Magicoder S CL 7B

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Here is a simple snake game in Python using the turtle library. ```python import turtle import time import random delay = 0.1 # Score score = 0 high_score = 0 # Set up the screen wn = turtle.Screen() wn.title("Snake Game by Magicoder") wn.bgcolor("green") wn.setup(width=600, height=600) wn.tracer(0) # Turns off the screen updates # Snake head head = turtle.Turtle() head.speed(0) head.shape("square") head.color("black") head.penup() head.goto(0, 0) head.direction = "stop" # Snake food food = turtle.Turtle() food.speed(0) food.shape("circle") food.color("red") food.penup() food.goto(0, 100) segments = [] # Pen pen = turtle.Turtle() pen.speed(0) pen.shape("square") pen.color("white") pen.penup() pen.hideturtle() pen.goto(0, 260) pen.write("Score: 0 High Score: 0", align="center", font=("Courier", 24, "normal")) # Functions def go_up(): if head.direction!= "down": head.direction = "up" def go_down(): if head.direction!= "up": head.direction = "down" def go_left(): if head.direction!= "right": head.direction = "left" def go_right(): if head.direction!= "left": head.direction = "right" def move(): if head.direction == "up": y = head.ycor() head.sety(y + 20) if head.direction == "down": y = head.ycor() head.sety(y - 20) if head.direction == "left": x = head.xcor() head.setx(x - 20) if head.direction == "right": x = head.xcor() head.s…

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Here is a simple example of how you can build a convolutional neural network (CNN) using PyTorch to classify handwritten digits from the MNIST dataset. ```python import torch import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms import matplotlib.pyplot as plt # Define the CNN architecture class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout2d(0.25) self.dropout2 = nn.Dropout2d(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = nn.functional.relu(x) x = self.conv2(x) x = nn.functional.relu(x) x = nn.functional.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = nn.functional.relu(x) x = self.dropout2(x) x = self.fc2(x) output = nn.functional.log_softmax(x, dim=1) return output # Load the MNIST dataset transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform) trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True) testset = torchvision.datasets.MNIST(root='./data', train=False, dow…

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Here is the Python code to create, train, and evaluate a Convolutional Neural Network (CNN) to classify handwritten digits from MNIST dataset. ```python import torch import torch.nn as nn import torchvision.transforms as transforms import torchvision.datasets as dsets import torch.optim as optim import matplotlib.pyplot as plt # Step 1: Loading Data # Transform the data to torch tensors and normalize it by the mean and std deviation transform = transforms.Compose([ transforms.ToTensor(), transforms.Normalize(mean=(0.5, ), std=(0.5, )) ]) train_dataset = dsets.MNIST(root='./data', train=True, transform=transform, download=True) test_dataset = dsets.MNIST(root='./data', train=False, transform=transform, download=True) # Prepare Data Loaders train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True) test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False) # Step 2: Define Model class CNN(nn.Module): def __init__(self): super().__init__() self.layer1 = nn.Sequential( nn.Conv2d(1, 32, kernel_size=5, padding=2), nn.ReLU(), nn.MaxPool2d(2) ) self.layer2 = nn.Sequential( nn.Conv2d(32, 64, kernel_size=5, padding=2), nn.ReLU(), nn.MaxPool2d(2) ) self.fc = nn.Linear(7*7*64, 10, bias=True) torch.nn.init.xavier_uniform_(self.fc.weight) def forward(self, x):…