title: keras resume training with different learning rate
body: <p>I built a simple LSTM model using keras and trained as follows:</p>
  
  <pre><code>model = Sequential()
  model.add(LSTM(activation='tanh',input_dim=6,output_dim=50,return_sequences=False))
  model.add(Dense(output_dim=1,activation = 'sigmoid'))
  model.compile(loss='binary_crossentropy', optimizer =optimizers.Adam(lr = 0.01),metrics=['accuracy'])
  model.fit(X_train,y_train,batch_size=256,nb_epoch=1000,validation_data = (X_test,y_test))
  model.save('model_params.h5')
  </code></pre>
  
  <p>The model almost converged. Therefore I want to fine tune the model by resuming training using smaller learning rate (i.e., 0.001). How could I achieve this?</p>