My colleagues and this question on Cross Validated say you should transform data to zero mean and unit variance for neural networks. However, my performance was slightly worse with scaling than without.
I tried using:
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
steps = 5000
def exp_decay(global_step):
return tf.train.exponential_decay(
learning_rate=0.1, global_step=global_step,
decay_steps=steps, decay_rate=0.01)
random.seed(42) # to sample data the same way
classifier = skflow.TensorFlowDNNClassifier(
hidden_units=[150, 150, 150],
n_classes=2,
batch_size=128,
steps=steps,
learning_rate=exp_decay)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
Did I do something wrong or is scaling not necessary?
Usually scaling benefits most for models that don't have regularization and linear models. For example simple mean squared error loss (like in TensorFlowLinearRegressor) without regularization won't work very well on not scaled data.
In your case you are using classifier that runs softmax regularization and you are using DNN, so scaling is not needed. DNNs themselve can model rescaling (via bias and weight on the feature in the first layer) if that's a useful thing to do.