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CNN = SimpleCNN()
trainNet(CNN, batch_size=32, n_epochs=5, learning_rate=0.001)

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#Define your API Key from Algorithmia
apikey = 'YOUR_API_KEY'

#Initialize the Algorithmia client
client = Algorithmia.client(apikey)

#Create an instance of the RetrieveTweetsWithKeyword algorithm
algo = client.algo('diego/RetrieveTweetsWithKeyword/0.1.2')

#Call the algorithm for both of our keywords and store the results

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import Algorithmia

input = {
  "username": "elonmusk",
  "auth": {
      "app_key": "xxxxxxxxxx",
      "app_secret": "xxxxxxxxxx",
      "oauth_token": "xxxxxxxxxx",
      "oauth_token_secret": "xxxxxxxxxx"
  }

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for epoch in range(numEpochs):
  
  train(...) #Training code
  accuracy = eval(...) #Evaluate accuracy
  
  #If we've reached a new best accuracy
  if accuracy > best_accuracy:
    #Save model checkpoint
    torch.save({'epoch': epoch,
                'state_dict': model.state_dict(),

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#Import the optim module from the pytorch package
import torch.optim as optim

#Initialize an optimizer object
learning_rate = 0.001
optimizer = optim.Adam(net.parameters(), lr=learning_rate)

#Set the parameter gradients to 0 and take a step (as part of a training loop)
for epoch in num_epochs:
train(...)

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const request = require('request');

/**
 * Sends input from a Slack SlashCommand to Algorithmia's nlp/SummarizeURL, and returns result
 *
 * @param {!Object} req Cloud Function request context.
 * @param {!Object} res Cloud Function response context.
 */
exports.summarizeURL = function summarizeURL(req, res) {
  // verify that this request came from a valid Slack App

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