title: Overfitting after one epoch
body: <p>I am training a model using Keras.</p>
  
  <pre><code>model = Sequential()
  model.add(LSTM(units=300, input_shape=(timestep,103), use_bias=True, dropout=0.2, recurrent_dropout=0.2))
  model.add(Dense(units=536))
  model.add(Activation("sigmoid"))
  model.compile(loss="binary_crossentropy", optimizer="adam", metrics=["accuracy"])
  
  while True:
          history = model.fit_generator( 
              generator = data_generator(x_[train_indices],
                      y_[train_indices], batch = batch, timestep=timestep),
                  steps_per_epoch=(int)(train_indices.shape[0] / batch), 
                  epochs=1, 
                  verbose=1, 
                  validation_steps=(int)(validation_indices.shape[0] / batch), 
                  validation_data=data_generator(
                      x_[validation_indices],y_[validation_indices], batch=batch,timestep=timestep))
  </code></pre>
  
  <p>It is a multiouput classification accoriding to scikit-learn.org definition:
  <strong><em>Multioutput regression assigns each sample a set  of  target values.This can be thought of as predicting several properties for each data-point, such as wind direction and magnitude at a certain location</strong>.</em> </p>
  
  <p>Thus, it is a recurrent neural network I tried out different timestep sizes. But the result/problem is mostly the same.</p>
  
  <p>After <strong>one epoch</strong>, my train loss is around 0.0X and my validation loss is around 0.6X. And this values keep stable for the next 10 epochs.</p>
  
  <p>Dataset is around 680000 rows. Training data is 9/10 and validation data is 1/10.</p>
  
  <p>I ask for intuition behind that..</p>
  
  <ul>
  <li>Is my model already over fittet after just one epoch? </li>
  <li>Is 0.6xx even a good value for a validation loss?</li>
  </ul>
  
  <p>High level question:
  Therefore it is a multioutput classification task (not multi class), I see the only way by using sigmoid an binary_crossentropy. Do you suggest an other approach?</p>