Basic start. Implemented skeleton of loss functions.

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+"""
+Homework 5 - CNNs
+CS1430 - Computer Vision
+Brown University
+"""
+
+"""
+Number of epochs. If you experiment with more complex networks you
+might need to increase this. Likewise if you add regularization that
+slows training.
+"""
+num_epochs = 50
+
+"""
+A critical parameter that can dramatically affect whether training
+succeeds or fails. The value for this depends significantly on which
+optimizer is used. Refer to the default learning rate parameter
+"""
+learning_rate = 1e-4
+
+"""
+Momentum on the gradient (if you use a momentum-based optimizer)
+"""
+momentum = 0.01
+
+"""
+Resize image size for task 1. Task 3 must have an image size of 224,
+so that is hard-coded elsewhere.
+"""
+img_size = 224
+
+"""
+Sample size for calculating the mean and standard deviation of the
+training data. This many images will be randomly seleted to be read
+into memory temporarily.
+"""
+preprocess_sample_size = 400
+
+"""
+Maximum number of weight files to save to checkpoint directory. If
+set to a number <= 0, then all weight files of every epoch will be
+saved. Otherwise, only the weights with highest accuracy will be saved.
+"""
+max_num_weights = 5
+
+"""
+Defines the number of training examples per batch.
+You don't need to modify this.
+"""
+batch_size = 10
+
+"""
+The number of image scene classes. Don't change this.
+"""
+num_classes = 15