Wooooo smaller repo!

app.py

@@ -5,30 +5,9 @@ import tensorflow
 import math
 from tensorflow.python.framework.ops import disable_eager_execution
 
-# Becuase important
+# Because important
 disable_eager_execution()
 
-# # Load the training and testing data
-# load_data = numpy.load('data/train_test_split_data.npz')  # Data saved by the VAE
-#
-# # Convert Data to Tuples and Assign to respective variables
-# box_matrix_train, box_density_train, additional_pixels_train, box_shape_train = tuple(
-#     load_data['box_matrix_train']), tuple(load_data['box_density_train']), tuple(
-#     load_data['additional_pixels_train']), tuple(load_data['box_shape_train'])
-# box_matrix_test, box_density_test, additional_pixels_test, box_shape_test = tuple(load_data['box_matrix_test']), tuple(
-#     load_data['box_density_test']), tuple(load_data['additional_pixels_test']), tuple(load_data['box_shape_test'])
-# testX = box_matrix_test  # Shows the relationship to the MNIST Dataset vs the Shape Dataset
-# image_size = numpy.shape(testX)[-1]  # Determines the size of the images
-# test_data = numpy.reshape(testX, (len(testX), image_size, image_size, 1))
-
-# Creates tuples that contain all of the data generated
-# allX = numpy.append(box_matrix_train, box_matrix_test, axis=0)
-# all_box_density = numpy.append(box_density_train, box_density_test, axis=0)
-# all_additional_pixels = numpy.append(additional_pixels_train, additional_pixels_test, axis=0)
-# all_box_shape = numpy.append(box_shape_train, box_shape_test, axis=0)
-# all_data = numpy.reshape(allX, (len(allX), image_size, image_size, 1))
-
-
 def basic_box_array(image_size):
     A = numpy.zeros((int(image_size), int(image_size)))  # Initializes A matrix with 0 values
     # Creates the outside edges of the box
@@ -287,8 +266,8 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
         latent_matrix.append(new_column)
     latent_matrix = numpy.array(latent_matrix).T  # Transposes the matrix so that each row can be easily indexed
 
-    plot_rows = 2
-    plot_columns = num_interp + 2
+    # plot_rows = 2
+    # plot_columns = num_interp + 2
 
     predicted_interps = [number_1_expand[0, :, :, 0]]