# 7shi n[Python] MNISTを認識するNNの画像化

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131``` ```import pickle import numpy as np from PIL import Image def sigmoid(x): return 1 / (1 + np.exp(-x)) def softmax(x): e = np.exp(x - np.max(x)) return e / np.sum(e) def predict(x): a1 = np.dot(x, W1) + b1 z1 = sigmoid(a1) a2 = np.dot(z1, W2) + b2 z2 = sigmoid(a2) a3 = np.dot(z2, W3) + b3 z3 = softmax(a3) return z1, z2, a3, z3 def toimg(img, imin, imax): img = (img - imin) * 255 / (imax - imin) return np.uint8(img.reshape(28, 28)) def amax(imgs): return max([max(abs(x)) for x in imgs]) def concat(imgs, w, h): ws, hs = [0] * w, [0] * h for y in range(h): for x in range(w): img = imgs[x + y * w] ws[x] = max(ws[x], img.shape[1]) hs[y] = max(hs[y], img.shape[0]) ret = Image.new("RGB", (sum(ws) + w - 1, sum(hs) + h - 1), "white") px, py = 0, 0 for y in range(h): px = 0 for x in range(w): img = imgs[x + y * w] dx = int((ws[x] - img.shape[1]) / 2) dy = int((hs[y] - img.shape[0]) / 2) ret.paste(Image.fromarray(img), (px + dx, py + dy)) px += ws[x] + 1 py += hs[y] + 1 return ret def hist(img): vs = [0] * 28 for p in img.reshape(img.size): vs[int(p * 28 / 256)] += 1 h = np.zeros((28, 28)) for x in range(28): v = 28 - (vs[x] + 27) / 28 for y in range(28): if y < v: h[y, x] = 1 return toimg(h, 0, 1) def toimgs(imgs, w, h): am = amax(imgs) return concat([toimg(img, -am, am) for img in imgs], w, h) with open("sample_weight.pkl", "rb") as f: network = pickle.load(f) with open("mnist.pkl", "rb") as f: mnist = pickle.load(f) # (784, 50), (50, 100), (100, 10) W1, W2, W3, b1, b2, b3 = ( network["W1"], network["W2"], network["W3"], network["b1"], network["b2"], network["b3"]) # (10000,), (10000, 784) test_label, test_img = ( mnist["test_label"], mnist["test_img"]) sp = np.uint8([[255] * 4] * 28) pt = np.uint8([[255] * 4] * 20 + [[0,0,0,0]] * 4 + [[255] * 4] * 4) num, nimg = [], [] i, j = 0, 0 while i < 10: if i == test_label[j]: img = test_img[j] / 255 num += [img] nimg += [toimg(img, 1, 0)] i += 1 j += 1 def chimg(ch): if "0" <= ch <= "9": return nimg[int(ch)] if ch == ".": return pt return sp z1_0, z2_0, a3_0, z3_0 = predict(np.zeros(784)) def diff(x): l = x.size z1s = np.zeros((z1_0.size, l)) z2s = np.zeros((z2_0.size, l)) a3s = np.zeros((a3_0.size, l)) z3s = np.zeros((z3_0.size, l)) for i in range(l): img = np.zeros(l) img[i] = x[i] z1, z2, a3, z3 = predict(img) for j in range(z1.size): z1s[j, i] = z1[j] - z1_0[j] for j in range(z2.size): z2s[j, i] = z2[j] - z2_0[j] for j in range(a3.size): a3s[j, i] = a3[j] - a3_0[j] for j in range(z3.size): z3s[j, i] = z3[j] - z3_0[j] return z1s, z2s, a3s, z3s def diffimg(n, x): z1s, z2s, a3s, z3s = diff(x) z1 , z2 , a3 , z3 = predict(x) imgs = [ toimg(x, 0, 1), sp, np.asarray(toimgs(z1s, 5, 10)), sp, np.asarray(toimgs(z2s, 10, 10)), sp, np.asarray(toimgs(a3s, 1, 10)), np.asarray(concat([nimg[i] for i in range(10)], 1, 10)), np.asarray(toimgs(z3s, 1, 10)), np.asarray(concat([chimg(ch) for v in z3 for ch in "%0.5f" % v], 7, z3.size))] concat(imgs, len(imgs), 1).save(n + ".png") diffimg("white", np.array([1.0] * 784)) for i in range(10): diffimg(str(i), num[i]) ```