Now that we've defined the input and output, we can take a look at the code for the network.
from keras.layers import Input, Densefrom keras.models import Modeldef build_network(input_features=None): inputs = Input(shape=(input_features,), name="input") x = Dense(32, activation='relu', name="hidden")(inputs) prediction = Dense(1, activation='linear', name="final")(x) model = Model(inputs=inputs, outputs=prediction) model.compile(optimizer='adam', loss='mean_absolute_error') return model
That's all there is to it! We can then use this code to build a neural network instance suitable for our problem simply by calling it, as follows:
model = build_network(input_features=10)
Before we get to that, however, let's review ...
