CODEWITHSUNDEEP
pythontensorflowmachine-learningkerasdata-science
Lewiky
Lewiky

Reputation: 325

Tensorflow neural network has very high error with simple regression

I'm trying to build a NN to do regression with Keras in Tensorflow.

I've trying to predict the chart ranking of a song based on a set of features, I've identified a strong correlation of having a low feature 1, a high feature 2 and a high feature 3, with having a high position on the chart (a low output ranking, eg position 1).

However after training my model, the MAE is coming out at about 3500 (very very high) on both the training and testing set. Throwing some values in, it seems to give the lowest output rankings for observations with low values in all 3 features.

I think this could be something to do with the way I'm normalising my data. After brining it into a pandas dataframe with a column for each feature, I use the following code to normalise: 

def normalise_dataset(df):
    return df-(df.mean(axis=0))/df.std()

I'm using a sequential model with one Dense input layer with 64 neurons and one dense output layer with one neuron. Here is the definition code for that:

model = keras.Sequential([
    keras.layers.Dense(64, activation=tf.nn.relu, input_dim=3),
    keras.layers.Dense(1)
])
optimizer = tf.train.RMSPropOptimizer(0.001)
model.compile(loss='mse', optimizer=optimizer, metrics=['mae'])

I'm a software engineer, not a data scientist so I don't know if this model set-up is the correct configuration for my problem, I'm very open to advice on how to make it better fit my use case.

Thanks

EDIT: Here's the first few entires of my training data, there are ~100,000 entires. The final col (finalPos) contains the labels, the field I'm trying to predict.

chartposition,tagcount,artistScore,finalPos
256,191,119179,4625
256,191,5902650,292
256,191,212156,606
205,1480523,5442
256,195,5675757,179
256,195,933171,7745

Upvotes: 0

Views: 316

Answers (1)

Zoe
Zoe

Reputation: 1410

The first obvious thing is that you are normalizing your data in the wrong way. The correct way is

return (df - df.mean(axis=0))/df.std()

I just changed the bracket, but basically it is (data - mean) divided by standard deviation, whereas you are dividing the mean by the standard deviation.

Upvotes: 3

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