The next step is to utilize the model for training, and record the learned model parameters, which we will accomplish in train.py.
Let's start by importing the dependencies:
import tensorflow as tfimport hy_param# MLP Model which we defined in previous stepimport model
Then, we define the variables that we require to be fed into our MLP:
# This will feed the raw imagesX = model.X# This will feed the labels associated with the imageY = model.Y
Let's create the folder to which we will save our checkpoints. Checkpoints are basically the intermediate steps that capture the values of W and b in the process of learning. Then, we will use the tf.train.Saver() function (more details on this function can be found at https://www.tensorflow.org/api_docs/python/tf/train/Saver ...
