Keras model LSTM predict 2 features

Keras model LSTM predict 2 features

I am trying to predict 2 features. This is how my model looks like:
Defining the model
def my_model():
    input_x = Input(batch_shape=(batch_size, look_back, x_train.shape[2]), name='input')
    drop = Dropout(0.5)

    lstm_1 
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Gensim doc2vec 300 dimensional vector fed into keras,lstm model not working.Loss is not diminishing

Gensim doc2vec 300 dimensional vector fed into keras,lstm model not working.Loss is not diminishing

This is my code snippet:
model=keras.Sequential()
model.add(keras.layers.LSTM(28,input_shape=(300,1),return_sequences=True))
model.add(keras.layers.Dropout(0.4))
model.add(keras.layers.LSTM(14))
model.add(keras.layers.Dropout(0.5))
model.add(keras.layers.Dense(2,activation="softmax"))
sgd=keras.optimizers.SGD(lr=0.001)
model.compile(optimizer=sgd,loss=keras.losses.sparse_categorical_crossentropy)
model.fit(trainData,labeledData.sentiment,epochs=20,batch_size=3000)

trainData shape is [batch_size,300,1],when i begin training 
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Keras, get output of a layer at each epochs

Keras, get output of a layer at each epochs

What I have done?
I implemented a keras model as follow:
train_X, test_X, train_Y, test_Y = train_test_split(X, Y, test_size=0.2, random_state=np.random.seed(7), shuffle=True)

train_X = np.reshape(train_X, (train_X.shape[0], 1, 
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