title: Keras - Stationary result using model.fit()
body: <p>I'm implementing this simple neural network, with these inputs data:</p>
  
  <pre><code>x_train = np.asarray(x_train)
  y_train = np.asarray(y_train)
  x_test = np.asarray(x_test)
  y_test = np.asarray(y_test)
  </code></pre>
  
  <p>After have defined the network's structure:</p>
  
  <pre><code>model = Sequential()
  model.add(Dense(20, input_dim=5, init='normal', activation='sigmoid'))
  model.add(Dense(1, init='normal', activation='sigmoid'))
  </code></pre>
  
  <p>I run this to train and evaluate the NN:</p>
  
  <pre><code>model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
  history = model.fit(x_train, y_train, nb_epoch=10, validation_split=0.2)
  </code></pre>
  
  <p>and I always get the same result from model.fit( ... ) :
   <em>32/143 [=====>........................] - ETA: 0s</em>.
   It seems that doesn't work at all, despite I obtain consistent results on training and validation. How does I have to interpreter this stationary result about <em>model. fit</em> output?</p>