Implementing a deep CNN using TensorFlow

In Chapter 14, Going Deeper – The Mechanics of TensorFlow, you may recall that we used TensorFlow Estimators for handwritten digit recognition problems, using different API levels of TensorFlow. You may also recall that we achieved about 89 percent accuracy using the DNNClassifier Estimator with two hidden layers.

Now, let's implement a CNN and see whether it can achieve a better predictive performance compared to the MLP (DNNClassifier) for classifying handwritten digits. Note that the fully connected layers that we saw in Chapter 14, Going Deeper – The Mechanics of TensorFlow, were able to perform well on this problem. However, in some applications, such as reading bank account numbers from handwritten digits, even tiny mistakes can be very costly. Therefore, it is crucial to reduce this error as much as possible.

The multilayer CNN architecture

The architecture of the network that we are going to implement is shown...