Model Output
Complete a multilayer perceptron model in Keras.
We'll cover the following
Chapter Goals:
- Add the final layers to the MLP for multiclass classification
A. Final layer activation
In the Intro to Deep Learning section, we built the MLP classification models such that each model produced logits. This is because the TensorFlow cross-entropy loss functions applied the sigmoid/softmax function to the output of the MLP.
In Keras, the cross-entropy loss functions only calculate cross-entropy, without applying the sigmoid/softmax function to the MLP output. Therefore, we can have the model directly output class probabilities instead of logits (i.e. we apply sigmoid/softmax activation to the output layer).
model = Sequential()layer1 = Dense(5, activation='relu', input_dim=4)model.add(layer1)layer2 = Dense(1, activation='sigmoid')model.add(layer2)
model = Sequential()layer1 = Dense(5, input_dim=4)model.add(layer1)layer2 = Dense(3, activation='softmax')model.add(layer2)
Time to code!
The coding exercise will complete the Keras Sequential model that was set up in the previous chapter. Note that the output size of the model will be 3 (there are 3 possible classes for each data observation).
Set layer2 equal to a Dense with 5 as the required argument and ‘relu’ for the activation keyword argument. Then call model.add on layer2.
Set layer3 equal to a Dense with 3 as the required argument and ‘softmax’ for the activation keyword argument. Then call model.add on layer3.
model = Sequential()layer1 = Dense(5, activation='relu', input_dim=2)model.add(layer1)# CODE HERE
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