It's now time to build the core of our classification application, which is the computational graph of this CNN architecture, but to maximize the benefits of this implementation, we aren't going to use the TensorFlow layers API. Instead, we are going to use the TensorFlow neural network version of it.
So, let's start off by defining the model input placeholders which will input the images, target classes, and the keep probability parameter of the dropout layer (this helps us to reduce the complexity of the architecture by dropping some connections and hence reducing the chances of overfitting):
# Defining the model inputsdef images_input(img_shape): return tf.placeholder(tf.float32, (None, ) + img_shape, name="input_images") ...
