title: LSTM is Showing very low accuracy and large loss
body: <p>I am applying LSTM on a dataset that has 53699 entries for the training set and 23014 entries for the test set. The shape of the input training set is (53699,4). I've tried with different activation (tanh, relu) and with different units size with four LSTM layers and a dense layer but the accuracy is very low. How can I improve the accuracy of this problem?</p>
  <pre><code>regressor=Sequential()
  
  from keras.layers.normalization.batch_normalization import BatchNormalization
  regressor.add(LSTM(units=256, activation='tanh',return_sequences=True,input_shape=(4,1)))
  regressor.add(Dropout(0.2))
  regressor.add(BatchNormalization())
  regressor.add(LSTM(units=256, activation='tanh',return_sequences=True))
  regressor.add(Dropout(0.2))
  regressor.add(BatchNormalization())
  regressor.add(LSTM(units=256, activation='tanh', return_sequences=True))
  regressor.add(Dropout(0.2))
  regressor.add(BatchNormalization())
  regressor.add(LSTM(units=32,activation='tanh'))
  regressor.add(Dropout(0.2))
  regressor.add(BatchNormalization())
  regressor.add(Dense(units=16,activation='tanh'))
  
  regressor.compile(optimizer=&quot;adam&quot;, loss=&quot;mean_squared_error&quot;,metrics=['accuracy'])
  history=regressor.fit(X_train,y_train,epochs=20,batch_size=32,validation_data=(X_test,y_test))```
  
  Epoch 1/20
  1679/1679 [==============================] - 151s 85ms/step - loss: 189050.0781 - accuracy: 0.0016 - val_loss: 195193.2188 - val_accuracy: 0.0000e+00
  </code></pre>