Conormc98

import random

random.shuffle(training_data)

Conormc98

for epoch in range(100):
    for i, vals in enumerate(training_data):
        X, Y = iter(vals)
        X = Variable(torch.FloatTensor([X]), requires_grad=True)
        Y = Variable(torch.FloatTensor([Y]), requires_grad=False)
        optimizer.zero_grad()
        outputs = net(X)
        loss = criterion(outputs, Y)
        loss.backward()
        optimizer.step()

Conormc98

def criterion(out, label):
    return (label - out) ** 2

Conormc98

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(1, 1)
    def forward(self, x):
        x = F.relu(self.fc1(x))
        return x

Conormc98

training_data = []

for i, val in enumerate(x_vals):
    pairing = (val, y_vals[i])
    training_data.append(pairing)

Conormc98

y_vals = []

for val in x_vals:
  y = (3 * val) + 5
  y_vals.append(y)

Conormc98

x_vals = list(range(51))

Conormc98

from flask import Flask

app = Flask(__name__)

@app.route('/')
def index():
    return "Hello World!"

Conormc98

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