title: Keras multiple binary outputs
body: <p>Can someone help me understand a bit better this problem? I must train a neural network which should output 200 mutually independent categories, each of these categories is a percentage ranging from 0 to 1. This seems to me like a <code>binary_crossentropy</code> problem, but every example I see on the internet uses <code>binary_crossentropy</code> with a single output. Since my output should be 200, if I apply <code>binary_crossentropy</code>, would that be correct?</p>
  <p>This is what I have in mind, is that a correct approach or should I change it?</p>
  <pre><code>inputs = Input(shape=(input_shape,))
  hidden = Dense(2048, activation='relu')(inputs)
  hidden = Dense(2048, activation='relu')(hidden)
  output = Dense(200, name='output_cat', activation='sigmoid')(hidden)
  model = Model(inputs=inputs, outputs=[output])
  loss_map = {'output_cat': 'binary_crossentropy'}
  model.compile(loss=loss_map, optimizer=&quot;sgd&quot;, metrics=['mae', 'accuracy'])
  </code></pre>