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Browse files- app.py +3 -24
- data/train_test_split_data.npz +0 -0
app.py
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@@ -5,30 +5,9 @@ import tensorflow
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import math
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from tensorflow.python.framework.ops import disable_eager_execution
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#
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disable_eager_execution()
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# # Load the training and testing data
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# load_data = numpy.load('data/train_test_split_data.npz') # Data saved by the VAE
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#
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# # Convert Data to Tuples and Assign to respective variables
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# box_matrix_train, box_density_train, additional_pixels_train, box_shape_train = tuple(
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# load_data['box_matrix_train']), tuple(load_data['box_density_train']), tuple(
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# load_data['additional_pixels_train']), tuple(load_data['box_shape_train'])
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# box_matrix_test, box_density_test, additional_pixels_test, box_shape_test = tuple(load_data['box_matrix_test']), tuple(
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# load_data['box_density_test']), tuple(load_data['additional_pixels_test']), tuple(load_data['box_shape_test'])
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# testX = box_matrix_test # Shows the relationship to the MNIST Dataset vs the Shape Dataset
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# image_size = numpy.shape(testX)[-1] # Determines the size of the images
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# test_data = numpy.reshape(testX, (len(testX), image_size, image_size, 1))
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# Creates tuples that contain all of the data generated
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# allX = numpy.append(box_matrix_train, box_matrix_test, axis=0)
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# all_box_density = numpy.append(box_density_train, box_density_test, axis=0)
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# all_additional_pixels = numpy.append(additional_pixels_train, additional_pixels_test, axis=0)
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# all_box_shape = numpy.append(box_shape_train, box_shape_test, axis=0)
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# all_data = numpy.reshape(allX, (len(allX), image_size, image_size, 1))
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def basic_box_array(image_size):
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A = numpy.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values
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# Creates the outside edges of the box
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@@ -287,8 +266,8 @@ def interpolate(t1, t2, d1, d2, th1, th2, steps):
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latent_matrix.append(new_column)
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latent_matrix = numpy.array(latent_matrix).T # Transposes the matrix so that each row can be easily indexed
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plot_rows = 2
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plot_columns = num_interp + 2
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predicted_interps = [number_1_expand[0, :, :, 0]]
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import math
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from tensorflow.python.framework.ops import disable_eager_execution
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# Because important
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disable_eager_execution()
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def basic_box_array(image_size):
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A = numpy.zeros((int(image_size), int(image_size))) # Initializes A matrix with 0 values
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# Creates the outside edges of the box
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latent_matrix.append(new_column)
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latent_matrix = numpy.array(latent_matrix).T # Transposes the matrix so that each row can be easily indexed
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# plot_rows = 2
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# plot_columns = num_interp + 2
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predicted_interps = [number_1_expand[0, :, :, 0]]
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data/train_test_split_data.npz
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Binary file (32.1 kB)
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