return
-
Return of Django: A Spaghetti Western Full Length Film Set in the Wild West
Return of Django is a classic spaghetti western film that captures the essence of the old west with its gritty…
-
Official Audio: The Upsetters Return with Django
“The Upsetters – Return of Django” is a classic reggae track by the legendary Jamaican producer and musician Lee “Scratch”…
-
Using the FastAPI Python framework to return HTML templates with HTMX integration
In this tutorial, we will be covering how to use the FastAPI Python framework to return HTML templates with HTMX…
-
ThePrimeagen explains the importance of specifying return types in TypeScript
ThePrimeagen: Specifying Return Types in TypeScript When working with TypeScript, specifying return types for functions is an important aspect of…
-
NeRF: Neural Radiance Fields implementation in PyTorch with 100 lines of code | NeRF#5 “`python import torch import torch.nn as nn import torch.nn.functional as F class NeRF(nn.Module): def __init__(self): super(NeRF, self).__init__() # Define neural network layers here def forward(self, input): # Implement forward pass logic here return output model = NeRF() optimizer = torch.optim.Adam(model.parameters(), lr=0.001) # Training loop for epoch in range(num_epochs): for i, data in enumerate(train_loader): inputs, targets = data optimizer.zero_grad() outputs = model(inputs) loss = F.mse_loss(outputs, targets) loss.backward() optimizer.step() if i % 10 == 0: print(f”Epoch {epoch}, Iteration {i}, Loss: {loss.item()}”) “` This code snippet outlines the basic structure of a Neural Radiance Fields (NeRF) implementation in PyTorch. The `NeRF` class represents the neural network model, with customizable layers and a forward pass method. The code also includes an optimizer setup using Adam and a training loop that iterates through batches of training data, calculates the loss, performs backpropagation, and updates the model parameters. The model can be further customized with additional layers, activation functions, and training parameters to fit specific use cases.
NeRF in PyTorch NeRF – Neural Radiance Fields in PyTorch NeRF, short for Neural Radiance Fields, is a deep learning…
-
![Function Title: Create Confusion Matrix
def create_confusion_matrix(actual, predicted, classes):
matrix = [[0 for _ in range(len(classes))] for _ in range(len(classes))]
for i in range(len(actual)):
actual_index = classes.index(actual[i])
predicted_index = classes.index(predicted[i])
matrix[actual_index][predicted_index] += 1
return matrix](https://i0.wp.com/jscommands.com/wp-content/uploads/2024/03/1710174224_maxresdefault.jpg?resize=400%2C266&ssl=1)
Function Title: Create Confusion Matrix def create_confusion_matrix(actual, predicted, classes): matrix = [[0 for _ in range(len(classes))] for _ in range(len(classes))] for i in range(len(actual)): actual_index = classes.index(actual[i]) predicted_index = classes.index(predicted[i]) matrix[actual_index][predicted_index] += 1 return matrix
Understanding Confusion Matrix Function in Python Confusion Matrix Function in Python In machine learning, a confusion matrix is a table…
-
Using define component in Vue 3 TS: A Beginner’s Guide
Usar define component en Vue 3 TS Usar define component en Vue 3 TS Vue 3 TS es una biblioteca…
-
“`python # Import necessary libraries import torch import torch.nn as nn import torch.nn.functional as F # Define the multiplicative filter network class class MultiplicativeFilterNetwork(nn.Module): def __init__(self, input_channels, output_channels, kernel_size): super(MultiplicativeFilterNetwork, self).__init__() self.weight = nn.Parameter(torch.ones(output_channels, input_channels, kernel_size, kernel_size)) def forward(self, x): return F.conv2d(x, self.weight, padding=1) # Instantiate and use the multiplicative filter network input_channels = 3 output_channels = 64 kernel_size = 3 model = MultiplicativeFilterNetwork(input_channels, output_channels, kernel_size) input_data = torch.randn(1, input_channels, 32, 32) output = model(input_data) # Print the output size print(output.size()) “`
Machine Learning Papers: Multiplicative Filter Networks Machine Learning Papers: Multiplicative Filter Networks Machine Learning Papers: Multiplicative Filter Networks is a…
-
“`python import torch import torch.nn as nn import torch.nn.functional as F class NeuralRadianceFields(nn.Module): def __init__(self, input_dim, hidden_dim, output_dim): super(NeuralRadianceFields, self).__init__() self.fc1 = nn.Linear(input_dim, hidden_dim) self.fc2 = nn.Linear(hidden_dim, hidden_dim) self.fc3 = nn.Linear(hidden_dim, hidden_dim) self.fc4 = nn.Linear(hidden_dim, output_dim) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return x def main(): input_dim = 3 hidden_dim = 256 output_dim = 1 model = NeuralRadianceFields(input_dim, hidden_dim, output_dim) x = torch.rand(1, input_dim) y = model(x) print(y) if __name__ == “__main__”: main() “`
Neural Radiance Fields | NeRF Neural Radiance Fields | NeRF Neural Radiance Fields (NeRF) is a novel method for synthesizing…
-
Node.js Package for Initiating Payments and Managing Return and Callback URLs – Step 3
Step 3 – Node.js Package | Initiating the payment | Manage the Return and Callback URLs Step 3 – Node.js…
Recent Posts
- Entendendo o React JS: Quando e Por que Utilizar essa Biblioteca JavaScript?
- Building APIs quickly in Tamil with FastAPI in Python
- Django Confronta Sabata | Alta Definição | Faroeste | Filme Completo em Português
- 60 JavaScript Interview Questions answered in 60 days | Day 10 #javascript #interviewquestions #shorts
- Creating a SQLite Database with Python and Saving Data #pysimplegui #python
