## Issue

This reproducible example creates a basic regression model predicting MPG given Horsepower (hope I am OK to just provide link). As far as I understand, this bakes the transformation of the feature Horsepower into the model’s training – also refered to as "inside the model". This is appealing as the model does the necessary transformation of raw data during scoring/inference e.g. after deployment (please correct me if I misunderstood). I am wondering, how this could be implemented when one has more than on independent variable. This is taken from the reproducible code quoted above:

```
horsepower_normalizer = tf.keras.layers.Normalization(input_shape=[1, ], axis=None)
horsepower_normalizer.adapt(horsepower)
horsepower_normalizer = tf.keras.layers.Normalization(input_shape=[1, ], axis=None)
horsepower_normalizer.adapt(horsepower)
horsepower_model = Sequential([
horsepower_normalizer,
layers.Dense(units=1)
])
```

So let us say we have a list of numeric features `X, Y, Z`

could the model definition code be produced based on this (e.g. via the functional API)? Any pointers would be very much welcome. Thanks!

PS:

I am currently trying to learn Keras + TF and ideally I want the normalisation make part of the mode/training. I use very rudemnetary code (to be improved!) along those lines:

```
train_data = pd.read_csv('train.csv')
val_data = pd.read_csv('val.csv')
target_name = 'ze_target'
y_train = train_data[target_name]
X_train = train_data.drop(target_name, axis=1)
y_val = train_data[target_name]
X_val = train_data.drop(target_name, axis=1)
def create_model():
model = Sequential()
model.add(Dense(20, input_dim=X.shape[1], activation='relu'))
model.add(Dense(20, input_dim=X.shape[1], activation='relu'))
model.add(Dense(20, input_dim=X.shape[1], activation='relu'))
model.add(Dense(1))
# Compile model
model.compile(optimizer=Adam(learning_rate=0.0001), loss = 'mse')
return model
model = create_model()
model.summary()
model.fit(X_train, y_train, validation_data=(X_val,y_val), batch_size=128, epochs=30)
```

## Solution

You can use `tf.concat`

and concatenate three features on axis=1 then use `tf.keras.layers.Normalization`

for three feature like below, because we want to normalize on three features, make sure to set `input_shape=(3,)`

and `axis=-1`

.

```
import tensorflow as tf
x = tf.random.uniform((100, 1))
y = tf.random.uniform((100, 1))
z = tf.random.uniform((100, 1))
xyz = tf.concat([x, y, z], 1)
horsepower_normalizer = tf.keras.layers.Normalization(input_shape=(3,), axis=-1)
horsepower_normalizer.adapt(xyz)
horsepower_model = tf.keras.models.Sequential([
horsepower_normalizer,
tf.keras.layers.Dense(units=1)
])
horsepower_model(xyz)
```

Output:

```
<tf.Tensor: shape=(100, 1), dtype=float32, numpy=
array([[-0.17135675],
[-0.48248804],
[-2.2847023 ],
[-0.05702276],
[ 2.9332483 ],
[ 0.64826846],
[-2.1490448 ],
[-1.1697797 ],
[-0.01030668],
...
[-1.880199 ],
[ 1.2854142 ],
[-0.5471661 ]], dtype=float32)>
```

Answered By – I'mahdi

**This Answer collected from stackoverflow, is licensed under cc by-sa 2.5 , cc by-sa 3.0 and cc by-sa 4.0 **