Our final layer will consist of a single neuron that, using the 32 inputs from the hidden layer, will predict a single output value 
for each observation.
Putting all the layers together, our MLP network structure will look like this:
- Title Page
- Copyright and Credits
- Dedication
- Packt Upsell
- Foreword
- Contributors
- Preface
- The Building Blocks of Deep Learning
- Using Deep Learning to Solve Regression Problems
- Monitoring Network Training Using TensorBoard
- Using Deep Learning to Solve Binary Classification Problems
- Using Keras to Solve Multiclass Classification Problems
- Hyperparameter Optimization
- Training a CNN from Scratch
- Transfer Learning with Pretrained CNNs
- Training an RNN from scratch
- Training LSTMs with Word Embeddings from Scratch
- Training Seq2Seq Models
- Using Deep Reinforcement Learning
- Generative Adversarial Networks
- Other Books You May Enjoy
Output layer shape
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