Machine Learning: ADAM in 100 lines of PyTorch code

Machine learning is a powerful tool for creating intelligent systems that can learn and make decisions on their own. One popular method for training machine learning models is the ADAM optimization algorithm, which is widely used in the field.

With PyTorch, a popular machine learning library, implementing the ADAM algorithm is straightforward. In just 100 lines of code, you can create a simple implementation of ADAM for training your machine learning models.

# Importing the necessary libraries
import torch
from torch.autograd import Variable

# Defining the model
class Model(torch.nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.linear = torch.nn.Linear(1, 1)  # One input feature and one output feature

    def forward(self, x):
        y_pred = self.linear(x)
        return y_pred

# Initializing the model and the ADAM optimizer
model = Model()
criterion = torch.nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

# Generating some toy data for training
x_data = torch.Tensor([[1.0], [2.0], [3.0], [4.0]])
y_data = torch.Tensor([[2.0], [4.0], [6.0], [8.0]])

# Training the model
for epoch in range(100):
    # Forward pass
    y_pred = model(x_data)

    # Calculating loss
    loss = criterion(y_pred, y_data)

    # Zeroing the gradients
    optimizer.zero_grad()

    # Backward pass
    loss.backward()

    # Updating the weights
    optimizer.step()

    # Printing the progress
    if epoch % 10 == 0:
        print('Epoch:', epoch, 'Loss:', loss.item())

# Making a prediction
x_test = torch.Tensor([[5.0]])
y_test = model(x_test)
print('Prediction:', y_test.data[0][0])

This simple code sets up a linear regression model and uses the ADAM optimizer to train it on some toy data. After 100 epochs of training, the model can make a prediction on new data.

Machine learning and PyTorch make it easy to experiment with different optimization algorithms like ADAM. By understanding and implementing these algorithms, you can improve the performance of your machine learning models.