Ram usage of model keep increasing when training to previously trained model Update
It keep increasing when i load the saved model like this.

return tf.keras.models.load_model("models/min__1576172002.model")


and i am saving model using 

   agent.model.save(f'models/min__{int(time.time())}.model')




I declare my model as 

model = Sequential()
model.add(Dense(6,activation='relu',input_shape=(4,)))
model.add(Dropout(0.2))
# model.add(Flatten())

model.add(Dense(5,activation='relu'))
model.add(Dropout(0.2))
# model.add(Flatten())

model.add(Dense(5,activation='relu'))
model.add(Dropout(0.2))


model.add(Dense(2,activation='softmax'))

model.compile(loss="mse", optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])


Then its working ok.
But after some time as it was not showing results, I use 

model = Sequential()
model.add(Dense(6,activation='relu',input_shape=(4,)))
model.add(Dropout(0.2))

# model.add(Flatten())

model.add(Dense(5,activation='relu'))
model.add(Dropout(0.2))
# model.add(Flatten())

model.add(Dense(5,activation='relu'))
model.add(Dropout(0.2))

model.add(Dense(5,activation='relu'))
model.add(Dropout(0.2))


model.add(Dense(2,activation='softmax'))

model.compile(loss="mse", optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])


I added only one more layer. But now. 
I see RAM usage of computer. It is keep increasing and not stopping. If i pause training then RAM will also pause at that point and when i continue training  then RAM will increase and it took 32GB of RAM (and still increasing )and previous model with one less layer took only 6GB of RAM
I found it and answering as it might help somebody.

I saw pattern in this problem. Every time I load the model from my saved ones and then start them training again it will start eating my ram. Slowly it will reach to limit of my laptop.
But same model if i train from scratch will not do same. 
I was loading model using

tf.keras.models.load_model("models/min__1576172002.model")


So Instead of training it again. I Build a new one and assign its weights = trained models weight. This way this problem is solved.

        prev_modal = tf.keras.models.load_model("models/min__1576224504.h5")
        model = Sequential()
        model.add(Dense(6,activation='relu',input_shape=(4,)))
        model.add(Dropout(0.2))
        # model.add(Flatten())

        model.add(Dense(5,activation='relu'))
        model.add(Dropout(0.2))
        # model.add(Flatten())

        model.add(Dense(5,activation='relu'))
        model.add(Dropout(0.2))


        model.add(Dense(2,activation='softmax'))

        model.compile(loss="mse", optimizer=Adam(learning_rate=0.001), metrics=['accuracy'])

        model.set_weights(prev_modal.get_weights())