質問編集履歴
2
test
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全体のコードは以下
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```python
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from google.colab import drive
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drive.mount('/content/drive')
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import sys
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import numpy as np
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import matplotlib.pyplot as plt
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sys.path.append('/content/drive/My Drive')
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import ActivationFunction as AF
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from PIL import Image
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from IPython.display import display
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img = Image.open("drive/My Drive/mnist_dataset/rei.jpeg")
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img = img.resize((100, 100))
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img = np.asarray(img)
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size = 5
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v_split = img.shape[0] // size
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h_split = img.shape[1] // size
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out_img = []
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[out_img.extend(np.hsplit(h_img, h_split))
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for h_img in np.vsplit(img, v_split)]
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plt.subplot(161).imshow(out_img[0])
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plt.subplot(162).imshow(out_img[1])
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plt.subplot(163).imshow(out_img[2])
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plt.subplot(164).imshow(out_img[3])
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plt.subplot(165).imshow(out_img[4])
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plt.subplot(166).imshow(out_img[5])
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plt.subplot(167).imshow(out_img[6])
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plt.subplot(168).imshow(out_img[7])
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plt.subplot(169).imshow(out_img[8])
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# PILで開いたうえでデータをNumpy形式にする
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# （例えばJPEGは圧縮されていてNumpyな配列になっていないので、
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# そこからNumpyのデータ空間(?)に持ってくる必要がある）
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tefilename = "test2.png"
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teimg = Image.open("drive/My Drive/mnist_dataset/" + tefilename)
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teimg = teimg.resize((10, 10))
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teimg = np.asarray(teimg)
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def extract(x, y):
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# カラー画像の時Gだけ抜き取りたい
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if len(x.shape) == 3:
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h, w, ch = x.shape
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# RGBのGだけ抜き取りたい
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return x[:,:,y]
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v_max, v_min = 300, 200
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def diff(x):
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imgrows, lenrows, imgcolumns, lencolumns = [], [], [], []
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for (img, imgt) in zip(x, x.T):
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rows = img[(v_min<img)&(v_max>img)]
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columns = imgt[(v_min<imgt)&(v_max>imgt)]
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imgrows.append(rows)
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lenrows.append(len(rows))
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imgcolumns.append(columns)
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lencolumns.append(len(columns))
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return lenrows + lencolumns
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test_data_list = []
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test_data_list.append([0] + diff(extract(teimg, 1)) + diff(extract(teimg, 2)) + diff(extract(teimg, 0))) # 略
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out_data_list0 = []
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out_data_list0.append([0] + diff(extract(out_img[0], 1)) + diff(extract(out_img[0], 2)) + diff(extract(out_img[0], 0)))
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out_data_list1 = []
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out_data_list1.append([0] + diff(extract(out_img[1], 1)) + diff(extract(out_img[1], 2)) + diff(extract(out_img[1], 0)))
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# 見本データに対しても同様に
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# exについて同様に
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training_data_list = []
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for i in range(10):
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for e in range(1):
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trad = Image.open("drive/My Drive/mnist_dataset/" + str(10*i+e) + ".png")
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trad = trad.resize((10, 10))
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trad = np.asarray(trad)
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#g #b #r 抽出後diffしてappend
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training_data_list.append([i] + diff(extract(trad, 1)) + diff(extract(trad, 2)) + diff(extract(trad, 0))) # 略
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print("training_data_list" ,training_data_list)
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print("training_data_list[1:]" ,training_data_list[1:])
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# 3層ニューラルネットワーク
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class ThreeLayerNetwork:
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# コンストラクタ
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def __init__(self, inodes, hnodes, onodes, lr):
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# 各レイヤーのノード数
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self.inodes = inodes
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self.hnodes = hnodes
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self.onodes = onodes
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# 学習率
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self.lr = lr
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# 重みの初期化
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self.w_ih = np.random.normal(0.0, 1.0, (self.hnodes, self.inodes))
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self.w_ho = np.random.normal(0.0, 1.0, (self.onodes, self.hnodes))
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# 活性化関数
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self.af = AF.sigmoid
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self.daf = AF.derivative_sigmoid
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# 誤差逆伝搬
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def backprop(self, idata, tdata):
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# 縦ベクトルに変換
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o_i = np.array(idata, ndmin=2).T
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t = np.array(tdata, ndmin=2).T
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# 隠れ層
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np.set_printoptions(threshold=10000)
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x_h = np.dot(self.w_ih, o_i)
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o_h = self.af(x_h)
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# 出力層
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x_o = np.dot(self.w_ho, o_h)
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o_o = self.af(x_o)
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# 誤差計算
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e_o = (t - o_o)
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e_h = np.dot(self.w_ho.T, e_o)
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# 重みの更新
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self.w_ho += self.lr * np.dot((e_o * self.daf(o_o)), o_h.T)
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self.w_ih += self.lr * np.dot((e_h * self.daf(o_h)), o_i.T)
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# 順伝搬
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def feedforward(self, idata):
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# 入力のリストを縦ベクトルに変換
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o_i = np.array(idata, ndmin=2).T
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# 隠れ層
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x_h = np.dot(self.w_ih, o_i)
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o_h = self.af(x_h)
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# 出力層
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x_o = np.dot(self.w_ho, o_h)
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o_o = self.af(x_o)
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return o_o
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if __name__=='__main__':
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# パラメータ
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#inodes=784から30に変更
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inodes = 31
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hnodes = 100
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onodes = 10
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lr = 0.3
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# ニューラルネットワークの初期化
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nn = ThreeLayerNetwork(inodes, hnodes, onodes, lr)
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# 学習
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epoch = 50
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# 50000
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for e in range(epoch):
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print('#epoch ', e)
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data_size = len(training_data_list)
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for i in range(data_size):
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if i % 1000 == 0:
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print(' train: {0:>5d} / {1:>5d}'.format(i, data_size))
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idata = (np.array(out_data_list1) / 255.0 * 0.99) + 0.01
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# 変更の余地あり
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tdata = np.zeros(onodes) + 0.01
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tdata[out_data_list1[0]] = 0.99
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nn.backprop(idata, tdata)
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pass
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pass
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# テスト
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scoreboard = []
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for record in test_data_list:
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idata = (np.array(out_data_list0) / 255.0 * 0.99) + 0.01
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predict = nn.feedforward(idata)
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plabel = np.argmax(predict)
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print("predict" ,predict)
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print("plabel" ,plabel)
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pass
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|
+
|
|
449
|
+
scoreboard_array = np.asarray(scoreboard)
|
|
450
|
+
|
|
451
|
+
print('performance: ', scoreboard_array.sum() / scoreboard_array.size)
|
|
452
|
+
|
|
453
|
+
```
|
1
引用元を書きました
test
CHANGED
|
File without changes
|
test
CHANGED
|
@@ -57,3 +57,11 @@
|
|
|
57
57
|
ならリスト変数に数が格納されるのではと思ったがそうでもない...？
|
|
58
58
|
|
|
59
59
|
また、複雑すぎて何をやっているのか分からない。。
|
|
60
|
+
|
|
61
|
+
|
|
62
|
+
|
|
63
|
+
このへんを引用しました
|
|
64
|
+
|
|
65
|
+
https://teratail.com/questions/338466
|
|
66
|
+
|
|
67
|
+
https://teratail.com/questions/338168
|