PyTorch is a Python library used to build and train deep learning models. It's commonly used for tasks like image recognition and natural language processing.
# Import PyTorch library import torch
Tensors in PyTorch are similar to arrays and are used to store data. You can create tensors from NumPy arrays using `torch.tensor()`. All elements in a tensor must be of the same type, which you can specify using the `dtype` parameter.
# Convert a NumPy array to a PyTorch tensor import numpy as np np_array = np.array([100, 75.5]) # Create a tensor with float data type torch_tensor = torch.tensor(np_array, dtype=torch.float)
Linear regression is a basic model that predicts an outcome based on one or more input features. The model is often represented with the equation y = mx + b, where m is the slope and b is the intercept.
y = mx + b # Where y is the output, m is the slope, x is the input, and b is the intercept
Linear models can be implemented in PyTorch using basic input values. For example, predicting rent based on size and age of a house might be represented as rent = 2.5 * size_sqft - 1.5 * age + 1000.
rent = 2.5 * size_sqft - 1.5 * age + 1000
Activation functions add non-linearity to models, allowing them to learn more complex patterns. A common activation function is ReLU, which transforms input values into non-negative outputs.
# Import PyTorch's neural network module from torch import nn
The ReLU (Rectified Linear Unit) activation function outputs the input value if it's positive and zero otherwise. It's widely used in neural networks.
# Define ReLU function def ReLU(x): return max(0, x) # Use ReLU in PyTorch from torch import nn ReLU = nn.ReLU()
Multi-layer neural networks consist of multiple layers of nodes, or 'neurons', which can learn complex patterns in data. These layers include an input layer, hidden layers, and an output layer.
# Create a simple neural network in PyTorch import torch.nn as nn model = nn.Sequential( nn.Linear(8, 16), nn.ReLU(), nn.Linear(16, 10), nn.Sigmoid(), nn.Linear(10, 1) )
Sequential models in PyTorch allow you to define a neural network by stacking layers in sequence. Each layer passes its output to the next layer.
# Define Mean Squared Error loss function import torch.nn as nn loss = nn.MSELoss() # Compute the loss between predictions and actual values MSE = loss(predictions, y)
Loss functions measure how well a model's predictions match the actual values. They help adjust the model's parameters to improve its performance.
# Import the optimizer module from torch import optim
Mean Squared Error (MSE) is a common loss function used to measure the difference between predicted and actual values. It helps the model to minimize errors during training.
# Use the Adam optimizer for updating model parameters import torch.optim as optim optimizer = optim.Adam(model.parameters(), lr=0.01)
An optimizer adjusts the model's weights and biases based on the computed loss to improve performance. It is used after calculating the gradients during backpropagation.
# Compute loss and update model parameters MSE = loss(predictions, y) MSE.backward() # Compute gradients optimizer.step() # Update weights and biases
Gradient Descent is an optimization technique used to minimize the loss function by iteratively adjusting model parameters. It is implemented in PyTorch using built-in functions.
# Training loop with 400 iterations num_epochs = 400 for epoch in range(num_epochs): predictions = model(X) # Forward pass MSE = loss(predictions, y) # Compute loss MSE.backward() # Compute gradients optimizer.step() # Update weights optimizer.zero_grad() # Reset gradients
The learning rate determines how much the model's weights are adjusted during each training step. A well-chosen learning rate can significantly affect the training performance.
# Save and load PyTorch models # Save model torch.save(model, 'model.pth') # Load model loaded_model = torch.load('model.pth')
Adam is an advanced optimizer that adjusts learning rates dynamically based on the training progress. It is widely used in training neural networks.
# Evaluate model and make predictions without updating gradients model.eval() with torch.no_grad(): predictions = model(X_test) test_MSE = loss(predictions, y_test)
The backward pass involves calculating gradients of the loss function with respect to model parameters. These gradients are used to update the model during training.
# Define a custom neural network class class NN_Regression(nn.Module): def __init__(self): super(NN_Regression, self).__init__() # Initialize layers .. def forward(self, x): # Define forward pass .. return x # Create an instance of the neural network model = NN_Regression()
Training neural networks involves feeding data into the model, calculating errors, and adjusting weights based on the gradients to improve accuracy.
Saving a trained model allows you to reuse it later without retraining. Loading a saved model enables you to make predictions or continue training.
After training, you evaluate the model's performance on new data and make predictions. This helps you assess how well the model generalizes to unseen data.
You can define your own neural network class in PyTorch by subclassing `nn.Module` and implementing the `forward` method to specify how data flows through the network.
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