Keras MNIST

This code defines a function that uses the Keras library to classify the MNIST dataset. It first loads the dataset, then preprocesses the data, then constructs a simple neural network model, compiles and trains the model, and finally evaluates the model performance.

The type of code 2024-12-16 12:17:47 31 views

PyTorch torchvision

This function is used to load and transform data from a specified PyTorch dataset. It supports the CIFAR10 and MNIST datasets. The function returns a DataLoader object for batch data loading.

Data Loading and Conversion Functions 2024-12-16 12:17:06 27 views

PyTorch torchvision

This function uses the PyTorch and torchvision libraries to generate random image data. It first randomly selects a dataset (CIFAR-10 or MNIST), then creates a DataLoader to handle batch processing. Finally, it extracts images from the loaded data and returns them.

The type of code 2024-12-16 12:07:59 3 views

PyTorch torchvision

This code implements a simple PyTorch-based MNIST handwritten digit recognition model. It first defines data preprocessing and loading, then defines a simple convolutional neural network model, and then performs model training, including forward propagation, loss calculation, backpropagation, and parameter update.

The type of code 2024-12-16 11:56:56 3 views

Keras Numpy

This function randomly selects a specified number of rows from the MNIST dataset and reshapes them to have a single channel.

The type of code 2024-12-16 11:40:09 3 views

PyTorch torchvision.transforms

This function randomly loads the MNIST dataset and transforms its images to a specified size. First, a data transformer is defined, which adjusts the image size, converts it to a tensor, and normalizes it. Then, the MNIST dataset is loaded, and a DataLoader is created that shuffles the data and provides it in batches.

The type of code 2024-12-16 11:37:16 4 views

Keras Numpy

This code creates a simple Keras model for processing the MNIST dataset. The model includes an input layer, two hidden layers, and an output layer, using ReLU activation function and softmax activation function. The model uses the Adam optimizer and categorical crossentropy loss function.

Keras model creation 2024-12-16 11:22:36 5 views

PyTorch torchvision

This function uses PyTorch to train a simple Convolutional Neural Network (CNN) model for recognizing handwritten digits from the MNIST dataset. It first defines data transformations and loading, then sets training parameters including the loss function and optimizer. After that, it iterates over the data for a given number of epochs, updating model parameters to minimize the loss.

Machine learning 2024-12-16 11:20:06 4 views

PyTorch torchvision

This code defines a convolutional neural network (CNN) model using PyTorch and trains it on the MNIST dataset. It first defines data preprocessing and loading, then defines a simple CNN model including convolutional layers, ReLU activation functions, pooling layers, and fully connected layers. Then it defines the loss function and optimizer, and finally performs model training.

Machine learning 2024-12-07 16:21:47 5 views

PyTorch torchvision

This code defines a function that randomly selects an image processing transformation, applies it to the MNIST dataset, and then defines a simple convolutional neural network, performing a forward pass on it.

Function 2024-12-07 16:19:44 3 views