In this chapter, we will cover the following topics:
- Downloading and configuring an image dataset
- Learning the architecture of a CNN classifier
- Using functions to initialize weights and biases
- Using functions to create a new convolution layer
- Using functions to flatten the densely connected layer
- Defining placeholder variables
- Creating the first convolution layer
- Creating the second convolution layer
- Flattening the second convolution layer
- Creating the first fully connected layer
- Applying dropout to the first fully connected layer
- Creating the second fully connected layer with dropout
- Applying softmax activation to obtain a predicted class
- Defining the cost function used for optimization
- Performing gradient descent cost optimization
- Executing the graph in a TensorFlow session
- Evaluating the performance on test data
