title: My model's loss value decreases slowly .how to reduce my loss faster while training?
body: <p>when I train the model the loss decrease from 0.9 to 0.5 in 2500 epochs. Is it normal? </p>
  
  <p>my model:</p>
  
  <pre><code>    model = Sequential()
  
      model.add(Embedding(vocab_size , emd_dim, weights=[emd_matrix], input_length=maxLen,trainable=False))
  
      model.add(LSTM(256,return_sequences=True,activation="relu",kernel_regularizer=regularizers.l2(0.01),kernel_initializer=keras.initializers.glorot_normal(seed=None)))
      model.add(LSTM(256,return_sequences=True,activation="relu",kernel_regularizer=regularizers.l2(0.01),kernel_initializer=keras.initializers.glorot_normal(seed=None)))
  
      model.add(LSTM(256,return_sequences=False,activation="relu",kernel_regularizer=regularizers.l2(0.01),kernel_initializer=keras.initializers.glorot_normal(seed=None)))
      model.add(Dense(l_h2i,activation='softmax'))
      model.compile(loss='categorical_crossentropy', optimizer="adam", metrics=['accuracy'])
      filepath = "F:/checkpoints/"+modelname+"/lstm-{epoch:02d}-{loss:0.3f}-{acc:0.3f}-{val_loss:0.3f}-{val_acc:0.3f}.hdf5"
      checkpoint = ModelCheckpoint(filepath, monitor="loss", verbose=1, save_best_only=True, mode='min')
      reduce_lr = ReduceLROnPlateau(monitor='loss', factor=0.5, patience=2, min_lr=0.000001)
      print(model.summary())
      history=model.fit(X_train_indices, Y_train_oh, batch_size=batch_size ,
                        epochs=epochs , validation_split=0.1, shuffle=True,
                        callbacks=[checkpoint, reduce_lr])
  </code></pre>
  
  <p>some part of the results are as shown here : </p>
  
  <pre><code>loss improved from 0.54275 to 0.54272
  loss: 0.5427 - acc: 0.8524 - val_loss: 1.1198 - val_acc: 0.7610
  
  loss improved from 0.54272 to 0.54268
  loss: 0.5427 - acc: 0.8525 - val_loss: 1.1195 - val_acc: 0.7311
  
  loss improved from 0.54268 to 0.54251
  loss: 0.5425 - acc: 0.8519 - val_loss: 1.1218 - val_acc: 0.7420
  
  loss improved from 0.54251 to 0.54249
  loss: 0.5425 - acc: 0.8517 - val_loss: 1.1210 - val_acc: 0.7518
  </code></pre>