LSTM Model - Validation Accuracy is not changing I am working on classification problem, My input data is labels and output expected data is labels
Labels  Count
1      94481
0      65181
2      60448

I have made X, Y pairs by shifting the X and Y is changed to the categorical value
create X/y pairs
df1 = df['Data_Used']
df1 = concat([df1, df1.shift(1)], axis=1)
df1.dropna(inplace=True)

    X   Y
    2   1.0
    1   2.0
    1   1.0
    2   1.0
    2   2.0

values = df1.values

encoder = LabelEncoder()
test_labels = to_categorical(encoder.fit_transform(values[:,1]),num_classes=3)
train_X,test_X,train_y,test_y= train_test_split(values[:,0], test_labels,test_size = 0.30,random_state = 42)

print(train_X.shape)
print(train_y.shape)
print(test_X.shape)
print(test_y.shape)

(154076,)
(154076, 3)
(66033,)
(66033, 3)
Converting this to LSTM format
train_X = train_X.reshape(train_X.shape[0],1,1)
test_X = test_X.reshape(test_X.shape[0],1,1)

# configure network
n_batch = 1
n_epoch = 10
n_neurons = 100

ModelArchitecture
tf.keras.backend.clear_session()

model = tf.keras.models.Sequential([
    tf.keras.layers.LSTM(n_neurons, batch_input_shape=(n_batch, train_X.shape[1],train_X.shape[2]), stateful=True),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(100, activation = 'relu',kernel_regularizer=regularizers.l2(0.0001)),
    tf.keras.layers.Dense(3, activation='softmax')
])

model.summary()
model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['acc'])
history = model.fit(train_X,train_y,validation_data=(test_X, test_y),epochs=n_epoch, batch_size=n_batch, verbose=1,shuffle= False)

Validation Accuracy is not Changing
Epoch 1/5
154076/154076 [==============================] - 356s 2ms/step - loss: 1.0844 - acc: 0.4269 - val_loss: 1.0814 - val_acc: 0.4310
Epoch 2/5
154076/154076 [==============================] - 354s 2ms/step - loss: 1.0853 - acc: 0.4256 - val_loss: 1.0813 - val_acc: 0.4310
Epoch 3/5
154076/154076 [==============================] - 355s 2ms/step - loss: 1.0861 - acc: 0.4246 - val_loss: 1.0814 - val_acc: 0.4310
Epoch 4/5
154076/154076 [==============================] - 356s 2ms/step - loss: 1.0874 - acc: 0.4228 - val_loss: 1.0825 - val_acc: 0.4310
Epoch 5/5
154076/154076 [==============================] - 353s 2ms/step - loss: 1.0887 - acc: 0.4208 - val_loss: 1.0828 - val_acc: 0.4310

What can be the changes to improve the model.
One possible reason of this could be unbalanced data. You should have same amount of examples per label. And if you don't have that data, you can use Loss Weights. It is a parameter in model.compile(). You can learn more about Loss weights on google.
Also, I noticed you were using rmsprop as the optimizer. Try using Adam optimizer, as it is one of the best optimizer.
If, doing all of these I mentioned above, doesn't changes anything and the results are the same, remove the Dense() Layers and just keep 1 dense() layer, that is, just keep the last Dense Layer, and remove all the other Dense() Layers.
This will surely improve the model. But, if still it doesn't changes anything, then have a look here