completed "Layer Types" section

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]
+
+
+def convolve(raw_img, kernel):
+    new_img = numpy.zeros((28, 28))
+    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)
+
+    return new_img
+
+
+def maxpool(raw_image):
+    new_img = numpy.zeros((14, 14))
+    new_y = -1
+    for y in range(0, 28, 2):
+        new_x = -1
+        new_y += 1
+        for x in range(0, 28, 2):
+            new_x += 1
+            new_img[new_y][new_x] = max(
+                [raw_image[y][x], raw_image[y][x + 1], raw_image[y + 1][x], raw_image[y + 1][x + 1]])
+    return new_img
+
+
+def avgpool(raw_image):
+    new_img = numpy.zeros((14, 14))
+    new_y = -1
+    for y in range(0, 28, 2):
+        new_x = -1
+        new_y += 1
+        for x in range(0, 28, 2):
+            new_x += 1
+            new_img[new_y][new_x] = sum(
+                [raw_image[y][x], raw_image[y][x + 1], raw_image[y + 1][x], raw_image[y + 1][x + 1]]) / 4
+    return new_img
+
+
+kernel = numpy.array([[-1, -1, -1],
+                      [1, 1, 1],
+                      [0, 0, 0]])
+
+plt.xticks([])
+plt.yticks([])
+plt.imshow(avgpool(convolve(raw_img, kernel)), cmap='gray')
+plt.show()