title: softmax and sigmoid are giving same results in multiclass classification
body: <p>I am building an lstm model. I tested my model using softmax and sigmoid activation function. In the documentation sigmoid is used for binary classification and softmax is used for multiclass classification. But in my case, both are giving the same results. Why is it so?<br />
  Here is my code:</p>
  <pre><code>embedding_vecor_length = 128
  max_length = 700
  model = Sequential()
  model.add(Embedding(len(tokenizer.word_index)+1, embedding_vecor_length, input_length=max_length))
  model.add(Conv1D(filters=32, kernel_size=5, padding='same', activation='relu'))
  model.add(MaxPooling1D(pool_size=2))
  model.add(Conv1D(filters=16, kernel_size=3, padding='same', activation='relu'))
  model.add(MaxPooling1D(pool_size=2))
  model.add(Bidirectional(LSTM(64)))
  model.add(Dense(10, activation='softmax'))
  model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
  print(model.summary())
  </code></pre>
  <p>Here are the predicted results:</p>
  <pre><code>   [[2.72062905e-02 1.47979835e-03 4.44446778e-04 1.60833297e-05
    4.15672457e-06 3.20438482e-02 9.38653767e-01 1.41544719e-04
    5.55426550e-06 4.47654566e-06]
   [2.31099591e-01 1.71699154e-03 1.32052042e-02 4.70457249e-04
    8.86382014e-02 2.65704724e-03 6.54215395e-01 7.50611164e-03
    4.89178114e-04 1.89376965e-06]
   [1.24909900e-01 8.73659015e-01 9.71468398e-06 1.66079029e-04
    1.05203628e-06 4.14116839e-05 3.97000113e-05 6.98190925e-05
    1.10231712e-03 9.84829512e-07]
  </code></pre>