util.py

import torch
from torch import nn

import ray.train

# Model Definition
class NeuralNetwork(nn.Module):
    def __init__(self):
        super(NeuralNetwork, self).__init__()
        self.flatten = nn.Flatten()
        self.linear_relu_stack = nn.Sequential(
            nn.Linear(28 * 28, 512),
            nn.ReLU(),
            nn.Dropout(0.25),
            nn.Linear(512, 512),
            nn.ReLU(),
            nn.Dropout(0.25),
            nn.Linear(512, 10),
            nn.ReLU(),
        )

    def forward(self, x):
        x = self.flatten(x)
        logits = self.linear_relu_stack(x)
        return logits

# Create model, loss funtion and optimizer.
def prepare_model():
    lr = 1e-3
    model = NeuralNetwork()
    model = ray.train.torch.prepare_model(model)
    loss_fn = nn.CrossEntropyLoss()
    optimizer = torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9)
    return (model, loss_fn, optimizer)