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from torchvision.datasets import MNIST
from torchvision import transforms
from torch.utils.data import DataLoader

class Model(Layer):

    def __init__(self, lr=0.00001):
        self.lr = lr
        self.layers = [
            Linear(784,100, lr=self.lr),

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class Model(Layer):

    def __init__(self, lr=0.0001):
        self.lr = lr
        self.layers = [
            Linear(3,15, lr=self.lr),
            Relu(),
            Linear(15,1, lr=self.lr)        
        ]

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class Layer:

    def forward(self):
        raise NotImplemented

    def backward(self, grad):
        raise NotImplemented

    def __call__(self, *args):
        return self.forward(*args)

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_x = torch.tensor(x,requires_grad=True)
_a = torch.tensor(a,requires_grad=True)
_b = torch.tensor(b,requires_grad=True)
y = (_a*_x + _b) * _a
print('y = ', y)
print()
print('y.grad_fn = ', y.grad_fn)
print()
y.backward(torch.tensor(loss))
print('_x.grad = ',_x.grad)

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y =  Tensor([[0.45119641 0.25401614 0.36177802]
  [0.2445061  0.22747291 0.41594346]
  [0.35465603 0.26046455 1.26796941]])

y.grad_fn =  <__main__.MulOp object at 0x7f169e332358>

_x.grad =  Tensor([[0.08710775 0.00380129 0.14389783]
  [0.09573756 0.05524222 0.07022905]
  [0.03903106 0.09778539 0.79086884]])

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x = np.random.random((3,3))
a = np.random.random((3,3))
b = np.random.random((3,))

loss = np.random.random((3,3))

_x = Tensor(x,requires_grad=True)
_a = Tensor(a,requires_grad=True)
_b = Tensor(b,requires_grad=True)

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 class Tensor:

    def __init__(self, data, requires_grad=False):
        self.data = data
        if not isinstance(data, np.ndarray):
            self.data = np.array(data)
        # whether to run backpropagation or not
        self.requires_grad = requires_grad
        # tensor gradient
        self._grad = None

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from IPython.display import display, Javascript, Image
from google.colab.output import eval_js
from base64 import b64decode

def take_photo(filename='photo.jpg', quality=0.8):
  js = Javascript('''
    async function takePhoto(quality) {
      const div = document.createElement('div');
      const capture = document.createElement('button');
      capture.textContent = 'Capture';

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function IMPORTJSON(url, keys){
    //keys = "id,message,likes,comments,shares,insights.post_impressions,insights.post_impressions_fan,insights.post_engaged_users"
  
    var res = UrlFetchApp.fetch(url);
    var content = res.getContentText();
    var json = JSON.parse(content);
    
    var data = [];
    keys = keys.split(',')
    for(var i=0;i<json['data'].length;i++){

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from skimage import data
from skimage import exposure
from skimage.transform import match_histograms


img = cv2.imread('surfing-1.jpg')[...,::-1]
matched = match_histograms(img, imgy, multichannel=True)

plt.imshow(img)
plt.show()