.zero_grad()

    ## backward + update model params 

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## loss function
criterion = nn.MSELoss()

## optimizer algorithm
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)

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## Neural network with 1 hidden layer
layer1 = nn.Linear(1,1, bias=False)
model = nn.Sequential(layer1)

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## our data in tensor form
x = torch.tensor([[-1.0],  [0.0], [1.0], [2.0], [3.0], [4.0]], dtype=torch.float)
y = torch.tensor([[-3.0], [-1.0], [1.0], [3.0], [5.0], [7.0]], dtype=torch.float)