pranshuj73

JavaScript 27 mins        █████████████████▎░░░  82.3%
JSON       5 mins         ███▋░░░░░░░░░░░░░░░░░  17.8%

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import os

files = os.listdir()

for file in files:
    if '_11zon' in file:
        print(file)
        os.rename(file, file.replace('_11zon', ''))

pranshuj73

Rock-Paper_Scissors in Python

Algorithm:

• Collect user input and check if user's move is rock, paper or scissors and simultaneously use random library to generate a random computer move.
• Use if-elif-else or if-else statements to compare the user's move and computer's move
  • if user's move is superior to computer's move (eg: rock > scissors and similarly paper > rock), print that user won
  • else print user lost

Things to learn to make the game:

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import typing


class ArticleField:
	"""The `ArticleField` class for the Advanced Requirements."""

	def __init__(self, field_type: typing.Type[typing.Any]):
		self.field_type = field_type
		self.attribute = ''

pranshuj73

face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

while vid.isOpened():
    _, frame = vid.read()

    # takes in a gray coloured filter of the frame
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # initializing the haarcascade face detector
    faces = face_cascade.detectMultiScale(frame)

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# function to turn photos to tensor
def img2tensor(x):
    transform = transforms.Compose(
            [transforms.ToPILImage(),
            transforms.Grayscale(num_output_channels=1),
            transforms.ToTensor(),
            transforms.Normalize((0.5), (0.5))])
    return transform(x)

# the model for predicting

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torch.save(model.state_dict(), 'FER2013-Resnet9.pth')

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@torch.no_grad()    # this is for stopping the model from keeping track of old parameters
def evaluate(model, val_loader):
    # This function will evaluate the model and give back the val acc and loss
    model.eval()
    outputs = [model.validation_step(batch) for batch in val_loader]
    return model.validation_epoch_end(outputs)

# getting the current learning rate
def get_lr(optimizer):
    for param_group in optimizer.param_groups:

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def conv_block(in_chnl, out_chnl, pool=False, padding=1):
    layers = [
        nn.Conv2d(in_chnl, out_chnl, kernel_size=3, padding=padding),
        nn.BatchNorm2d(out_chnl),
        nn.ReLU(inplace=True)]
    if pool: layers.append(nn.MaxPool2d(2))
    return nn.Sequential(*layers)

class FERModel(FERBase):
    def __init__(self, in_chnls, num_cls):

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def accuracy(outputs, labels):
    _, preds = torch.max(outputs, dim=1)
    return torch.tensor(torch.sum(preds==labels).item()/len(preds))

class FERBase(nn.Module):
    
    # this takes is batch from training dl
    def training_step(self, batch):
        images, labels = batch
        out = self(images)                     # calls the training model and generates predictions