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dataset = TensorDataset(inputs, targets)
dataset

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class FishModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(input_size, output_size)                # fill this (hint: use input_size & output_size defined above)
        
    def forward(self, xb):
        out = self.linear(xb)                          # fill this
        return out
    
    def training_step(self, batch):

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model = FishModel()

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def evaluate(model, val_loader):
    outputs = [model.validation_step(batch) for batch in val_loader]
    return model.validation_epoch_end(outputs)

def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
    history = []
    optimizer = opt_func(model.parameters(), lr)
    for epoch in range(epochs):
        # Training Phase 
        for batch in train_loader:

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model = FishModel()
epochs = 1000
lr = 1e-2
history1 = fit(epochs, lr, model, train_loader, val_loader)

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def predict_single(input, target, model):
    inputs = input.unsqueeze(0)
    predictions = model(inputs)               # fill this #model(inputs)
    prediction = predictions[0].detach()
    print("Input:", input)
    print("Target:", target)
    print("Prediction:", prediction)

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input, target = val_ds[10]

predict_single(input, target, model)

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%-------------------------
% Resume in Latex
% Author : Sourabh Bajaj
% License : MIT
%------------------------

\documentclass[letterpaper,11pt]{article}

\usepackage{latexsym}
\usepackage[empty]{fullpage}

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stages:
  - test

publish:
  image: docker:20.10.12-git
  stage: test
  services:
    - docker:20.10.12-dind
  tags: 
    - docker

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gitlabUrl: <your gitlab URL>
runnerRegistrationToken: <your runner secret>
rbac:
  create: True
  clusterWideAccess: true
# runners:
#   image: ubuntu:18.04
#   privileged: true
runners:
  privileged: true