First commit

.gitignore

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+*ubyte.gz
+image.npy
+image.png

convert.py

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+# MIT License
+
+# Copyright (c) 2017 Hyeonseok Jung
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+
+
+# ALL RIGHTS TO ORIGINAL OWNER 
+# https://github.com/hsjeong5/MNIST-for-Numpy
+
+# Modified by Steven Furgurson
+# https://github.com/theDweeb
+import mnist
+import numpy as np
+import imageio
+import os.path
+from PIL import Image
+
+# Check if MNIST dataset has been downloaded
+isDownloaded = (
+    os.path.exists("train-images-idx3-ubyte.gz") and
+    os.path.exists("train-images-idx3-ubyte.gz") and
+    os.path.exists("train-images-idx3-ubyte.gz") and
+    os.path.exists("train-images-idx3-ubyte.gz")
+)
+
+if(not isDownloaded):
+    print("Downloading files")
+    mnist.init()
+
+print("Files downloaded")
+
+# Hold MNIST dataset
+# x_train: Training images
+# t_train: Training labels
+# x_test: Testing images
+# t_test = Testing labels
+x_train, t_train, x_test, t_test = mnist.load()
+
+# Pull a image to test for MLP
+# Just adjust the left index
+image = x_test[0,:]
+image.resize((28,28))
+
+# For my MLP I used np.int32 for image.
+# Make sure the data type matches for the vector<dType>
+npimage = np.asarray(image, dtype=np.int32)
+
+np.save("image", npimage)
+
+# Shows image for labeling
+img = Image.fromarray(image)
+img.save('image.png')
+img.show()