Implementing a deep convolutional neural network using TensorFlow
In Chapter 13, Parallelizing Neural Network Training with TensorFlow, you may recall that we implemented a multilayer neural network for handwritten digit recognition problems, using different API levels of TensorFlow. You may also recall that we achieved about 97 percent accuracy.
So now, we want to implement a CNN to solve this same problem and see its predictive power in classifying handwritten digits. Note that the fully connected layers that we saw in the Chapter 13, Parallelizing Neural Network Training with 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 in the following figure. The input is 28 x 28 grayscale images...
