Finished convolution chapter

other scripts/convolutioner.py

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+import torch
+import PIL
+import numpy
+from torchvision import transforms, datasets
+import matplotlib.pyplot as plt
+
+
+def frobdot(mat1, mat2):
+    return mat1[0][0] * mat2[0][0] + mat1[0][1] * mat2[0][1] + mat1[0][2] * mat2[0][2] + mat1[1][0] * mat2[1][0] + \
+           mat1[1][1] * mat2[1][1] + mat1[1][2] * mat2[1][2] + mat1[2][0] * mat2[2][0] + mat1[2][1] * mat2[2][1] + \
+           mat1[2][2] * mat2[2][2]
+
+
+data = datasets.MNIST('../datasets', train=True, download=True,
+                      transform=transforms.Compose([
+                          transforms.ToTensor()
+                      ]))
+
+loader = torch.utils.data.DataLoader(data, batch_size=1, shuffle=False)
+examples = enumerate(loader)
+batch_idx, (example_data, example_targets) = next(examples)
+
+raw_img = example_data[0][0]
+
+new_img = numpy.zeros((28, 28))
+
+kernel = numpy.array([[1, 20, 1],
+                      [20, 0, 20],
+                      [1, 20, 1]])
+print(new_img.shape)
+for y, line in enumerate(raw_img):
+    for x, value in enumerate(line):
+        tempmat = numpy.zeros((3, 3))
+        possible_positions = [[y + 1, x - 1], [y + 1, x], [y + 1, x + 1], [y, x - 1], [y, x], [y, x + 1],
+                              [y - 1, x - y], [y - 1, x], [y - 1, x - 1]]
+        n = -1
+        for i, pos in enumerate(possible_positions):
+            if i % 3 is 0:
+                n += 1
+
+            if (pos[0] < 0) or (pos[0] > 27) or (pos[1] < 0) or (pos[1] > 27):
+                tempmat[n][i % 3] = 0
+            else:
+                tempmat[n][i % 3] = raw_img[pos[0]][pos[1]]
+        new_img[y][x] = frobdot(tempmat, kernel)
+
+plt.xticks([])
+plt.yticks([])
+plt.imshow(new_img, cmap='gray')
+plt.show()