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feature_columns = [
	tf.feature_column.numeric_column(
	"pixels", shape=784)
]

classifier = tf.estimator.LinearClassifier(
    feature_columns=feature_columns, 
    n_classes=10,
    model_dir=logdir)

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tf.estimator.inputs.numpy_input_fn(
    x={'pixels': X},
    y=Y,
    batch_size=batch_size,
    num_epochs=epochs,
    shuffle=shuffle)

DATA_SETS = input_data.read_data_sets(
    "/tmp/fashion-mnist")

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classifier.train(
  input_fn=train_input_fn,
  steps=num_steps)

accuracy_score = classifier.evaluate(
  input_fn=eval_input_fn)['accuracy']

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classifier =
  tf.estimator.DNNClassifier(
    feature_columns=feature_columns, 
    n_classes=10,
    hidden_units=[100, 75, 50],
    model_dir=logdir
  )

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X = DATA_SETS.test.images[5000:5005]

predict_input_fn = 
  tf.estimator.inputs.numpy_input_fn(        
    x={'pixels': X},
    batch_size=1,
    num_epochs=1,
    shuffle=False)

predictions = classifier.predict(

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// Corresponding spreadsheet: https://docs.google.com/spreadsheets/d/11pse2j9nLimlgyGsxVCtcHY7vBRYchMFslR_rIReNiQ/edit?usp=sharing

function myFunction(event) {
  Logger.log(SpreadsheetApp.getActive().getUrl());
  
  Logger.log(JSON.stringify(event));
  
  row_num = event.range.rowStart;
  col_num = event.range.columnEnd;

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