I am trying to build a CNN LSTM classifier for 1d sequential data.Input is of length 20 and contains 4 features.
I have trained the model and saved it. However I am unable to get good performance in both training as well as test data:-
Below is my code for the tensorflow model.
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv1D(filters=128, kernel_size=8, padding = 'same', activation='relu', input_shape = (20,4)))
model.add(tf.keras.layers.Conv1D(filters=128, kernel_size=5, padding = 'same', activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(tf.keras.layers.Conv1D(filters=128, kernel_size=3, padding = 'same', activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.01)))
model.add(tf.keras.layers.MaxPooling1D(pool_size=2))
model.add(tf.keras.layers.LSTM(units = 128))
model.add(tf.keras.layers.Dense(units = 1, activation = 'sigmoid'))
model.compile(optimizer='adam', loss='binary_crossentropy', metrics = 'accuracy')
model.build()
model.summary()
history = model.fit(X_tf, y_tf, epochs=60, batch_size=256, validation_data = (X_tf_,y_tf_))

Here are the logs that I am getting while training.

Epoch 5/60 19739/19739 [==============================] - 1212s 61ms/step - loss: 0.5858 - accuracy: 0.7055 - val_loss: 0.5854 - val_accuracy: 0.7062

I need help in how can I further improve the performance.What are the various techniques that I can apply to sequential data?
My training dataset has 4.8 million rows and test set has 1.2 million rows.