title: Constant Accuracy/Loss in Keras NN
body: <p>I am learning deep learning and was implementing the titanic challenge on kaggle.</p>
  
  <p>In Preprocessing,I have dropped the Pid,name,ticket and cabin columns.I have replaced the empty values in Fare and Age columns by adding the respective means. I have one hot encoded sex, Pclass(passenger class) and Embarked Port. Added an alone column to determine if the passenger was traveling alone or not(From sibling and parents count). Finally I used scikit's MinMaxScalar.</p>
  
  <p>Here is the keras model used.</p>
  
  <pre><code>model = Sequential()
  model.add(Dense(32, input_dim=13, activation="relu"))
  model.add(Dense(64, activation="relu"))
  model.add(Dense(128, activation="relu"))
  model.add(Dense(256, activation="relu"))
  model.add(Dropout(0.3))
  model.add(Dense(512, activation="relu"))
  model.add(Dense(1024, activation="relu"))
  model.add(Dense(512, activation="relu"))
  model.add(Dropout(0.3))
  model.add(Dense(1024, activation="relu"))
  model.add(Dense(2048, activation="relu"))
  model.add(Dense(1024, activation="relu"))
  model.add(Dropout(0.3))
  model.add(Dense(512, activation="relu"))
  model.add(Dense(1024, activation="relu"))
  model.add(Dense(512, activation="relu"))
  model.add(Dropout(0.3))
  model.add(Dense(256, activation="relu"))
  model.add(Dense(128, activation="relu"))
  model.add(Dense(64, activation="relu"))
  model.add(Dense(32, activation="relu"))
  model.add(Dropout(0.3))
  model.add(Dense(1, activation="sigmoid"))
  model.compile(optimizer="Adam", loss='binary_crossentropy', metrics=["binary_accuracy"])
  </code></pre>
  
  <p>The val_binary_accuracy is a constant 0.6425 throughout the process</p>