title: keras sequential().predict(x_test) only returns 1 column for two classes
body: <p>I'm having problem with keras <code>sequential().predict(x_test)</code>.</p>
  
  <p>Btw getting the same output using <code>sequential().predict_proba(x_test)</code> as I found that these two are indifferent in sequential now.</p>
  
  <p>My data has two classes: 0 or 1, I believe <code>predict(x_test)</code> should give two columns, where the first column is the prob for getting 0 and the second is prob of getting 1. However I only have one column with this.</p>
  
  <pre><code>    In [85]:y_train.value_counts()
      Out[85]: 
      0    616751
      1     11140
      Name: _merge, dtype: int64
  </code></pre>
  
  <p>There should be no problem with my data as I used the same x_train, y_train, x_test, y_test for both LogisticRegression model and neural network model, it works perfect in LogisticRegression.</p>
  
  <pre><code>In [87]:y_pred_LR
  Out[87]: 
  array([[  9.96117151e-01,   3.88284921e-03],
         [  9.99767583e-01,   2.32417329e-04],
         [  9.87375774e-01,   1.26242258e-02],
         ..., 
         [  9.72159138e-01,   2.78408623e-02],
         [  9.97232916e-01,   2.76708432e-03],
         [  9.98146985e-01,   1.85301489e-03]])
  </code></pre>
  
  <p>but I only get 1 column in neural network model.</p>
  
  <p>So I guess there is some problem with the NN model setting up? Here is my codes</p>
  
  <pre><code>NN = Sequential()
  NN.add(Dense(40, input_dim = 65, kernel_initializer = 'uniform', activation = 'relu'))
  NN.add(Dense(20, kernel_initializer = 'uniform', activation = 'relu'))
  NN.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))
  NN.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
  
  NN.fit(x_train, y_train, batch_size = 50, epochs=5)
  y_pred_NN = NN.predict(x_test)
  print(y_pred_NN)
  
      In [86]: print(y_pred_NN)
      [[ 0.00157279]
       [ 0.0010451 ]
       [ 0.03178826]
       ..., 
       [ 0.01030775]
       [ 0.00584918]
       [ 0.00186538]]
  </code></pre>
  
  <p>Actually it looks like it's the prob of getting 1? 
  Any help is appreciated!</p>
  
  <p>Btw the shapes of my predictions in both models are as follows</p>
  
  <pre><code>In [91]:y_pred_LR.shape
  Out[91]: (300000, 2)
  
  In [90]:y_pred_NN.shape
  Out[90]: (300000, 1)
  </code></pre>