Training Pipeline - PyTorch Beginner 06

# 1) Design model (input, output, forward pass with different layers)
# 2) Construct loss and optimizer
# 3) Training loop
#       - Forward = compute prediction and loss
#       - Backward = compute gradients
#       - Update weights

import torch
import torch.nn as nn

# Linear regression
# f = w * x 

# here : f = 2 * x

# 0) Training samples
X = torch.tensor([1, 2, 3, 4], dtype=torch.float32)
Y = torch.tensor([2, 4, 6, 8], dtype=torch.float32)

# 1) Design Model: Weights to optimize and forward function
w = torch.tensor(0.0, dtype=torch.float32, requires_grad=True)

def forward(x):
    return w * x

print(f'Prediction before training: f(5) = {forward(5).item():.3f}')

# 2) Define loss and optimizer
learning_rate = 0.01
n_iters = 100

# callable function
loss = nn.MSELoss()

optimizer = torch.optim.SGD([w], lr=learning_rate)

# 3) Training loop
for epoch in range(n_iters):
    # predict = forward pass
    y_predicted = forward(X)

    # loss
    l = loss(Y, y_predicted)

    # calculate gradients = backward pass
    l.backward()

    # update weights
    optimizer.step()

    # zero the gradients after updating
    optimizer.zero_grad()

    if epoch % 10 == 0:
        print('epoch ', epoch+1, ': w = ', w, ' loss = ', l)

print(f'Prediction after training: f(5) = {forward(5).item():.3f}')

# 1) Design model (input, output, forward pass with different layers)
# 2) Construct loss and optimizer
# 3) Training loop
#       - Forward = compute prediction and loss
#       - Backward = compute gradients
#       - Update weights

import torch
import torch.nn as nn

# Linear regression
# f = w * x 

# here : f = 2 * x

# 0) Training samples, watch the shape!
X = torch.tensor([[1], [2], [3], [4]], dtype=torch.float32)
Y = torch.tensor([[2], [4], [6], [8]], dtype=torch.float32)

n_samples, n_features = X.shape
print(f'#samples: {n_samples}, #features: {n_features}')
# 0) create a test sample
X_test = torch.tensor([5], dtype=torch.float32)

# 1) Design Model, the model has to implement the forward pass!
# Here we can use a built-in model from PyTorch
input_size = n_features
output_size = n_features

# we can call this model with samples X
model = nn.Linear(input_size, output_size)

'''
class LinearRegression(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LinearRegression, self).__init__()
        # define diferent layers
        self.lin = nn.Linear(input_dim, output_dim)
    def forward(self, x):
        return self.lin(x)
model = LinearRegression(input_size, output_size)
'''

print(f'Prediction before training: f(5) = {model(X_test).item():.3f}')

# 2) Define loss and optimizer
learning_rate = 0.01
n_iters = 100

loss = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)

# 3) Training loop
for epoch in range(n_iters):
    # predict = forward pass with our model
    y_predicted = model(X)

    # loss
    l = loss(Y, y_predicted)

    # calculate gradients = backward pass
    l.backward()

    # update weights
    optimizer.step()

    # zero the gradients after updating
    optimizer.zero_grad()

    if epoch % 10 == 0:
        [w, b] = model.parameters() # unpack parameters
        print('epoch ', epoch+1, ': w = ', w[0][0].item(), ' loss = ', l)

print(f'Prediction after training: f(5) = {model(X_test).item():.3f}')