title: Keras unrealistic results
body: <p>I try to do a creditcard-fraud prediction with keras.
  For that, I have a creditcard.csv file, with over 280 000 different cases which are all labeled as fraud or valid. </p>
  
  <p>My problem is, that my code actually does compile, but in the first epoche, my accuracy is already 0.9979 and from the second epoche on acc: 0.9982.</p>
  
  <p>That doesn't seem to be very realistic to me, but I don't know my mistake.</p>
  
  <p>Here is the shortened version of my code: </p>
  
  
  
  <pre class="lang-python prettyprint-override"><code>import pandas as pd
  import numpy as np
  from keras import models
  from keras import layers
  
  combinedData = pd.read_csv('creditcard.csv')
  trainData = combinedData[:227845]
  testData = combinedData[227845:]
  
  trainDataFactors = trainData.copy()
  del trainDataFactors['Class']
  trainDataLabels = pd.DataFrame(trainData, columns=['Class'])
  
  testDataFactors = testData.copy()
  del testDataFactors['Class']
  testDataLabels = pd.DataFrame(testData, columns=['Class'])
  
  model = models.Sequential()
  model.add(layers.Dense(30, activation="relu", input_shape = (30, )))
  model.add(layers.Dense(60, activation ="relu"))
  model.add(layers.Dense(30, activation="sigmoid"))
  
  model.compile(
                optimizer = "rmsprop",
                loss = "sparse_categorical_crossentropy",
                metrics = ["accuracy"]
               )
  
  history = model.fit(
                      trainDataFactors, trainDataLabels,
                      epochs = 20,
                      batch_size = 512,
                      validation_data=(testDataFactors, testDataLabels)
                      )
  </code></pre>
  
  <p>I appreciate any help!</p>