title: Keras loss is not decreasing
body: <p>I tried to learn my NN with <a href="https://www.kaggle.com/uciml/breast-cancer-wisconsin-data" rel="nofollow noreferrer">breast Cancer Wisconsin</a>
   (I add <code>"id"</code> column as an index and changed <code>"diagnosis"</code> column to 0 and 1 with <code>sklearn.preprocessing.LabelEncoder</code>), but my NN is not reducing Loss.</p>
  
  <p>I tried other optimizers and losses but this isn't working.</p>
  
  <p>That's my NN:</p>
  
  <pre class="lang-py prettyprint-override"><code>from tensorflow.keras.models import Sequential
  from tensorflow.keras.layers import Dense, BatchNormalization, InputLayer
  import tensorflow.nn as tfnn
  
  model = Sequential()
  
  model.add(Dense(30, activation = tfnn.relu, input_dim = 30))
  model.add(BatchNormalization(axis = 1))
  
  model.add(Dense(60, activation = tfnn.relu))
  model.add(BatchNormalization(axis = 1))
  
  model.add(Dense(1, activation = tfnn.softmax))
  
  model.compile(loss = 'binary_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
  model.fit(data, target, epochs = 6)
  </code></pre>
  
  <p>And my output:</p>
  
  <pre class="lang-py prettyprint-override"><code>Epoch 1/6
  569/569 [==============================] - 2s 3ms/sample - loss: 10.0025 - acc: 0.3726
  Epoch 2/6
  569/569 [==============================] - 0s 172us/sample - loss: 10.0025 - acc: 0.3726
  Epoch 3/6
  569/569 [==============================] - 0s 176us/sample - loss: 10.0025 - acc: 0.3726
  Epoch 4/6
  569/569 [==============================] - 0s 167us/sample - loss: 10.0025 - acc: 0.3726
  Epoch 5/6
  569/569 [==============================] - 0s 163us/sample - loss: 10.0025 - acc: 0.3726
  Epoch 6/6
  569/569 [==============================] - 0s 169us/sample - loss: 10.0025 - acc: 0.3726
  </code></pre>
  
  <p>I seems that NN after a few iterations stops learning (look at the time of epochs learning, in the first epoch it's 2s and in others it's 0s and in first epoch speed of processing the data is ms/sample, but in other epochs iits us/sample)
  Thank you for your time!</p>