Convolution Neural Network

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