pytorch Detailed explanation of RNN parameters (Latest)

When training recurrent neural networks (RNNs) using PyTorch, you need to understand each parameter of the relevant classes and methods and their meaning. Here are the main classes and methods, as well as their parameters and functions:

1.

This is the class in PyTorch that defines simple recurrent neural networks (RNNs).

Main parameters:

input_size: Enter the dimension of the feature.
hidden_size: The dimension of hidden layer features.
num_layers: Number of RNN layers.
nonlinearity: Nonlinear activation function, which can be ‘tanh’ or ‘relu’.
bias: Whether to use bias, default isTrue。
batch_first: IfTrue, the first dimension of input and output will be batch size, defaultFalse。
dropout: The dropout probability between layers except the last layer, defaults to 0.
bidirectional: Whether it is a bidirectional RNN, the default isFalse。

2.

This is the class in PyTorch that defines long and short-term memory networks (LSTMs).

Main parameters:

input_size: Enter the dimension of the feature.
hidden_size: The dimension of hidden layer features.
num_layers: Number of LSTM layers.
bias: Whether to use bias, default isTrue。
batch_first: IfTrue, the first dimension of input and output will be batch size, defaultFalse。
dropout: The dropout probability between layers except the last layer, defaults to 0.
bidirectional: Whether it is a two-way LSTM, the default isFalse。

3.

This is the class in PyTorch that defines a gated loop unit (GRU).

Main parameters:

input_size: Enter the dimension of the feature.
hidden_size: The dimension of hidden layer features.
num_layers: Number of GRU layers.
bias: Whether to use bias, default isTrue。
batch_first: IfTrue, the first dimension of input and output will be batch size, defaultFalse。
dropout: The dropout probability between layers except the last layer, defaults to 0.
bidirectional: Whether it is a bidirectional GRU, default isFalse。

4. Optimizer

PyTorch provides a variety of optimizers for adjusting model parameters to minimize loss functions.

Commonly used optimizers:

: Stochastic Gradient Descent Optimizer.
- params: Parameters to be optimized.
- lr: Learning rate.
- momentum: Momentum factor, default is 0.
- weight_decay: Weight decay (L2 penalty), default is 0.
- dampening: Momentum damping factor, default is 0.
- nesterov: Whether to use Nesterov momentum, default isFalse。
:Adam Optimizer.
- params: Parameters to be optimized.
- lr: Learning rate, default is 1e-3.
- betas: Two coefficients used to calculate the moving average of gradient and gradient square, default is (0.9, 0.999).
- eps: The term for numerical stability, default is 1e-8.
- weight_decay: Weight decay (L2 penalty), default is 0.
- amsgrad: Whether to use the AMSGrad variant, default toFalse。

5.

This is the loss function used in PyTorch for multi-classification tasks.

Main parameters:

weight: The weight of each category, with the shape of [C], where C is the number of categories.
size_average: Whether to average the losses, the default isTrue。
ignore_index: If specified, the labels for that category are ignored.
reduce: Whether to sum the losses in the batch, the default isTrue。
reduction: Specify the dimensionality reduction method applied to the output, which can be 'none', 'mean', 'sum'.

6.

This is a tool in PyTorch for loading data.

Main parameters:

dataset: The dataset to be loaded.
batch_size: The size of each batch.
shuffle: Whether to disrupt data at the beginning of each epoch, the default isFalse。
sampler: Defines the policy for sampling from the dataset.
batch_sampler:andsamplerSimilar, but returns an index of one batch at a time.
num_workers: The number of child processes used when loading data, default is 0.
collate_fn: How to merge sample lists into a mini-batch.
pin_memory: Whether to load data into fixed memory, default isFalse。
drop_last: If the data size cannot be divided by batch size, whether to discard the last incomplete batch, the default isFalse。

Sample code

Here is a sample code for training simple classification tasks using LSTM:

import torch
import  as nn
import  as optim
from  import DataLoader, TensorDataset
# Define the modelclass LSTMModel():
    def __init__(self, input_size, hidden_size, num_layers, num_classes):
        super(LSTMModel, self).__init__()
         = (input_size, hidden_size, num_layers, batch_first=True)
         = (hidden_size, num_classes)
    def forward(self, x):
        h0 = (num_layers, (0), hidden_size).to(device)
        c0 = (num_layers, (0), hidden_size).to(device)
        out, _ = (x, (h0, c0))
        out = (out[:, -1, :])
        return out
# Parameter settingsinput_size = 28
hidden_size = 128
num_layers = 2
num_classes = 10
num_epochs = 2
batch_size = 100
learning_rate = 0.001
# Data preparationtrain_dataset = TensorDataset(train_x, train_y)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
# Model Initializationmodel = LSTMModel(input_size, hidden_size, num_layers, num_classes).to(device)
# Loss functions and optimizerscriterion = ()
optimizer = ((), lr=learning_rate)
# Train the modelfor epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        images = (-1, sequence_length, input_size).to(device)
        labels = (device)
        # Forward communication        outputs = model(images)
        loss = criterion(outputs, labels)
        # Backpropagation and optimization        optimizer.zero_grad()
        ()
        ()
        if (i+1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{total_step}], Loss: {():.4f}')

This sample code shows how to define and train an LSTM model using PyTorch, and explains the parameters and their effects of each class and method in detail.

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